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Keywords = air-conditioning hood

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20 pages, 5763 KiB  
Article
Flow Field Modeling Analysis on Kitchen Environment with Air Conditioning Range Hood
by Xiaoying Huang, Zhihang Shen, Shunyu Zhang, Yongqiang Tan, Ang Li, Bingsong Yu, Yi Jiang, Liang Peng and Zhenlei Chen
Atmosphere 2025, 16(2), 236; https://doi.org/10.3390/atmos16020236 - 19 Feb 2025
Abstract
This study proposes a flow field modeling analysis of kitchen environments with air-conditioning range hoods. The substructure approach is applied to resolve the challenges of low computational efficiency and convergence difficulties associated with the simultaneous consideration of the range hood and the cooling [...] Read more.
This study proposes a flow field modeling analysis of kitchen environments with air-conditioning range hoods. The substructure approach is applied to resolve the challenges of low computational efficiency and convergence difficulties associated with the simultaneous consideration of the range hood and the cooling air-conditioning fan impeller rotation models. The presented approach effectively enhances computational efficiency while ensuring accuracy. A flow field analysis of the air-conditioning substructure was performed in Fluent to obtain the velocity contour plot at the air-conditioning outlet monitoring surface. The data were then mapped to the full kitchen hood model to enable a comprehensive flow field analysis of the kitchen setup. The results show that the proposed substructure-based method to analyze the flow field in kitchens with air-conditioning hoods is computationally efficient, achieving an alignment accuracy above 95% across four measurement points. These findings establish a strong foundation for future comfort assessments and the optimization of kitchens with air-conditioning hoods. Full article
(This article belongs to the Section Air Pollution Control)
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Figure 1
<p>Geometric model of the kitchen environment.</p>
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<p>Diagram of the three fan impellers in the air-conditioning hood.</p>
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<p>Mesh refinement in critical areas of the model.</p>
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<p>Measurement points in the kitchen model (#1–4 indicate test points 1–4).</p>
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<p>Field test setup in the test kitchen.</p>
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<p>Airflow velocity data at different test points.</p>
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<p>Velocity cloud map of the center cross-section in the kitchen model.</p>
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<p>Velocity cloud map of the air-conditioning outlet in the kitchen model.</p>
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<p>Air-conditioning substructure model (for clarity, the front impeller baffle has been hidden).</p>
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<p>Velocity cloud map at the air-conditioning outlet detection surface in the substructure model.</p>
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<p>(<b>a</b>) Velocity cloud map at the air-conditioning outlet detection surface in the full kitchen model under the uniform flow assumption. (<b>b</b>) Velocity cloud map at the air-conditioning outlet detection surface in the full kitchen model after mapping the substructure model data.</p>
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<p>Comparison of velocity cloud maps at the air-conditioning outlet with and without the substructure model.</p>
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13 pages, 3866 KiB  
Article
The Development and Optimization of a New Wind Tunnel Design for Odour Sampling
by Francesca Tagliaferri, Luca Carrera, Anna Albertini, Marzio Invernizzi and Selena Sironi
Atmosphere 2024, 15(10), 1181; https://doi.org/10.3390/atmos15101181 - 30 Sep 2024
Viewed by 808
Abstract
The characterization of passive area sources, emitting odours due to wind-driven convection, poses significant challenges. The present experimental study aims to evaluate the performance, in terms of fluid dynamics and mass transfer, of a recently developed wind tunnel, with a more compact design [...] Read more.
The characterization of passive area sources, emitting odours due to wind-driven convection, poses significant challenges. The present experimental study aims to evaluate the performance, in terms of fluid dynamics and mass transfer, of a recently developed wind tunnel, with a more compact design and reduced weight, compared to the one proposed by the Italian regulations. The results show that the new design outperforms the Italian standard in several aspects. From a fluid dynamic point of view, the new wind tunnel exhibits a slightly more homogenous and uniform velocity distribution, and it does not reveal airflow preferential channels inside the central body. The pressure tests highlight that the presence of fillers in the new wind tunnel does not significantly alter the pressure inside the hood and therefore the gas–liquid equilibrium conditions; actually, the slight overpressure may help to prevent the infiltration of external air. Finally, mass transfer tests on the standard device show a vertical concentration gradient along the outlet duct, highlighting concentration values that differ up to a factor of two depending on the measurement point. The new design has almost completely solved this issue, thanks to the use of fillers that promote mixing of the outlet flow. Full article
(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)
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<p>Italian standard wind tunnel (<b>a</b>) and new proposed wind tunnel design (<b>b</b>).</p>
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<p>A scheme of the 27 sample points for the wind tunnel.</p>
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<p>Pressure measurement setup.</p>
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<p>Mass transfer measurement setups for pure water, Italian standard wind tunnel (<b>a</b>), and new wind tunnel design (<b>b</b>); MEK solution, Italian standard wind tunnel (<b>c</b>), and new wind tunnel design (<b>d</b>).</p>
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<p>Fluid dynamic results for the standard wind tunnel (<b>left</b>) and the new design (<b>right</b>).</p>
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<p>The velocity profile along the central body of the new proposed wind tunnel design.</p>
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<p>Mass transfer measurement results for pure water (RH = Relative Humidity), Italian standard wind tunnel (<b>a</b>), and new wind tunnel design (<b>b</b>); MEK (Methyl Ethyl-Ketone) solution, Italian standard wind tunnel (<b>c</b>), and new wind tunnel design (<b>d</b>).</p>
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<p>A scheme of the possible future design optimized for the new proposed design wind tunnel.</p>
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24 pages, 5979 KiB  
Article
The Performance of Reinforcement Learning for Indoor Climate Control Devices according to the Level of Outdoor Air Particulate Matters
by Sun Ho Kim and Hyeun Jun Moon
Buildings 2023, 13(12), 3062; https://doi.org/10.3390/buildings13123062 - 8 Dec 2023
Cited by 1 | Viewed by 1374
Abstract
As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain [...] Read more.
As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain an appropriate IEQ with changing outdoor air conditions and occupant behavior in a building. This study proposes a reinforcement learning (RL) algorithm to maintain indoor air quality (IAQ) with low energy consumption in a residential environment by optimally operating indoor climate control devices such as ventilation systems, air purifiers, and kitchen hoods. The proposed artificial intelligence algorithm (AI2C2) employs DDQN (double deep Q-network) to determine the optimal operation of various indoor climate control devices, reflecting IAQ and energy consumption via different outdoor levels of particulate matter. This approach considers the outdoor air condition and occupant activities in training the developed algorithm, which are the most significant factors affecting IEQ and building energy performance. A co-simulation platform using Python and EnergyPlus is applied to train and evaluate the model. As a result, the proposed approach reduced energy consumption and maintained good IAQ. The developed RL algorithm for energy and IAQ showed different performances based on the outdoor PM 2.5 level. The results showed the RL-based control can be more effective when the outdoor PM 2.5 level is higher (or unhealthy) compared to moderate (or healthy) conditions. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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<p>Concept of the proposed RL-based approach.</p>
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<p>Plan of the test chamber and indoor climate control devices.</p>
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<p>Time and duration of occupant activities in a residential building (updated from [<a href="#B34-buildings-13-03062" class="html-bibr">34</a>]).</p>
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<p>A schematic diagram of the testbed and sources of indoor contaminants for the numerical IAQ model.</p>
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<p>Co-simulation platform for AI2C2.</p>
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<p>Indoor PM 2.5 concentrations when all indoor climate control systems are off (Case 1).</p>
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<p>Indoor PM 2.5 concentrations when all indoor climate control systems are off (Case 2).</p>
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<p>Indoor CO<sub>2</sub> concentrations when all indoor climate control systems are off (all cases).</p>
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<p>Convergence of AI2C2 (left: Case 1, right: Case 2).</p>
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<p>IAQ and operation of the indoor climate control devices using RBC (Case 1).</p>
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<p>IAQ and operation of the indoor climate control devices using RBC (Case 2).</p>
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<p>IAQ and operation of the indoor climate control devices using AI2C2 (Case 1, episode 19,981).</p>
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<p>IAQ and operation of the indoor climate control devices using AI2C2 (Case 2).</p>
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19 pages, 5551 KiB  
Article
Multi-Zonal Analysis of Indoor Air Quality in a Higher Educational Building in the UK
by Atefeh Abbaspour, Ali Bahadori-Jahromi, Shiva Amirkhani, Alan Janbey, Paulina B. Godfrey, Hooman Tahayori and Jacek Piechowicz
Sustainability 2023, 15(16), 12118; https://doi.org/10.3390/su151612118 - 8 Aug 2023
Cited by 2 | Viewed by 1392
Abstract
This study focuses on the indoor air quality (IAQ) in a higher educational building, the London College in the UK. In this regard, indoor CO2 levels, as well as three contaminants with detrimental effects on human health: NO2, PM2.5 [...] Read more.
