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Computation
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Post-Modern Computational Fluid Dynamics
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Post-Modern Computational Fluid Dynamics

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5603

Special Issue Editor

Dr. Dimitrios Koubogiannis

E-Mail Website
Guest Editor
Department of Naval Architecture, School of Engineering, University of West Attica, Aigaleo, Greece
Interests: CFD (techniques, industrial applications); computational methods; energy technology; reduced-order modeling; design optimization; flow control; modeling of energy systems (power cycles, waste heat recovery, natural gas liquefaction, supercritical CO2 cycle); operational and embodied energy; life cycle assessment in buildings and ships

Special Issue Information

Dear Colleagues,

Computational fluid dynamics (CFD) has matured and now dominates many branches of research and engineering applications. Nowadays, there are a lot of sufficiently tested and well-documented CFD codes available (commercial, in-house laboratorial ones, of free access), capable of simulating engineering flows. In conjunction with the continued development and upgrading of computing facilities, CFD offers the capability of faster and low-cost simulations that can provide detailed description of flows, compared to experiments. Furthermore, CFD provides the capability to obtain insight into cases for which no experimental data exist or are not even feasible. The availability of reliable CFD tools enables one to tackle specialized problems and industrial applications of various diverse fields, as well as to develop methods relying on CFD, such as design optimization, flow control, reduced-order models, machine-learning-assisted CFD, etc. In light of the above, it is my immense pleasure to invite you to contribute to a high-impact Special Issue entitled “Post-Modern Computational Fluid Dynamics”, mainly focusing on industrial or specialized CFD applications (in energy technology and mechanical, naval, civil and chemical engineering) and on developing either CFD techniques or methods based on CFD. Topics of interest include, but are not limited to, the following:

  • CFD models and techniques;
  • Fast fluid dynamics;
  • CFD applications (industrial, novel, specialized, micro-scale to large-scale);
  • Design optimization and application in multidisciplinary systems;
  • Flow control;
  • Reduced-order modeling of flows;
  • Machine learning in CFD.

Dr. Dimitrios Koubogiannis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CFD techniques
  • CFD applications
  • design optimization
  • flow control
  • reduced-order modeling
  • machine learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

Jump to: Review

18 pages, 4031 KiB  
Article
Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part B: Hypersonic Vehicles
by Angelos Klothakis and Ioannis K. Nikolos
Computation 2024, 12(10), 200; https://doi.org/10.3390/computation12100200 - 4 Oct 2024
Viewed by 619
Abstract
In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper [...] Read more.
In the past decade, there has been significant progress in the development, testing, and production of vehicles capable of achieving hypersonic speeds. This area of research has garnered immense interest due to the transformative potential of these vehicles. Part B of this paper initially explores the current state of hypersonic vehicle development and deployment, as well as the propulsion technologies involved. At next, two additional test cases, used for the evaluation of DSMC code SPARTA are analyzed: a Mach 12.4 flow over a flared cylinder and a Mach 15.6 flow over a 25/55-degree biconic. These (2D-axisymmetric) test cases have been selected as they are tailored for the assessment of flow and heat transfer characteristics of present and future hypersonic vehicles, for both their external and internal aerodynamics. These test cases exhibit (in a larger range compared to the test cases presented in Part A of this work) shock–boundary and shock–shock interactions, which can provide a fair assessment of the SPARTA DSMC solver accuracy, in flow conditions which characterize hypersonic flight and can adequately test its ability to qualitatively and quantitatively capture the complicated physics behind such demanding flows. This validation campaign of SPARTA provided valuable experience for the correct tuning of the various parameters of the solver, especially for the use of adequate computational grids, thus enabling its subsequent application to more complicated three-dimensional test cases of hypersonic vehicles. Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Figure 1