This study focuses on the indoor air quality (IAQ) in a higher educational building, the London College in the UK. In this regard, indoor CO2 levels, as well as three contaminants with detrimental effects on human health: NO2, PM2.5, and SARS-CoV-2, are investigated. Various IAQ enhancement strategies are analyzed, including increased ventilation, background ventilation, improved airflow through opened doors, and the use of HEPA air cleaners. Results revealed that background ventilation and open doors during occupied periods reduced CO2 concentrations to around 1000 ppm. However, the effectiveness of background ventilation was influenced by outdoor conditions, such as wind speed and direction. The most effective method for reducing PM2.5 levels was installing an air cleaner alongside a commercial kitchen hood, resulting in a 15% greater reduction compared to background ventilation. To control the SARS-CoV-2 level, combining background ventilation or opening the doors with a 16,000 m3/h ventilation rate or using an air cleaner with baseline ventilation resulted in a basic reproductive number below 1. Overall, the research highlights the importance of background ventilation and open doors in enclosed spaces without operable windows for natural airflow. Additionally, the effectiveness of air purifiers in reducing particle and biological contaminant concentrations is demonstrated, providing valuable insights for improving IAQ in educational buildings. Full article
(This article belongs to the Special Issue Building Carbon Emissions and Their Impact on the Climate Change)
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<p>The London College building, London.</p>
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<p>The sketchpad view of the London College in CONTAM.</p>
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<p>The sketchpad view of the London College in CONTAM.</p>
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<p>Outdoor level of NO<sub>2</sub> and PM<sub>2.5</sub> in 2022, provided by AQE [<a href="#B28-sustainability-15-12118" class="html-bibr">28</a>].</p>
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<p>Comparison of the measured and simulated CO<sub>2</sub> concentration in selected zones.</p>
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<p>Indoor NO<sub>2</sub> concentration in an office room under different scenarios.</p>
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<p>CO<sub>2</sub> level in the laboratory, classroom, and office under different AHU-related scenarios.</p>
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<p>Occupancy schedule of selected zones: laboratory, classroom, and office.</p>
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<p>Illustration of the background ventilation used in the model [<a href="#B38-sustainability-15-12118" class="html-bibr">38</a>].</p>
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<p>CO<sub>2</sub> level in the laboratory, classroom, and office with background ventilation and opening the doors.</p>
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<p>Airflow through an air brick and trickle vent in the office.</p>
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<p>Comparison of the average level of PM<sub>2.5</sub> with different mitigation strategies.</p>
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<p>Comparison of the basic reproductive number in the café.</p>
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20 pages, 11555 KiB  
Article
CFD Methodology for an Underhood Analysis towards the Optimum Fan Position in a Compact Off-Road Machine
by Cristian Ferrari, Nicolò Beccati and Francesca Pedrielli
Energies 2023, 16(11), 4369; https://doi.org/10.3390/en16114369 - 27 May 2023
Cited by 2 | Viewed by 2131
Abstract
A compact off-road machine tends to have a compact engine structure, which may result in small clearances between the main engine, the cooling system, and the radiator. In the design of its cooling system, the heat exchanger, fan, and conveyor are normally chosen [...] Read more.
A compact off-road machine tends to have a compact engine structure, which may result in small clearances between the main engine, the cooling system, and the radiator. In the design of its cooling system, the heat exchanger, fan, and conveyor are normally chosen based on their fixed operating point. Unfortunately, these machines work in variable conditions and the performance of each component is different when they are working as a whole under the hood. The aim of this work is to optimize the position of these components through a parametric analysis of some variables, using the Computational Fluid Dynamics technique. The air flows are analyzed in order to show the pressure waves created by the air moved by the fan blades, showing how the fluid interacts with the engine. The results show that optimizing this installation can increase the efficiency of the fan by 10% and reduce the noise emitted by 13 dB. These results should sensitize designers to use CFD analyses, not for a single component, but for the entire system. The methodology shown can be applied for the better design of cooling systems, mainly in off-road vehicles that have noise emission problems. Full article
(This article belongs to the Topic Computational Fluid Dynamics (CFD) and Its Applications)
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<p>Mini-Loader Vehicle (courtesy of MultiOne s.r.l.).</p>
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<p>Particular of the fan position (courtesy of MultiOne s.r.l.).</p>
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<p>Lateral and A-A section views of the fan cooling system. a is the engine-fan distance and b is the engine-radiator distance (courtesy of MultiOne s.r.l.).</p>
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<p>Engine vane modelling: (<b>A</b>) original 3D CAD; (<b>B</b>) surface simplification; (<b>C</b>) surface meshing; and (<b>D</b>) volume meshing.</p>
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<p>(<b>A</b>) Blade surface in the MRF domain; (<b>B</b>) surface of the shroud conveyor domain; and (<b>C</b>) surface of the heat exchanger domain.</p>
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<p>Computational domain of the virtual test with a zoom on the fan domain.</p>
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<p>Characteristics of cell vertex centered based code for mesh quality (based on [<a href="#B40-energies-16-04369" class="html-bibr">40</a>,<a href="#B41-energies-16-04369" class="html-bibr">41</a>]).</p>
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<p>Comparison between numerical and experimental fan curves.</p>
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<p>Underhood fluid domain. The arrows indicate the flow direction.</p>
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<p>Mesh-independent analysis for pressure rise variable.</p>
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<p>Test cases with different conveyor geometries. (<b>a</b>) Original (test case a). (<b>b</b>) Curved shape (test case g). (<b>c</b>) Divergent shape (test case h).</p>
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<p>Test cases with different blade angles. (<b>a</b>) 35° (test case a). (<b>b</b>) 30° (test case i).</p>
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<p>Case a.</p>
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<p>Case f.</p>
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<p>Case g.</p>
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<p>Case h.</p>
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<p>Case i.</p>
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<p>Case j.</p>
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<p>Flow velocity in a cross-section of the underhood.</p>
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17 pages, 4294 KiB  
Article
Design and Test of Stripping and Impurity Removal Device for Spring-Tooth Residual Plastic Film Collector
by Qiangji Peng, Kaikai Li, Xiaoyu Wang, Guohai Zhang and Jianming Kang
Agriculture 2023, 13(1), 42; https://doi.org/10.3390/agriculture13010042 - 23 Dec 2022
Cited by 7 | Viewed by 2135
Abstract
The residual agricultural plastic film in China is not easily recovered due to the thinness and poor mechanical properties of domestic films, and a large amount of plastic film remaining in farmland soil poses a great threat to soil quality and crop production. [...] Read more.