Figure 1
<p>Flared cylinder geometry (units in millimeters) [<a href="#B29-computation-12-00200" class="html-bibr">29</a>].</p> Full article ">Figure 2
<p>(<b>Top</b>) Heat transfer on the external surface of the cylinder. (<b>Bottom</b>) Pressure distribution on the external surface of the cylinder (LENS Run 11).</p> Full article ">Figure 3
<p>(<b>Top</b>) Rotational temperature. (<b>Bottom</b>) Total temperature (LENS Run 11, “Refined_Grid”).</p> Full article ">Figure 4
<p>(<b>Top</b>) Velocity component along the <span class="html-italic">x</span>-axis. (<b>Bottom</b>) Velocity component along the r-axis (LENS Run 11, “Refined_Grid”).</p> Full article ">Figure 5
<p>(<b>Top</b>) Pressure at the flared cylinder surface. (<b>Bottom</b>) Heat flux on the flared cylinder surface (LENS Run 11, “Refined_Grid”).</p> Full article ">Figure 5 Cont.
<p>(<b>Top</b>) Pressure at the flared cylinder surface. (<b>Bottom</b>) Heat flux on the flared cylinder surface (LENS Run 11, “Refined_Grid”).</p> Full article ">Figure 6
<p>The double-cone geometry [<a href="#B29-computation-12-00200" class="html-bibr">29</a>,<a href="#B36-computation-12-00200" class="html-bibr">36</a>].</p> Full article ">Figure 7
<p>Heat transfer computation on the surface of the biconic. The effect of the grid quality on the DSMC simulation results is pronounced.</p> Full article ">Figure 8
<p>Static pressure computation on the surface of the biconic.</p> Full article ">Figure 9
<p>(<b>Left</b>) Contours of axial velocity component. (<b>Right</b>) Contours of radial velocity component (biconic test case).</p> Full article ">Figure 10
<p>(<b>Left</b>) Contours of velocity. (<b>Right</b>) Contours of number density (biconic test case).</p> Full article ">Figure 11
<p>(<b>Left</b>) Contours of the rotational temperature. (<b>Right</b>) Contours of the total temperature (biconic test case).</p> Full article ">Figure 12
<p>The complicated flow formations near the surface of the body (biconic test case).</p> Full article ">Figure 13
<p>(<b>Left</b>) Contours of static pressure on the surface of the biconic. (<b>Right</b>) Contours of the number of particles hitting the surface elements of the grid (biconic test case).</p> Full article ">
24 pages, 16220 KiB  
Article
Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part A: Fundamentals
by Angelos Klothakis and Ioannis K. Nikolos
Computation 2024, 12(10), 198; https://doi.org/10.3390/computation12100198 - 1 Oct 2024
Viewed by 764
Abstract
The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation [...] Read more.
The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations. Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Figure 1