The residual agricultural plastic film in China is not easily recovered due to the thinness and poor mechanical properties of domestic films, and a large amount of plastic film remaining in farmland soil poses a great threat to soil quality and crop production. A spring-tooth residual plastic film collector (SRPFC) is widely used in domestic residual plastic film (RPF) recycling operations. However, there are two major problems in the current SRPFC: the low recovery rate of the residual film (RRRF) caused by the difficulty of film-stripping and the high impurity rate in the film (IRF). In this paper, a stripping and impurity removal device (SIRD) is designed to address the existing problems of SRPFC, which is mainly composed of film-stripping tooth plates (FTP), two wind-collecting hoods, and two centrifugal fans. The motion and force analysis of the RPF in the film-stripping process was carried out, and the arc FTP was determined to be used for film-stripping. The size parameters of the FTP were obtained by establishing the coordinate system to solve the differential equation. By comparing and analyzing the force of RPF in the airflow field of the test bench for suspension speed and the airflow field of the wind-collecting hood, the RPF equivalent particle was established. The discrete phase model (DPM) in Fluent software was used to simulate the movement of the RPF equivalent particle, and the calculated air volume range of the centrifugal fan was 5501.88~6829.92 m3/h. The effects of forward speed, rotating speed of film conveying chain harrow (FCCH), and rotating speed of the centrifugal fan on RRRF and IRF were studied by orthogonal rotary combination experiment. The test results showed that the best combination of machine operation parameters was when the forward speed was 5 km/h, the rotating speed of the FCCH was 235 r/min, and the rotating speed of the centrifugal fan was 1978 r/min. Under these conditions, the RRRF was 92.53%, and the IRF was 9.31%. Field experiments were carried out with the rounded parameters, and the average RRRF was 92.07%, and the average IRF was 9.56% under the parameter combination, indicating that the optimization scheme of the device was feasible. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Overall structure diagram of SRPFC. Note: 1—Linkage; 2—Collecting box; 3—SIRD; 4—Cover plate; 5—Crushing device; 6—FCCH; 7—Film lifting device; 8—Traveling system; 9—Transmission device; 10—Film cutting device; 11—Straw crushing and returning device; 12—Rack.</p>
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<p>Schematic diagram of the structure of the SIRD. Note: 1—Supporting beam; 2—Mounting plate of FTP; 3—Spring teeth; 4—FCCH; 5—Supporting plate; 6—Impurity conveying mechanism; 7—FTP; 8—Wind-collecting hood; 9—Centrifugal fan; 10—Film conveying pipe.</p>
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<p>Analysis of the movement trajectory of the spring teeth and the force analysis of the RPF in the film-stripping process. (<b>a</b>) Motion trajectory of spring teeth in the process of film-stripping; (<b>b</b>) stress analysis of RPF during the film-stripping process. Note: 1, spring teeth; 2, RPF; 3, FTP.</p>
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<p>Analysis of FTP line shape.</p>
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<p>Analysis of force and movement of RPF in the airflow field.</p>
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<p>Fluid domain model of the wind-collecting hood. Note: 1—External area of the wind-collecting hood; 2—Particle inlet; 3—Suction film port of the wind-collecting hood; 4—Outlet.</p>
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<p>Fluid Domain Meshing Model.</p>
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<p>Particle trajectory and airflow velocity distribution (outlet velocity is 14.5 m/s). (<b>a</b>) Particle trajectory; (<b>b</b>) speed contour; (<b>c</b>) velocity vector.</p>
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<p>Particle trajectory and airflow velocity distribution (outlet velocity is 18 m/s). (<b>a</b>) particle trajectory; (<b>b</b>) speed contour; (<b>c</b>) velocity vector.</p>
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<p>(<b>a</b>) Field performance test of SRPFC; (<b>b</b>) effect of RPF recovery.</p>
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<p>Influence of various factors on the RPF collector performance. (<b>a</b>) <span class="html-italic">X</span><sub>3</sub> = 1950 r/min; (<b>b</b>) <span class="html-italic">X</span><sub>1</sub> = 5 km/h; (<b>c</b>) <span class="html-italic">X</span><sub>1</sub> = 5 km/h.</p>
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23 pages, 8751 KiB  
Article
Simulation Study of the Capture and Purification Performance of Exhaust Fume Systems in Chinese Commercial Kitchens—Case Study in Tianjin
by Awen Zhang, Na Deng, Zhengwei Long, Ruisen Hao, Changyu Shen and Guoqing Cao
Appl. Sci. 2022, 12(17), 8896; https://doi.org/10.3390/app12178896 - 5 Sep 2022
Viewed by 2246
Abstract
A Chinese commercial kitchen fume exhaust (CCKEF) system mainly consists of a wall-mounted canopy hood, air duct and terminal electrostatic purifiers, the capture and purification performance of which should be guaranteed to obtain satisfactory indoor and outdoor air environment in engineering applications. However, [...] Read more.
A Chinese commercial kitchen fume exhaust (CCKEF) system mainly consists of a wall-mounted canopy hood, air duct and terminal electrostatic purifiers, the capture and purification performance of which should be guaranteed to obtain satisfactory indoor and outdoor air environment in engineering applications. However, few studies have focused on the operation performance of CCKEF systems. This study was aimed at providing a simulation method to investigate the operation performance of such systems. The simulation model of a representative CCKEF system was established using CFD software and validated with measured temperature, air velocity and purification efficiency with a deviation within 10%. The validated model was used to predict the indoor air environment and purification efficiency of the CCKEF system under different working conditions. The results showed that the temperature of transfer air from adjacent rooms had a greater impact on the thermal environment of the cooking area than the surface temperature of stoves. The exhaust air volume had a significant influence on both the indoor air environment and purification efficiency. CCKEF system was suggested to be operated at the optimum airflow according to the simultaneous coefficient of stoves as the energy consumption of the system can be saved by 3.75%. Full article
(This article belongs to the Section Applied Thermal Engineering)
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<p>The geometric model of the CCKEF system. (<b>a</b>) Stoves and personnel; (<b>b</b>) Electrostatic purifier; (<b>c</b>) CCKEF system.</p>
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<p>Mesh division of the CCKEF system.</p>
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<p>Grid independence test.</p>
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<p>Schematic diagram of heat and mass transfer process of different stoves. (<b>a</b>) Cauldron stove; (<b>b</b>) Frying stove or stockpot.</p>
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<p>Layout of measurement points.</p>
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<p>Field test pictures of typical parameters. (<b>a</b>) Exhaust gas temperature; (<b>b</b>) Stove surface temperature; (<b>c</b>) Exhaust air rate; (<b>d</b>) Cooking fume concentration.</p>
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<p>Validation of temperature field of CCKEF system. (<b>a</b>) Measurement points in kitchen (<b>b</b>) Measurement points in exhaust fume system.</p>
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<p>Validation of velocity field of CCKEF system. (<b>a</b>) Measurement points in kitchen; (<b>b</b>) Measurement points in exhaust fume system.</p>
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<p>Comparison of simulated, measured and designed values of system purification efficiency.</p>
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<p>Four flame states during cooking. (<b>a</b>) Changming Fire; (<b>b</b>) Small fire; (<b>c</b>) Medium fire; (<b>d</b>) Big fire.</p>
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<p>The air velocity, temperature, pollution concentration of cooking area (X = 1.3 m) and system purification efficiency under different stove surface temperature conditions (Q<sub>ex</sub> = 11,726 m<sup>3</sup>/h). (<b>a</b>) Velocity; (<b>b</b>) Temperature; (<b>c</b>) C<sub>6</sub>H<sub>6</sub> concentration; (<b>d</b>) Purification efficiency.</p>
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<p>The air velocity, temperature, pollution concentration of cooking area (X = 1.3 m) and system purification efficiency under different stove surface temperature conditions (Q<sub>ex</sub> = 11,726 m<sup>3</sup>/h). (<b>a</b>) Velocity; (<b>b</b>) Temperature; (<b>c</b>) C<sub>6</sub>H<sub>6</sub> concentration; (<b>d</b>) Purification efficiency.</p>
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<p>The indoor air environment of cooking area and system purification efficiency under different simultaneity coefficient of stoves (Q<sub>ex</sub> = 11,726 m<sup>3</sup>/h). (<b>a</b>) Air temperature; (<b>b</b>) Air velocity; (<b>c</b>) C6H6 concentration; (<b>d</b>) Purification efficiency.</p>
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<p>The indoor air environment of cooking area and system purification efficiency under different natural replacement air temperatures (Q<sub>ex</sub> = 11,726 m<sup>3</sup>/h) (<b>a</b>) Air temperature; (<b>b</b>) Air velocity; (<b>c</b>) C6H6 concentration; (<b>d</b>) Purification efficiency.</p>
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<p>The indoor air environment of cooking area and system purification efficiency under different natural replacement air temperatures (Q<sub>ex</sub> = 11,726 m<sup>3</sup>/h) (<b>a</b>) Air temperature; (<b>b</b>) Air velocity; (<b>c</b>) C6H6 concentration; (<b>d</b>) Purification efficiency.</p>
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<p>Indoor temperature, air velocity, pollutant concentration and system purification efficiency under different exhaust air volume. (<b>a</b>) Air temperature; (<b>b</b>) Air velocity; (<b>c</b>) C6H6 concentration; (<b>d</b>) Purification efficiency.</p>
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<p>The control of exhaust air volume under three scenarios.</p>
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8 pages, 1127 KiB  
Technical Note
An Innovative Filtering System for the Handling of Asbestos-Based Products: Improvement of Safety and Quality of Work in Analysis Laboratories
by Oriana Motta, Concetta Pironti, Marta Venier and Antonio Proto
Toxics 2022, 10(6), 281; https://doi.org/10.3390/toxics10060281 - 25 May 2022
Cited by 2 | Viewed by 2392
Abstract
Although being banned or restricted in many countries since the early 1990s, large quantities of asbestos are still used or present in building materials all over the world and its removal or handling requires specific systems that limit exposure to airborne fibers The [...] Read more.