Figure 1
<p>A typical flowchart of the DSMC algorithm.</p> Full article ">Figure 2
<p>(<b>Top</b>) parallel efficiency; (<b>Bottom</b>) memory spread (flat-plate test case) [<a href="#B39-computation-12-00198" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>Velocity contours.</p> Full article ">Figure 4
<p>Rotational temperature contours.</p> Full article ">Figure 5
<p>(<b>Top</b>) pressure distribution at the upper surface; (<b>Bottom</b>) shear stress distribution along <math display="inline"><semantics> <mrow> <mi>x</mi> </mrow> </semantics></math>-axis. DAC results are obtained from [<a href="#B39-computation-12-00198" class="html-bibr">39</a>].</p> Full article ">Figure 6
<p>Upper-surface heat flux. DAC results obtained from [<a href="#B39-computation-12-00198" class="html-bibr">39</a>].</p> Full article ">Figure 7
<p>(<b>Top</b>) the geometry of the planetary probe; (<b>Bottom</b>) the positions of the corresponding thermocouples (1 to 9).</p> Full article ">Figure 8
<p>Surface heat transfer (“flow conditions 1” subcase).</p> Full article ">Figure 9
<p>Fore-cone computational grid (zoom-in view).</p> Full article ">Figure 10
<p>(<b>Left</b>) axial velocity component; (<b>Right</b>) radial velocity component (“flow conditions 1” subcase).</p> Full article ">Figure 11
<p>(<b>Left</b>) flow-field streamlines; (<b>Right</b>) velocity magnitude contours (“flow conditions 1” subcase).</p> Full article ">Figure 12
<p>(<b>Left</b>) rotational temperature contours; (<b>Right</b>) total temperature contours (“flow conditions 1” subcase).</p> Full article ">Figure 13
<p>Number-density distribution (“flow conditions 1” subcase).</p> Full article ">Figure 14
<p>Surface heat transfer (“flow conditions 2” subcase).</p> Full article ">Figure 15
<p>(<b>Left</b>) axial velocity; (<b>Right</b>) radial velocity (“flow conditions 2” subcase).</p> Full article ">Figure 16
<p>(<b>Left</b>) flow-field streamlines; (<b>Right</b>) velocity contours (“flow conditions 2” subcase).</p> Full article ">Figure 17
<p>(<b>Left</b>) rotational temperature; (<b>Right</b>) total temperature (“flow conditions 2” subcase).</p> Full article ">Figure 18
<p>Number-density contours (“flow conditions 2” subcase).</p> Full article ">Figure 19
<p>Three-dimensional contours of the surface heat-flux contours on the 70-degree blunted cone at (<b>a</b>) 0°, (<b>b</b>) 10°, (<b>c</b>) 20°, and (<b>d</b>) 30° angle of attack (“flow conditions 1” subcase).</p> Full article ">Figure 20
<p>Line plots of the surface heat flux of the 70-degree interplanetary probe at (<b>a</b>) 0°, (<b>b</b>) 10°, (<b>c</b>) 20°, and (<b>d</b>) 30° angle of attack (“flow conditions 1” subcase).</p> Full article ">
24 pages, 16040 KiB  
Article
Design and Evaluation of a Hypersonic Waverider Vehicle Using DSMC
by Angelos Klothakis and Ioannis K. Nikolos
Computation 2024, 12(7), 140; https://doi.org/10.3390/computation12070140 - 9 Jul 2024
Viewed by 1209
Abstract
This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was [...] Read more.
This work investigates the aerodynamic performance of a hypersonic waverider designed to operate at Mach 7, focusing on optimizing its design through advanced computational methods. Utilizing the Direct Simulation Monte Carlo (DSMC) method, the three-dimensional flow field around the specifically designed waverider was simulated to understand the shock wave interactions and thermal dynamics at an altitude of 90 km. The computational approach included detailed meshing around the vehicle’s critical leading edges and the use of three-dimensional iso-surfaces of the Q-criterion to map out the shock and vortex structures accurately. Additional simulation results demonstrate that the waverider achieved a lift–drag ratio of 2.18, confirming efficient aerodynamic performance at a zero-degree angle of attack. The study’s findings contribute to the broader understanding of hypersonic flight dynamics, highlighting the importance of precise computational modeling in developing vehicles capable of operating effectively in near-space environments. Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Figure 1

Figure 1
<p>Schematic representation of the waverider design methodology.</p> Full article ">Figure 2
<p>Schematic representation of the waverider design methodology.</p> Full article ">Figure 3
<p>The geometry of the 7-degree half cone used for the calculation of the initial flow field.</p> Full article ">Figure 4
<p>Streamlines of the three-dimensional flow field around the 7-degree cone. Side view (<b>top</b>), top view (<b>middle</b>), rear view (<b>bottom</b>).</p> Full article ">Figure 5
<p>Waverider surface along the flow streamlines, in comparison with the initial cone. Top view (<b>top</b>), side view (<b>middle</b>), and rear view (<b>bottom</b>).</p> Full article ">Figure 5 Cont.
<p>Waverider surface along the flow streamlines, in comparison with the initial cone. Top view (<b>top</b>), side view (<b>middle</b>), and rear view (<b>bottom</b>).</p> Full article ">Figure 6
<p>Back section of the waverider (units in mm).</p> Full article ">Figure 7
<p>Surface lofts along the vehicle profiles (<b>top</b>). Vehicle overview without the nose section (<b>bottom</b>).</p> Full article ">Figure 8
<p>Nose section with upper boundary surface.</p> Full article ">Figure 9
<p>(<b>Top</b>): Waverider sections and the complete geometry. (<b>Bottom</b>): Flowchart of the design methodology.</p> Full article ">Figure 9 Cont.
<p>(<b>Top</b>): Waverider sections and the complete geometry. (<b>Bottom</b>): Flowchart of the design methodology.</p> Full article ">Figure 10
<p>The utilized surface mesh. Lower surface (<b>top</b>) and isometric view (<b>bottom</b>).</p> Full article ">Figure 11
<p>Pressure contours around the vehicle (<b>top</b>), Knudsen number of the flow field based on the waverider length (<b>bottom</b>).</p> Full article ">Figure 11 Cont.
<p>Pressure contours around the vehicle (<b>top</b>), Knudsen number of the flow field based on the waverider length (<b>bottom</b>).</p> Full article ">Figure 12
<p>Q-criterion contours around the vehicle.</p> Full article ">Figure 13
<p>Q-criterion contours on the plane of symmetry.</p> Full article ">Figure 14
<p>Q-criterion contours on a vertical plane.</p> Full article ">Figure 15
<p>Streamwise velocity contours on the symmetry plane of the waverider.</p> Full article ">Figure 16
<p>(<b>Top</b>): total temperature field around the vehicle. (<b>Bottom</b>): rotational temperature field (on the symmetry plane of the waverider).</p> Full article ">Figure 17
<p>Pressure contours on a horizontal plane parallel to the vehicle.</p> Full article ">Figure 18
<p>Streamwise velocity contours on a horizontal plane parallel to the vehicle.</p> Full article ">Figure 19
<p>Temperature contours on a horizontal plane parallel to the vehicle.</p> Full article ">Figure 20
<p>Q-criterion contours on a horizontal plane parallel to the vehicle.</p> Full article ">Figure 21
<p>Vorticity magnitude at the back of the vehicle.</p> Full article ">Figure 22
<p>Three-dimensional Q-criterion contours, colored by vorticity magnitude.</p> Full article ">Figure 23
<p>Overview of the three-dimensional Q-criterion contours around the vehicle, colored by velocity magnitude.</p> Full article ">Figure 24
<p>Overview of the lift (<b>bottom</b>) and drag (<b>top</b>) per unit surface, exerted on the waverider’s lower surface.</p> Full article ">Figure 25
<p>Mach number contours around the vehicle at the plane of symmetry.</p> Full article ">
15 pages, 10569 KiB  
Article
Numerical Simulation and Comparison of Different Steady-State Tumble Measuring Configurations for Internal Combustion Engines
by Andreas Theodorakakos
Computation 2024, 12(7), 138; https://doi.org/10.3390/computation12070138 - 8 Jul 2024
Viewed by 656
Abstract
To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state [...] Read more.
To enhance air–fuel mixing and turbulence during combustion, spark ignition internal combustion engines commonly employ tumble vortices of the charge inside the cylinder. The intake phase primarily dictates the generated tumble, which is influenced by the design of the intake system. Utilizing steady-state flow rigs provides a practical method to assess an engine’s cylinder head design’s tumble-generating characteristics. This study aims to conduct computational fluid dynamics (CFD) numerical simulations on various configurations of steady-state flow rigs and compare the resulting tumble ratios. The simulations are conducted for different inlet valve lifts of a four-valve cylinder head with a shallow pent-roof. The findings highlight variations among these widely adopted configurations. Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Figure 1