Although being banned or restricted in many countries since the early 1990s, large quantities of asbestos are still used or present in building materials all over the world and its removal or handling requires specific systems that limit exposure to airborne fibers The exposure to asbestos causes chronic diseases such as asbestosis and lung cancer with an incubation period of 20 to 50 years. Among the operators most exposed to contamination are those who handle and analyze the materials in laboratories. For this reason, our work focused on an innovative method for removing a filter unit from a laboratory extraction hood, in order to improve the safety conditions for the operators and the surrounding environment. The hood has a particular construction technology with a mechanism that allows the spraying of a special encapsulating liquid on the ULPA filters below the work-bench, which is capable of forming a film and blocking the fibers on the surface of the same filter. The fibers are irreversibly bounded and can no longer be released into the surrounding environment. The monitoring of activity highlighted the absence of asbestos fibers in the air after installation of the filter and workers feel safer performing their activities. The introduction of an innovative filtering system enhanced the safety of work activities involving asbestos exposure, moreover, the time spent on the hood’s maintenance and the risk perception of workers were improved. Full article
(This article belongs to the Special Issue Advances in Risk Assessment and Management)
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<p>Schematic and section side view of the laboratory extraction hood comprising the filtering and safety removal kit.</p>
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<p>Picture of (<b>a</b>) filter before spraying the encapsulating agent; (<b>b</b>) colored film capable of blocking the fibers; (<b>c</b>) lifting jacks and closed sarcophagus for the safe transportation.</p>
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10 pages, 1957 KiB  
Article
Changing Patterns of the Flow Ratio with the Distance of Exhaust and Supply Hood in a Parallel Square Push-Pull Ventilation
by Jianwu Chen
Int. J. Environ. Res. Public Health 2022, 19(5), 2957; https://doi.org/10.3390/ijerph19052957 - 3 Mar 2022
Cited by 4 | Viewed by 1973
Abstract
The method of flow ratio k is often used for designing parallel push-pull ventilation. The k value is mostly selected empirically and is difficult to determine accurately, resulting in an imprecise design of the push-pull ventilation system. Therefore, parallel push-pull ventilation was taken [...] Read more.
The method of flow ratio k is often used for designing parallel push-pull ventilation. The k value is mostly selected empirically and is difficult to determine accurately, resulting in an imprecise design of the push-pull ventilation system. Therefore, parallel push-pull ventilation was taken as the research object in this paper. The push-pull ventilation studied consists of a square uniform supply hood and a square uniform exhaust hood, and the side length of pull hood and pull hood was same. A workbench was set between the push hood and pull hood, and the source of toluene pollutions was set in the center of the worktable surface. The optimal k values for different distances between push hood and pull hood were studied by numerical simulation using Ansys Fluent, which were obtained base on the distribution of wind speed and toluene concentration. The results showed that parallel push-pull ventilation is not suitable for applications when L/a ≥ 6. The changing patterns of k value with L/a is proposed in the range of 1.5 ≤ L/a ≤ 5 for the parallel square push-pull ventilation, which can be used to estimate k value relatively accurately under the condition that L/a is known, so as to guide the determination of the exhaust air volume of the parallel push-pull ventilation system. Full article
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<p>Geometric model of a push-pull ventilation system. <span class="html-italic">a</span>: side length of square hood, which is 0.7 m in this study, <span class="html-italic">L</span>: the distance between push hood and pull hood, <span class="html-italic">L<sub>off push hood</sub></span>: the distance from push hood, <span class="html-italic">L<sub>off pull hood</sub></span>: the distance from pull hood.</p>
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<p>Distribution of wind speed and toluene concentration for different <span class="html-italic">k</span> values at <span class="html-italic">L</span>/<span class="html-italic">a</span> = 1.5.</p>
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<p>Toluene concentration at respiratory zone locations for different <span class="html-italic">k</span> values at <span class="html-italic">L</span>/<span class="html-italic">a</span> = 1.5. The data lines of <span class="html-italic">k</span> = 1.5, 1.7, and 2.0 overlap.</p>
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<p>Distribution of wind speed and toluene concentration for different <span class="html-italic">k</span> values at <span class="html-italic">L</span>/<span class="html-italic">a</span> = 6.</p>
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<p>Toluene concentration at respiratory zone locations for different <span class="html-italic">k</span> values at <span class="html-italic">L</span>/<span class="html-italic">a</span> = 6.</p>
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<p>The variation patterns of <span class="html-italic">k</span> value with <span class="html-italic">L</span>/<span class="html-italic">a</span> when 1.5 ≤ <span class="html-italic">L</span>/<span class="html-italic">a</span> ≤ 5.</p>
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26 pages, 54777 KiB  
Article
Evaluating of IAQ-Index and TVOC Parameter-Based Sensors for Hazardous Gases Detection and Alarming Systems
by Mohammed Faeik Ruzaij Al-Okby, Sebastian Neubert, Thomas Roddelkopf, Heidi Fleischer and Kerstin Thurow
Sensors 2022, 22(4), 1473; https://doi.org/10.3390/s22041473 - 14 Feb 2022
Cited by 18 | Viewed by 5535
Abstract
The measurement of air quality parameters for indoor environments is of increasing importance to provide sufficient safety conditions for workers, especially in places including dangerous chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality index (IAQ-index) and total volatile [...] Read more.
The measurement of air quality parameters for indoor environments is of increasing importance to provide sufficient safety conditions for workers, especially in places including dangerous chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality index (IAQ-index) and total volatile organic Compounds (TVOC) are two important parameters to measure air impurities or air pollution. Both parameters are widely used in gases sensing applications. In this paper, the IAQ-index and TVOCs have been investigated to identify the best and most flexible solution for air quality threshold selection of hazardous/toxic gases detection and alarming systems. The TVOCs from the SGP30 gas sensor and the IAQ-index from the SGP40 gas sensor were tested with 12 different organic solvents. The two gas sensors are combined with an IoT-based microcontroller for data acquisition and data transfer to an IoT-cloud for further processing, storing, and monitoring purposes. Extensive tests of both sensors were carried out to determine the minimum detectable volume depending on the distance between the sensor node and the leakage source. The test scenarios included static tests in a classical chemical hood, as well as tests with a mobile robot in an automated sample preparation laboratory with different positions. Full article
(This article belongs to the Special Issue Use Wireless Sensor Networks for Environmental Applications)
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<p>System structure.</p>
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<p>The used Adafruit SGP30 module board.</p>
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<p>The used Adafruit SGP40 module board.</p>
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<p>The used Adafruit SHTC3 module board.</p>
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<p>The used WeMos D1 Mini Development board.</p>
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<p>Flowchart of measurements process.</p>
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<p>Testing positions for VOC source; (<b>a</b>)-position 1 m below the sensor, (<b>b</b>)-position 1 m away from the sensor.</p>
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<p>The sensor node hosted by the H20 robot. (<b>a</b>,<b>b</b>) front and side views of the used H20 robot with the sensor node. (<b>c</b>) The robot position for the measurements process. (<b>d</b>) The sensors are positioned directly above the VOCs leakage source for the testing process.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for acetone. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for acetonitrile. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for benzene. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for Dichloromethane. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for Diethyl ether. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for ethanol. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for formic acid. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for heptane. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for hexane. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for isopropanol. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for methanol. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>TVOC for SGP30, and IAQ-index for SGP40 tests results for toluene. (<b>a</b>) the SGP30 tests results from 1 m directly under the leakage source, (<b>b</b>) the SGP40 tests results from 1 m directly under the leakage source, (<b>c</b>) SGP30 tests results after shifting the leakage source 1 m to the side of the first position, (<b>d</b>) the SGP40 tests results after shifted the leakage source 1 m to the side of the first position, (<b>e</b>) the H20 robot-SGP30 tests results from 22 cm from the VOC leakage source, and (<b>f</b>) the H20 robot-SGP40 tests results from 22 cm from the VOC leakage source.</p>
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<p>Percentage of successful gas detection attempts for both sensors.</p>
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<p>Response time for SGP30 and SGP40 gas sensors for several tested VOCs.</p>
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34 pages, 11305 KiB  
Article
A Study to Explore the Dew Condensation Potential of Cars
by Marc Muselli, Danilo Carvajal and Daniel A. Beysens
Atmosphere 2022, 13(1), 65; https://doi.org/10.3390/atmos13010065 - 30 Dec 2021
Cited by 4 | Viewed by 3622
Abstract
The metal surfaces of a car exhibit favorable properties for the passive condensation of atmospheric water. Under certain nocturnal climatic conditions (high relative humidity, weak windspeed, and total nebulosity), dew is often observed on cars, and it is appropriate to ask the question [...] Read more.
The metal surfaces of a car exhibit favorable properties for the passive condensation of atmospheric water. Under certain nocturnal climatic conditions (high relative humidity, weak windspeed, and total nebulosity), dew is often observed on cars, and it is appropriate to ask the question of using a vehicle as a standard condenser for estimating the dew yield. In order to see whether cars can be used as reference dew condensers, we report a detailed study of radiative cooling and dew formation on cars in the presence of radiating obstacles and for various windspeeds. Measurements of temperature and condensed dew mass on different car parts (rooftop, front and back hoods, windshield, lateral and back windows, inside and outside air) are compared with the same data obtained on a horizontal, thermally isolated planar film. The paper concludes that heat transfer coefficients, evaluated from temperature and dew yield measurements, are found nearly independent of windspeed and tilt angles. Moreover, this work describes the relation between cooling and dew condensation with the presence or not of thermal isolation. This dependence varies with the surface tilt angle according to the angular dependence of the atmosphere radiation. This work also confirms that cars can be used to estimate the dew yields in a given site. A visual observation scale h = Kn, with h the dew yield (mm) and n = 0, 1 2, 3 an index, which depends whether dew forms or not on rooftop, windshield, and lateral windows, is successfully tested with 8 different cars in 5 sites with three different climates, using K = (0.067 ± 0.0036) mm·day−1. Full article
(This article belongs to the Special Issue Feature Papers in Atmosphere Science)
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<p>(<b>a</b>) Definition of the apparent angle above horizontal <span class="html-italic">α</span> of nearby obstacles, angle <span class="html-italic">φ</span> of the condensing surface <span class="html-italic">S</span> with horizontal, skyview angle <span class="html-italic">SV</span>, and sky radiation angle <span class="html-italic">θ</span> with vertical. (<b>b</b>–<b>d</b>) Effect of obstacle radiations from ground and obstacles (angle <span class="html-italic">α</span> + <span class="html-italic">φ</span>) and skyview limitation (angle 180° − <span class="html-italic">α</span> − <span class="html-italic">φ</span>). Other notations: see text.</p>
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<p>Experimental sites. (<b>a</b>) Ajaccio site. 1: VW and AD cars, oriented SSE; 2: nearby parked car; 3: planar condenser used as a reference; 4: meteorological station. The North direction is indicated by the black arrow. (From Google Earth, 1 October 2020). (<b>b</b>) Valparaiso. The arrow indicates the DC car location and the cross the meteo station. (From Google Earth, 1 August 2019).</p>
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<p>(<b>a</b>). Mean number of events with respect to the wind direction at night (21 h−7 h) between 15 July and 30 November 2020. Orange lines: all nights; blue line: only nights with dew events. (<b>b</b>) The corresponding distribution of windspeed at 10 m.</p>
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<p>Characteristics of (<b>a</b>) windshield, (<b>b</b>) other windows (<b>c</b>) roof top, and (<b>d</b>) reference condenser. Thicknesses are denoted by <span class="html-italic">e</span>, thermal conductivity by <span class="html-italic">λ</span>, heat transfer coefficients by <span class="html-italic">a</span>, temperatures by <span class="html-italic">T</span>. Subscripts correspond to: <span class="html-italic">a</span>—air, <span class="html-italic">g</span>—glass, <span class="html-italic">s</span>—sandwich film, <span class="html-italic">p</span>, rooftop metal, <span class="html-italic">i</span>, thermal isolation, <span class="html-italic">b</span>—ground basis. Superscripts ‘ and ‘’ correspond to different values. <span class="html-italic">R<sub>i</sub></span> is the radiative deficit flux.</p>
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<p>Cars under study with the position of the K-thermocouple sensors. (<b>a</b>,<b>b</b>): VW and AD cars (Ajaccio, France); (<b>c</b>): DC car (Valparaiso, Chile). (Scale and actual angles are not respected on pictures).</p>
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<p>(<b>a</b>) Directional emissivity <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi>θ</mi> </msub> </mrow> </semantics></math> with respect to angle <math display="inline"><semantics> <mi>θ</mi> </semantics></math> with vertical for different total emissivities <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi>s</mi> </msub> </mrow> </semantics></math> (Equation (11) with <span class="html-italic">b</span> = 1.8). The interrupted vertical lines (A) corresponds to the influence of the nearby vehicle in the NNE direction, with emissivity near unity. The double interrupted vertical line (B) corresponds to the influence of the nearby buildings in nearly all directions, with emissivity near unity. The continuous vertical lines (C) corresponds to the limiting sky views related to the windows tilt angles <span class="html-italic">φ</span>. (<b>b</b>) Variation with tilt angle <math display="inline"><semantics> <mi>φ</mi> </semantics></math> of radiation deficit in Ajaccio at <span class="html-italic">T</span> = 15 °C with no obstacles, corresponding to the precipitable water vapor PWV = 0.78 cm (data interpolated from [<a href="#B31-atmosphere-13-00065" class="html-bibr">31</a>]). The vertical lines correspond to the window’s tilt angles. (<b>c</b>) Variation with respect to the tilt angle of the relative radiative deficit <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mo>,</mo> <mo> </mo> <mi>φ</mi> </mrow> <mo>)</mo> </mrow> <mo>/</mo> <msub> <mi>R</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mrow> <mn>0</mn> <mo>,</mo> <mn>0</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> at various obstacle view angles <math display="inline"><semantics> <mi>α</mi> </semantics></math> (data interpolated from [<a href="#B30-atmosphere-13-00065" class="html-bibr">30</a>]).</p>
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<p>Night temperature evolution of outside air temperature, <span class="html-italic">T<sub>a</sub></span>, temperature inside the car <span class="html-italic">T</span>′<span class="html-italic"><sub>a</sub></span>, dew point temperature <span class="html-italic">T<sub>d</sub></span>, rooftop temperature, <span class="html-italic">T<sub>RT</sub></span>, windshield temperature, <span class="html-italic">T<sub>WS</sub></span>, and difference <span class="html-italic">T<sub>RT</sub></span> − <span class="html-italic">T</span>′<span class="html-italic"><sub>a</sub></span>. (<b>a</b>) VW car in Ajaccio (23–24 April 2021, time UT + 2). (<b>b</b>) DC car in Valparaiso (28–29 April 2015, time UT − 3).</p>
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<p>Recorded data for the reference condenser (<span class="html-italic">T</span><sub>0</sub>) and left and right windows on the AD car for the night 6–7 November 2020 (hh:mm:ss, UT + 1) in Ajaccio. (<b>a</b>) Differences with air temperature <span class="html-italic">T<sub>a</sub></span> of the temperatures of reference (<span class="html-italic">T</span><sub>0</sub>), left (<span class="html-italic">T<sub>LW</sub></span>), and right (<span class="html-italic">T<sub>RW</sub></span>) windows. (<b>b</b>) Corresponding windspeed <span class="html-italic">V</span> and wind direction <span class="html-italic">Dir V</span>. (<b>c</b>) Cooling efficiency ratio <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>L</mi> <mi>W</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>R</mi> <mi>W</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (left and right windows, respectively). (<b>d</b>): Left and right window cooling with respect to air temperature (<span class="html-italic">T<sub>LW,RW</sub></span> − <span class="html-italic">T<sub>a</sub></span>) as a function of windspeed <span class="html-italic">V</span><sub>10</sub> extrapolated at 10 m from the ground. Curves are smoothing functions. During this night, dew yields were (REF) <span class="html-italic">h<sub>REF</sub></span> = 0.265 mm·day<sup>−1</sup>, (LW) <span class="html-italic">h<sub>LW</sub></span> = 0.039 mm·day<sup>−1</sup> and (RW) <span class="html-italic">h<sub>RW</sub></span> = 0.043 mm·day<sup>−1</sup>, corresponding in the visual scale to <span class="html-italic">n</span> = 3 (≈ 0.17 mm·day<sup>−1</sup> using the factor 0.056 mm·day<sup>−1</sup> from <a href="#sec4dot3dot4-atmosphere-13-00065" class="html-sec">Section 4.3.4</a>).</p>