Figure 1
<p>Ricardo tumble adaptor: (<b>a</b>) side view (“T-type” and “L-type”); (<b>b</b>) front view “L-type”; (<b>c</b>) front view “L-type”. The red arrows illustrate the tumbling motion of the flow.</p> Full article ">Figure 2
<p>FEV tumble adaptor: (<b>a</b>) side view; (<b>b</b>) front view. The red arrows illustrate the tumbling motion of the flow.</p> Full article ">Figure 3
<p>(<b>a</b>) HWA velocity measuring (arrows illustrate the flow field inside the cylinder); (<b>b</b>) PIV velocity measuring.</p> Full article ">Figure 4
<p>Different views of the model for the simulated geometry: (<b>a</b>) 3D view; (<b>b</b>) Side view; (<b>c</b>) Top view.</p> Full article ">Figure 5
<p>Computational regions for (<b>a</b>) Ricardo T-tube tumble adaptor; (<b>b</b>) Ricardo L-tube tumble adaptor.</p> Full article ">Figure 6
<p>Computational domain for (<b>a</b>) FEV tumble adaptor; (<b>b</b>) HWA steady-state measuring configuration.</p> Full article ">Figure 7
<p>Cross-section of the computational grid used at the vertical plane bisecting the axis of an inlet valve for each configuration: (<b>a</b>) Ricardo T-tube tumble adaptor; (<b>b</b>) FEV tumble adaptor; (<b>c</b>) HWA steady-state measuring configuration.</p> Full article ">Figure 8
<p>Comparison of the predicted flow coefficients for all cases.</p> Full article ">Figure 9
<p>Comparison of the predicted tumble ratios for all cases. Note that the tumble ratios for each case are calculated using different methods.</p> Full article ">Figure 10
<p>Simulation results for HWA configuration: (<b>a</b>) streamlines for 4 mm valve lift colored by velocity magnitude; (<b>b</b>) streamlines for 8 mm valve lift colored by velocity magnitude; (<b>c</b>) non-dimensional velocity vectors and velocity magnitude contours in the cylinder’s symmetry plane and inside the exit tube, for 10 mm valve lift.</p> Full article ">Figure 11
<p>Velocity magnitude contours for the HWA configuration, in a plane parallel to the valve plate in the valve seat region and a horizontal plane neat the flow straightener: (<b>a</b>) 2 mm valve lift; (<b>b</b>) 10 mm valve lift.</p> Full article ">Figure 12
<p>Simulation results for the Ricardo T-tube tumble adaptor: (<b>a</b>) streamlines for 4 mm valve lift colored by velocity magnitude; (<b>b</b>) streamlines for 8 mm valve lift colored by velocity magnitude; (<b>c</b>) non-dimensional velocity vectors and velocity magnitude contours in the cylinder’s symmetry plane and inside the exit tube, for 10 mm valve lift.</p> Full article ">Figure 13
<p>Simulation results for the Ricardo L-tube tumble adaptor: (<b>a</b>) streamlines for 4 mm valve lift colored by velocity magnitude; (<b>b</b>) streamlines for 8 mm valve lift colored by velocity magnitude; (<b>c</b>) non-dimensional velocity vectors and velocity magnitude contours in the cylinder’s symmetry plane and inside the exit tube, for 10 mm valve lift.</p> Full article ">Figure 14
<p>Simulation results for the FEV tumble adaptor: (<b>a</b>) streamlines for 4 mm valve lift colored by velocity magnitude; (<b>b</b>) streamlines for 8 mm valve lift colored by velocity magnitude; (<b>c</b>) non-dimensional velocity vectors and velocity magnitude contours in the cylinder’s symmetry plane and inside the exit tube, for 10 mm valve lift.</p> Full article ">