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<p>Recorded data for the reference condenser (<span class="html-italic">T</span><sub>0</sub>) and left and right windows on the AD car for the night 6–7 November 2020 (hh:mm:ss, UT + 1) in Ajaccio. (<b>a</b>) Differences with air temperature <span class="html-italic">T<sub>a</sub></span> of the temperatures of reference (<span class="html-italic">T</span><sub>0</sub>), left (<span class="html-italic">T<sub>LW</sub></span>), and right (<span class="html-italic">T<sub>RW</sub></span>) windows. (<b>b</b>) Corresponding windspeed <span class="html-italic">V</span> and wind direction <span class="html-italic">Dir V</span>. (<b>c</b>) Cooling efficiency ratio <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>L</mi> <mi>W</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>R</mi> <mi>W</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (left and right windows, respectively). (<b>d</b>): Left and right window cooling with respect to air temperature (<span class="html-italic">T<sub>LW,RW</sub></span> − <span class="html-italic">T<sub>a</sub></span>) as a function of windspeed <span class="html-italic">V</span><sub>10</sub> extrapolated at 10 m from the ground. Curves are smoothing functions. During this night, dew yields were (REF) <span class="html-italic">h<sub>REF</sub></span> = 0.265 mm·day<sup>−1</sup>, (LW) <span class="html-italic">h<sub>LW</sub></span> = 0.039 mm·day<sup>−1</sup> and (RW) <span class="html-italic">h<sub>RW</sub></span> = 0.043 mm·day<sup>−1</sup>, corresponding in the visual scale to <span class="html-italic">n</span> = 3 (≈ 0.17 mm·day<sup>−1</sup> using the factor 0.056 mm·day<sup>−1</sup> from <a href="#sec4dot3dot4-atmosphere-13-00065" class="html-sec">Section 4.3.4</a>).</p>
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<p>Recorded data for the reference condenser (<span class="html-italic">T</span><sub>0</sub>) and rooftop and front hood on the AD car for the night 13–14 November 2020 (hh:mm:ss, UT + 1) in Ajaccio. (<b>a</b>) Differences with air temperature <span class="html-italic">T<sub>a</sub></span> of the temperatures of reference (<span class="html-italic">T</span><sub>0</sub>), rooftop (<span class="html-italic">T<sub>RT</sub></span>), and front hood (<span class="html-italic">T<sub>FH</sub></span>). (<b>b</b>) Corresponding windspeed <span class="html-italic">V</span> and wind direction <span class="html-italic">Dir V</span>. (<b>c</b>) Cooling efficiency ratio <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>R</mi> <mi>T</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>F</mi> <mi>H</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (roof top and front hood, respectively). (<b>d</b>): Roof top and front hood cooling (<span class="html-italic">T<sub>RT,FH</sub></span> − <span class="html-italic">T<sub>a</sub></span>) with respect to air temperature <span class="html-italic">T<sub>a</sub></span> as a function of windspeed <span class="html-italic">V</span><sub>10</sub> extrapolated at 10 m from the ground. Curves are smoothing functions. During this night, dew yields were (REF) <span class="html-italic">h<sub>REF</sub></span> = 0.197 mm, (RT) <span class="html-italic">h<sub>RT</sub></span> = 0.148 mm and (FH) <span class="html-italic">h<sub>FH</sub></span> = 0.043 mm, with <span class="html-italic">n</span> = 2 (≈0.11 mm·day<sup>−1</sup> using the factor 0.056 mm·day<sup>−1</sup> from <a href="#sec4dot3dot4-atmosphere-13-00065" class="html-sec">Section 4.3.4</a>).</p>
Full article ">Figure 10
<p>Recorded data from rooftop RT, back trunk BT, windshield WS, back window BW of the DC car in Valparaiso during the night 28–29 April 2015 (hh:mm:ss, UT-3). (<b>a</b>) Differences with air temperature <span class="html-italic">T<sub>a</sub></span> of the temperatures of rooftop (<span class="html-italic">T<sub>RT</sub></span>), back trunk (<span class="html-italic">T<sub>BT</sub></span>), windshield (<span class="html-italic">T<sub>WS</sub></span>), and back window (<span class="html-italic">T<sub>BW</sub></span>). (<b>b</b>) Corresponding windspeed <span class="html-italic">V</span> and wind direction <span class="html-italic">Dir V</span>. (<b>c</b>) Cooling efficiency ratio <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>R</mi> <mi>T</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>B</mi> <mi>T</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>W</mi> <mi>S</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>T</mi> <mrow> <mi>B</mi> <mi>W</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math>. Roof top value is, by definition, constant and equal to 0.8 (Equation (3)). (<b>d</b>): Roof top, back trunk, windshield, back window cooling (<span class="html-italic">T</span> − <span class="html-italic">T<sub>a</sub></span>) as a function of windspeed <span class="html-italic">V</span><sub>10</sub> extrapolated at 10 m from the ground. Curves are smoothing functions. During this night, <span class="html-italic">n</span> = 3 (≈0.17 mm·day<sup>−1</sup> using the factor 0.056 mm·day<sup>−1</sup> from <a href="#sec4dot3dot4-atmosphere-13-00065" class="html-sec">Section 4.3.4</a>). The calculated dew yield using [<a href="#B8-atmosphere-13-00065" class="html-bibr">8</a>] of <a href="#sec4dot2dot2-atmosphere-13-00065" class="html-sec">Section 4.2.2</a> gives (REF) <span class="html-italic">h<sub>REF</sub></span> = 0.15 mm.</p>
Full article ">Figure 11
<p>Daily mean relative cooling efficiencies <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> and daily dew yield <span class="html-italic">h<sub>c</sub></span> (mm·day<sup>−1</sup>) for the different car surfaces during condensation (<span class="html-italic">T<sub>j</sub></span> − <span class="html-italic">T<sub>d</sub></span>) &lt; 0 in Ajaccio. Error bars correspond to one SD. (<b>a</b>) rooftop RT; (<b>b</b>) front hood FH; (<b>c</b>) windshield WS; (<b>d</b>) left window LW; (<b>e</b>) right window RW; (<b>f</b>) back window BW; <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>T</mi> <mo>*</mo> </msup> <mo> </mo> </mrow> </semantics></math> data: Full squares (VW) and open squares (AD). Dew yield data: Dark bars (VW) and light bars (AD). The value for Valparaiso is indicated for comparison by a red circle (BT is compared with FH).</p>
Full article ">Figure 12
<p>Variation with tilt angle <math display="inline"><semantics> <mi>φ</mi> </semantics></math> of the temperature efficiency <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> for the VW, AD, and DC cars, fitted to Equation (30) by using the polynomial functions Equations (32) and (33) with <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>j</mi> </msub> <mo>/</mo> <msub> <mi>a</mi> <mn>0</mn> </msub> </mrow> </semantics></math> as adjustable parameters (curves). The isolated surfaces (RT, FH) are fitted separately from the non-isolated surfaces (WS, BW, LW). (<b>a</b>) Mean values for the VW car in Ajaccio. (<b>b</b>) Mean values for the AD car in Ajaccio. (<b>c</b>) DC car in Valparaiso. Red full circle and continuous curve: RT thermally isolated surface and obstacle view angle <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>. Blue open triangle and dotted curve: Non-isolated BW surface and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>. Brown inverted triangle and double-interrupted curve: Non-isolated WS surfaces and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>. Green open square and interrupted curve: Non-isolated BT surface and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 12 Cont.
<p>Variation with tilt angle <math display="inline"><semantics> <mi>φ</mi> </semantics></math> of the temperature efficiency <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msup> <mi>T</mi> <mo>*</mo> </msup> </mrow> </semantics></math> for the VW, AD, and DC cars, fitted to Equation (30) by using the polynomial functions Equations (32) and (33) with <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>j</mi> </msub> <mo>/</mo> <msub> <mi>a</mi> <mn>0</mn> </msub> </mrow> </semantics></math> as adjustable parameters (curves). The isolated surfaces (RT, FH) are fitted separately from the non-isolated surfaces (WS, BW, LW). (<b>a</b>) Mean values for the VW car in Ajaccio. (<b>b</b>) Mean values for the AD car in Ajaccio. (<b>c</b>) DC car in Valparaiso. Red full circle and continuous curve: RT thermally isolated surface and obstacle view angle <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>. Blue open triangle and dotted curve: Non-isolated BW surface and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>. Brown inverted triangle and double-interrupted curve: Non-isolated WS surfaces and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>. Green open square and interrupted curve: Non-isolated BT surface and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>≈</mo> <mn>30</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Instantaneous values of <span class="html-italic">j</span>-inverse surface temperature difference with air, <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>T</mi> <mi>a</mi> </msub> <mo>−</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>∝</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> </semantics></math>, the <span class="html-italic">j</span>-heat transfer coefficient, with respect to mean windspeed value <span class="html-italic">V</span><sub>10</sub> at 10 m off the ground. Data are taken during condensation (<span class="html-italic">T<sub>j</sub></span> &lt; <span class="html-italic">T<sub>d</sub></span>). (<b>a</b>) Reference foil, (<b>b</b>) rooftop, (<b>c</b>) front hood, (<b>d</b>) windshield, (<b>e</b>) back window, (<b>f</b>) left window, (<b>g</b>) right window. VW (red dots and continuous curves) and AD (blue dots and interrupted curves) cars are considered separately. The curves are smoothing functions.</p>
Full article ">Figure 14