Review

Jump to: Research

38 pages, 6913 KiB  
Review
Computational Fluid Dynamics-Based Systems Engineering for Ground-Based Astronomy
by Konstantinos Vogiatzis, George Angeli, Gelys Trancho and Rod Conan
Computation 2024, 12(7), 143; https://doi.org/10.3390/computation12070143 - 11 Jul 2024
Viewed by 1684
Abstract
This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the [...] Read more.
This paper presents the state-of-the-art techniques employed in aerothermal modeling to respond to the current observatory design challenges, particularly those of the next generation of extremely large telescopes (ELTs), such as the European ELT, the Thirty Meter Telescope International Observatory (TIO), and the Giant Magellan Telescope (GMT). It reviews the various aerothermal simulation techniques, the synergy between modeling outputs and observatory integrating modeling, and recent applications. The suite of aerothermal modeling presented includes thermal network models, Computational Fluid Dynamics (CFD) models, solid thermal and deformation models, and conjugate heat transfer models (concurrent fluid/solid simulations). The aerothermal suite is part of the overall observatory integrated modeling (IM) framework, which also includes optics, dynamics, and controls. The outputs of the IM framework, nominally image quality (IQ) metrics for a specific telescope state, are fed into a stochastic framework in the form of a multidimensional array that covers the range of influencing operational parameters, thus providing a statistical representation of observatory performance. The applications of the framework range from site selection, ground layer characterization, and site development to observatory performance current best estimate and optimization, active thermal control design, structural analysis, and an assortment of cost–performance trade studies. Finally, this paper addresses planned improvements, the development of new ideas, attacking new challenges, and how it all ties to the “Computational Fluid Dynamics Vision 2030” initiative. Full article
(This article belongs to the Special Issue Post-Modern Computational Fluid Dynamics)
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Figure 1

Figure 1
<p>Cycle of compliance and CBE analysis.</p> Full article ">Figure 2
<p>Stochastic framework flow-chart. (<b>A</b>) environmental inputs, (<b>B</b>) controlled parameters, (<b>C</b>) integrated modeling outputs, (<b>D</b>) data fusion and statistics.</p> Full article ">Figure 3
<p>Aerothermal modeling information flow-chart.</p> Full article ">Figure 4
<p>Representative ELT aerothermal requirement validation process.</p> Full article ">Figure 5
<p>Representative contour snapshots of the refractive index spatial gradient along the normal to the telescope elevation axis plane: (<b>top</b>) TIO, 30° zenith and 45° azimuth relative to wind, and (<b>bottom</b>) GMT, 0° zenith and 180° azimuth relative to wind. Wind speeds are median (6–7 m/s), and venting configuration is “open”.</p> Full article ">Figure 6
<p>Representative OPD maps (in nm) for TIO (<b>left</b>) and GMT (<b>right</b>).</p> Full article ">Figure 7
<p>C<sub>n</sub><sup>2</sup> profile at Las Campanas at the monitoring tower and inside the GMT enclosure. The coarse SLODAR-measured profile from the nearby Paranal site is superimposed.</p> Full article ">Figure 8
<p>Observatory simulations: PSSn vs. FWHM.</p> Full article ">Figure 9
<p>TIO Segment Support Assembly resolved components (<b>left</b>) and representative front sheet temperature distribution (<b>right</b>).</p> Full article ">Figure 10
<p>The GMT M1TCS modeling flow chart.</p> Full article ">Figure 11
<p>GMT ASMS model and representative pressure coefficient distribution.</p> Full article ">Figure 12
<p>TIO enclosure azimuth bogie with drive motor: typical nighttime surface temperature distribution.</p> Full article ">Figure 13
<p>GMT PVS model (<b>left</b>) and steady-state mid-plane air temperature contours (<b>right</b>).</p> Full article ">Figure 14
<p>TIO telescope structure thermal deformation magnitude (m) 12h after sunset.</p> Full article ">Figure 15
<p>GMT telescope structure thermal (<b>right</b>) and deformation (<b>left</b>) models.</p> Full article ">Figure 16
<p>TIO elevation drive model detail and surface temperature deviation from ambient.</p> Full article ">Figure 17
<p>TIO LGSF modeling flow chart.</p> Full article ">Figure 18
<p>Impact of the TIO deflector design on velocity levels around the telescope top end (velocity scale normalized with upwind value).</p> Full article ">
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