<p>(<b>a</b>) Windspeed versus wind direction during dew formation (<span class="html-italic">T<sub>j</sub></span> &lt; <span class="html-italic">T<sub>d</sub></span>). (<b>b</b>–<b>h</b>) Instantaneous values of <span class="html-italic">j</span>- inverse surface temperature difference with air <math display="inline"><semantics> <mrow> <msup> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>T</mi> <mi>a</mi> </msub> <mo>−</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>~</mo> <mo> </mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> </semantics></math>, the <span class="html-italic">j</span>-heat transfer coefficient, with respect to wind direction Dir(<span class="html-italic">V</span>) during condensation (<span class="html-italic">T<sub>j</sub></span> &lt; <span class="html-italic">T<sub>d</sub></span>). (<b>b</b>) Reference foil, (<b>c</b>) rooftop, (<b>d</b>) front hood, (<b>e</b>) windshield, (<b>f</b>) back window, (<b>g</b>) left window, (<b>h</b>) right window. VW and AD cars are both considered.</p>
Full article ">Figure 15
<p>Variation of the heat transfer coefficients with windspeed. (<b>a</b>) REF surface. (<b>b</b>) VW and AD rooftop and front hood surfaces. VW car: Red circles (RT) and brown triangles (FH). AD car: Blue square (RT) and light blue diamond (FH).</p>
Full article ">Figure 16
<p>(<b>a</b>) Variation of <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> <mi>c</mi> </msub> <msub> <mi>T</mi> <mi>d</mi> </msub> <mo>/</mo> <msub> <mi>ρ</mi> <mi>v</mi> </msub> </mrow> </semantics></math> with respect to <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> <mo>−</mo> <msub> <mi>T</mi> <mi>c</mi> </msub> </mrow> </semantics></math> for the Ajaccio reference surface. The proportionality factor is (3.14 ± 0.09) × 10<sup>−5</sup> m<sup>4</sup>·K.s<sup>−1</sup>·Kg<sup>−1</sup> (uncertainty: one standard deviation) corresponding to the value <span class="html-italic">a<sub>REF</sub></span> = 2.2 W·m<sup>−2</sup>·K<sup>−1</sup>. (<b>b</b>) Calculated heat transfer coefficient at different angles as calculated from (full symbols) the <span class="html-italic">a<sub>j</sub></span>/<span class="html-italic">a<sub>REF</sub></span> values from temperature measurements (<a href="#sec4dot2dot1-atmosphere-13-00065" class="html-sec">Section 4.2.1</a>, <a href="#atmosphere-13-00065-t005" class="html-table">Table 5</a>) with <span class="html-italic">a<sub>REF</sub></span> = 2.2 W·m<sup>−2</sup>·K<sup>−1</sup>, and the means of <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>h</mi> <mo>˙</mo> </mover> <mi>c</mi> </msub> <mo>/</mo> <mrow> <mo>(</mo> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> <mo>−</mo> <msub> <mi>T</mi> <mi>c</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> data from Equation (38) (values from <a href="#atmosphere-13-00065-t006" class="html-table">Table 6</a>, open symbols). VW car: red circles, full and dotted lines. AD car: squares, interrupted, and double interrupted lines. DC car: Green triangles and large interrupted line. REF: Black diamond. The shadow area corresponds to thermally isolated surfaces.</p>
Full article ">Figure 17
<p>Comparison between the calculated sum of dew yields from meteo data (sum(<span class="html-italic">h<sub>m</sub></span>)) and experimental values (<b>a</b>) from the reference surface (sum(<span class="html-italic">h<sub>REF</sub></span>)) and the VW and AD car surfaces: (<b>b</b>) RT, (<b>c</b>) FH, (<b>d</b>) WS, (<b>e</b>) LW, (<b>f</b>) RW, and (<b>g</b>) BW. The data are fitted to sum(<span class="html-italic">h<sub>j</sub></span>) = <span class="html-italic">λ<sub>j</sub></span> sum(<span class="html-italic">h<sub>m</sub></span>). The <span class="html-italic">λ<sub>j</sub></span> values are noted in the figures. Standard deviations are ≈ 0.01. Red dots: VW car; blue open squares: AD car.</p>
Full article ">Figure 17 Cont.
<p>Comparison between the calculated sum of dew yields from meteo data (sum(<span class="html-italic">h<sub>m</sub></span>)) and experimental values (<b>a</b>) from the reference surface (sum(<span class="html-italic">h<sub>REF</sub></span>)) and the VW and AD car surfaces: (<b>b</b>) RT, (<b>c</b>) FH, (<b>d</b>) WS, (<b>e</b>) LW, (<b>f</b>) RW, and (<b>g</b>) BW. The data are fitted to sum(<span class="html-italic">h<sub>j</sub></span>) = <span class="html-italic">λ<sub>j</sub></span> sum(<span class="html-italic">h<sub>m</sub></span>). The <span class="html-italic">λ<sub>j</sub></span> values are noted in the figures. Standard deviations are ≈ 0.01. Red dots: VW car; blue open squares: AD car.</p>
Full article ">Figure 18
<p>(<b>a</b>) Variation of sum(<span class="html-italic">h<sub>m</sub></span>) (red dots) and sum(<span class="html-italic">h<sub>REF</sub></span>) (blue open squares) as a function of the visual scale sum(<span class="html-italic">n</span>). The values correspond to the result of linear fits (straight lines, uncertainties: One SD). (<b>b</b>) Variation of sum(<span class="html-italic">h<sub>RT</sub></span>) for VW rooftop (blue open squares), AD rooftop (green open triangles) and VW + AD rooftops (red dots). The values correspond to the result of linear fits (straight lines, uncertainties: One SD).</p>
Full article ">Figure 19
<p>Values of factor <span class="html-italic">k</span> between dew yield on a thermally isolated horizontal plane and from the observation scale (Equations (1) and (43)–(45)) as a function of measurement site and type of car. The mean value <span class="html-italic">k</span> = 0.056 ± 0.003 mm·day<sup>−1</sup> is indicated with one standard deviation uncertainty. The value under the bracket is for comparison with a thermally isolated 30° inclined plane and corresponds to <span class="html-italic">K</span> = 1.2 <span class="html-italic">k</span> = 0.067 ± 0.0036 mm·day<sup>−1</sup>.</p>
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20 pages, 3852 KiB  
Article
Influence of Maintenance Actions in the Drying Stage of a Paper Mill on CO2 Emissions
by Luis Miguel Calvo and Rosario Domingo
Processes 2021, 9(10), 1707; https://doi.org/10.3390/pr9101707 - 23 Sep 2021
Cited by 2 | Viewed by 2427
Abstract
Greenhouse gases from industrial activities have become a global problem. Emissions management is being developed to raise awareness of the importance of controlling pollution in general and atmospheric emissions in particular. In 2017, the deficit of the rights of issuance in the industrial [...] Read more.
Greenhouse gases from industrial activities have become a global problem. Emissions management is being developed to raise awareness of the importance of controlling pollution in general and atmospheric emissions in particular. In 2017, the deficit of the rights of issuance in the industrial sectors increased up to 8.3% (verified emissions in 2017 versus allocation in 2017). This trend will increase more at the end of Phase III due to a progressive reduction in allocation. Phase IV will be much more restrictive in allocating emission rights than Phase III. The extra cost of this deficit reinforces the need for industry in general to reduce CO2 and for the paper industry to reduce GHG emissions and generate credits. Old factories are typically identified as sources of pollution in addition to being inefficient compared to new factories. This article discusses the possibilities offered by maintenance actions, whose integration into a process can successfully reduce the environmental impact of industrial plants, particularly by reducing the CO2 equivalent emissions (CO2-eq units henceforth CO2) they produce. This case study analyzes the integration of maintenance rules that enable significant thermal energy savings and consequently CO2 emissions reduction associated with papermaking. Managing Key Performance Indicators (KPIs), such as the amount of cold water added to the boiler circuit and the conditions of the air blown into the dryer section hood, can be used as indicators of CO2 emissions generated. The control of the water and temperature reduces these emissions. A defined measure—in this case, t CO2/t Paper—indicates an achievement of a 21% reduction in emissions over the past 8 years. Full article
(This article belongs to the Special Issue Optimization Technology of Greenhouse Gas Emission Reduction)
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Figure 1

Figure 1
<p>Process flow.</p>
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<p>Daily average steam production and cold water added to the circuit due to boiler blowdown.</p>
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<p>Daily and drying steam losses.</p>
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<p>Yearly CO<sub>2</sub> emissions and CO<sub>2</sub> losses.</p>
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<p>Evolution of the indicator—“t CO<sub>2</sub>/t Paper” over 10 years.</p>
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<p>Monthly evolution of the indicator “t CO<sub>2</sub>/t Paper” over 10 years: (<b>a</b>) Years 1, 2, 3 and 4; (<b>b</b>) Years 5, 6 and 7; (<b>c</b>) Years 8, 9 and 10.</p>
Full article ">Figure 6 Cont.
<p>Monthly evolution of the indicator “t CO<sub>2</sub>/t Paper” over 10 years: (<b>a</b>) Years 1, 2, 3 and 4; (<b>b</b>) Years 5, 6 and 7; (<b>c</b>) Years 8, 9 and 10.</p>
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13 pages, 2745 KiB  
Article
Hydraulic Properties of Forest Soils with Stagnic Conditions
by Stefan Julich, Janis Kreiselmeier, Simon Scheibler, Rainer Petzold, Kai Schwärzel and Karl-Heinz Feger
Forests 2021, 12(8), 1113; https://doi.org/10.3390/f12081113 - 20 Aug 2021
Cited by 10 | Viewed by 3519
Abstract
Tree species, e.g., shallow vs. deep rooting tree species, have a distinct impact on hydrological properties and pore size distribution of soils. In our study, we determined the soil hydrologic properties and pore size distribution at three forest stands and one pasture as [...] Read more.
Tree species, e.g., shallow vs. deep rooting tree species, have a distinct impact on hydrological properties and pore size distribution of soils. In our study, we determined the soil hydrologic properties and pore size distribution at three forest stands and one pasture as reference on soils with stagnant water conditions. All sites are located in the Wermsdorf Forest, where historical studies have demonstrated severe silvicultural problems associated with stagnant water in the soil. The studied stands represent different stages of forest management with a young 25-year-old oak (Sessile Oak (Quercus petraea) and Red oak (Q. robur)) plantation, a 170-year-old oak stand and a 95-year-old Norway Spruce (Picea abies) stand in second rotation. We determined the infiltration rates under saturated and near-saturated conditions with a hood-infiltrometer at the topsoil as well as the saturated hydraulic conductivity and water retention characteristic from undisturbed soil samples taken from the surface and 30 cm depth. We used the bi-modal Kosugi function to calculate the water retention characteristic and applied the normalized Young-Laplace equation to determine the pore size distribution of the soil samples. Our results show that the soils of the old stands have higher amounts of transmission pores, which lead to higher infiltration rates and conductance of water into the subsoil. Moreover, the air capacity under the old oak was highest at the surface and at 30 cm depth. There was also an observable difference between the spruce and oak regarding their contrasting root system architecture. Under the oak, higher hydraulic conductivities and air capacities were observed, which may indicate a higher and wider connected macropore system. Our results confirm other findings that higher infiltration rates due to higher abundance of macropores can be found in older forest stands. Our results also demonstrate that an adapted forest management is important, especially at sites affected by stagnant water conditions. However, more measurements are needed to expand the existing data base of soil hydraulic properties of forest soils in temperate climates. Full article
(This article belongs to the Section Forest Soil)
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Figure 1

Figure 1
<p>Overview over the site locations in the Wermsdorf Forest with photos of the surroundings and the respective soil profile. SPR95 = 95-year-old spruce stand in second generation; OAK25 = 25-year-old oak afforestation; OAK170 = old mixed deciduous stand with oaks up to 170 years old; MEA = meadow used as reference.</p>
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<p>Field conductivity (log10 transformed in cm day<sup>−1</sup>) under saturated (Kfs) and near-saturated (|h = −1 cm|) (Kfns) at the four sites.</p>
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<p>Bulk density (<b>a</b>) and saturated hydraulic conductivity (log10) measured in the laboratory and (<b>b</b>) derived from the 250 cm³ cores for the four sites for surface and 30 cm depth, respectively; sample size <span class="html-italic">n</span> = 5 for all sites and depths. Same lowercase letters indicate no significant differences among sites (<span class="html-italic">p</span> &lt; 0.05). Same uppercase letters in a column indicate no significant differences within the sites among depth (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Volume fraction of different pore size classes for the samples from the surface (upper plot) and samples from 30 cm depth (lower plot) obtained from the area under bimodal pore size distribution Classes were fissures (diameter ∅ &gt; 500 μm), transmission (∅ 50–500 μm), storage (∅ 1.5–50 μm), and fine pores (∅ &lt; 1.5 μm). Fissures, transmission, and storage pores are derived from the measured part of the water retention curve, and the fine pores are determined from the extrapolated part of the water retention curve. Same lowercase letters over the bars are not significantly different at <span class="html-italic">p</span> &lt; 0.05 among the pore size classes.</p>
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18 pages, 37599 KiB  
Article
Development of a Condensation Model and a New Design of a Condensation Hood—Numerical and Experimental Study
by Mieszko Tokarski, Arkadiusz Ryfa, Piotr Buliński, Marek Rojczyk, Krzysztof Ziarko, Ziemowit Ostrowski and Andrzej J. Nowak
Energies 2021, 14(5), 1344; https://doi.org/10.3390/en14051344 - 2 Mar 2021
Cited by 2 | Viewed by 2351
Abstract
The development of a numerical model and design for the innovative construction of a heat exchanger (HE) used in a condensation hood (being a part of the combi-steamer) are described in this work. The model covers an air-steam flow, heat transfer, and a [...] Read more.
The development of a numerical model and design for the innovative construction of a heat exchanger (HE) used in a condensation hood (being a part of the combi-steamer) are described in this work. The model covers an air-steam flow, heat transfer, and a steam condensation process. The last two processes were implemented with the use of an in-house model introduced via User Defined Functions (UDF). As the condensate volume is negligible compared to the steam, the proposed model removes the condensate from the domain. This approach enabled the usage of a single-phase flow for both air and steam using a species transport model. As a consequence, a significant mesh and computation time reduction were achieved. The new heat exchanger is characterised by reorganised fluid flow and by externally finned pipes (contrary to the original construction, where internally finned pipes were used). This allowed a reduction in the number of the pipes from 48 to 5, which significantly simplifies construction and manufacturing process of the HE. The redesigned HE was tested in two cases: one simulating normal working conditions with a combi-steamer, the other with extremely high heat load. Measurement data showed that the numerical model predicted condensate mass flow rate (3.67 g/s computed and 3.56 g/s measured) and that the condensation capability increased at least by 15% when compared to the original HE design. Full article
(This article belongs to the Section J: Thermal Management)
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<p>Condensation hood: (<b>a</b>) original construction; (<b>b</b>) redesigned construction; 1—fan outlet (coolant air inlet); 2—air outlet; 3—steam vents.</p>
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<p>Redesigned heat exchanger: (<b>a</b>)—rear view; (<b>b</b>)—side view; (<b>c</b>)—top view. II.c—air vent.</p>
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<p>Computational domain: (<b>a</b>) air path/zone; (<b>b</b>) steam path/zone. 1—air inlet; 2—air outlet; 3—steam inlets; 4—steam exit. I.a &amp; I.b—steam vent; I.c &amp; I.d—water trap; I.e—lower header; I.f—upper header; I.g—air/steam zone connector; II.a and II.b—flow guides; II.c—air vent.</p>
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<p>Schematic algorithm of the developed condensation and heat transfer model within developed UDF.</p>
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<p>Fragment of the numerical mesh. View on the headers, pipe, and fin cell zone.</p>
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<p>(<b>a</b>) Cross-sections; (<b>b</b>) H<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>O mass fraction; (<b>c</b>) temperature distribution; (<b>d</b>) velocity magnitude.</p>
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<p>H1 cross-section: (<b>a</b>) H<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>O mass fraction; (<b>b</b>) temperature distribution; (<b>c</b>) velocity magnitude.</p>
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<p>H2 cross-section: (<b>a</b>) H<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>O mass fraction; (<b>b</b>) temperature distribution; (<b>c</b>) velocity magnitude.</p>
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<p>V1 cross-section: (<b>a</b>) H<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>O mass fraction; (<b>b</b>) temperature distribution; (<b>c</b>) velocity magnitude.</p>
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<p>V2 cross-section: (<b>a</b>) H<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>O mass fraction; (<b>b</b>) temperature distribution; (<b>c</b>) velocity magnitude.</p>
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18 pages, 1895 KiB  
Article
Factors Impacting Range Hood Use in California Houses and Low-Income Apartments
by Haoran Zhao, Wanyu R. Chan, William W. Delp, Hao Tang, Iain S. Walker and Brett C. Singer
Int. J. Environ. Res. Public Health 2020, 17(23), 8870; https://doi.org/10.3390/ijerph17238870 - 28 Nov 2020
Cited by 22 | Viewed by 4746
Abstract
Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single [...] Read more.
Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single family houses and 17 low-income apartments in California in which cooking activities, range hood use, and fine particulate matter (PM2.5) were monitored for one week per home. Range hoods were used for 36% of cooking events in houses and 28% in apartments. The frequency of hood use increased with cooking frequency across homes. In both houses and apartments, the likelihood of hood use during a cooking event increased with the duration of cooktop burner use, but not with the duration of oven use. Actual hood use rates were higher in the homes of participants who self-reported more frequent use in a pre-study survey, but actual use was far lower than self-reported frequency. Residents in single family houses used range hoods more often when cooking caused a discernible increase in PM2.5. In apartments, residents used their range hood more often only when high concentrations of PM2.5 were generated during cooking. Full article
(This article belongs to the Special Issue Health Risk Assessment Related to Environmental Exposure)
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<p>An example of full range hood use during a cooktop event with associated particulate matter emissions.</p>
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<p>Distribution of total minutes of cooktop (CT) and oven (OV) use per home per week at each hour of the day in houses and apartments.</p>
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<p>Range hood use for all cooking events by home.</p>
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<p>Distribution of range hood use in each home grouped by survey responses for (<b>a</b>) houses (any CT) and (<b>b</b>) apartments (all cooking types). Boxes show interquartile range (IQR), whiskers are limit values within 75th + 1.5IQR and 25th − 1.5IQR, and circles show all data outside of whiskers.</p>
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