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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Strategies for the GPU Implementation of the OVERFLOW CFD Code

Wondering how to port a large, computational fluid dynamics (CFD) solver, written in Fortran, to run effectively on GPUs? Join this talk to learn about the strategies NASA’s OVERFLOW CFD code has used to effectively utilize GPUs to greatly improve the time to solution compared to CPUs. OVERFLOW is an overset, structured grid, computational fluid dynamics (CFD) flow solver developed by NASA and widely used by government, US industry, and academia. It is known for its effective use of CPU hardware, but this talk will discuss recent efforts to modify the code to run efficiently on GPUs. We will describe our use of OpenACC, CUDA Fortran, and CUDA C++, discussing why and how we use each to map our problem onto NVIDIA GPUs. We will also describe structural changes we made in the code to expose enough parallelism to effectively use the GPU hardware. Finally, we will present the performance benefits from running OVERFLOW on GPUs compared to the well optimized CPU version of the code.

GPU Programming↗
NASA NTRS2024

Global Optimization via Quadratic Disjunctive Programming for Water Networks Design with Energy Recovery

Generalized disjunctive programming (GDP) models with bilinear and concave constraints, often seen in water network design, are challenging optimization problems. This work proposes quadratic and piecewise linear approximations for nonlinear terms to reformulate GDP models into quadratic GDP (QGDP) models that suitable solvers may solve more efficiently. We illustrate the benefits of the quadratic reformulation with a water treatment network design problem in which nonconvexities arise from bilinear terms in the mixers’ mass balances and concave investment cost functions of treatment units. Given the similarities with water network design problems, we suggest quadratic approximation for the GDP model for the optimal design of a large-scale reverse electrodialysis (RED) process. This power technology can recover energy from salinity differences between by-product streams of the water sector, such as desalination brine mixed with regenerated wastewater effluents. The solver Gurobi excels in handling QGDP problems, but weighing the problem’s precision and tractability balance is crucial. The piecewise linear approximation yields more accurate, yet larger QGDP models that may require longer optimization times in large-scale process synthesis problems.

Water Networks↗
NASA NTRSDate not supplied

Towards Robust and Accurate Implicit Gradient Methods for Second- and Third-Order Nodal-Gradient Cell-Centered Finite-Volume Discretizations on Tetrahedral Grids

In this paper, we introduce implicit gradient methods as alternatives to conventional least-squares gradient methods for second- and third-order nodal-gradient cell-centered finite-volume discretizations, where solutions are stored at cells but gradients are stored at nodes. Because of the unique configuration of solutions and gradients, implicit gradient systems developed for the node-centered edge-based discretization method can be directly applied once the numerical solutions are interpolated from cells to nodes with sufficient accuracy. The resulting defect-correction solver can be loosely coupled with a flow-equation solver, and at convergence, solutions and gradients that satisfy the corresponding residual equations are obtained. Each iteration is relatively cheap compared with least-squares methods involving hundreds of neighbors. Numerical results are presented for accuracy verification studies and some simple but realistic flow problems.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

combinatorial reasoning↗
NASA NTRSDate not supplied

Multifidelity Optimization with Transonic Flutter Constraints

This work considers static and dynamic aeroelastic optimization of a cantilevered platewing in transonic flow. Low- and high-fidelity aeroelastic predictions are fed into a standard trust region model management scheme in order to efficiently solve this expensive optimization problem with multifidelity methods. Differences in fidelity are entirely driven by different aerodynamic solvers: linear panel methods for the low-fidelity method, and inviscid CFD-based solvers for the high-fidelity response. The optimization problem utilizes shape, sizing, and trim variables to satisfy stress, trim, and flutter constraints, and is solved across a range of subsonic and transonic Mach numbers. The multifidelity method is able to provide a speed-up for all cases considered here, despite sizable inaccuracies in the low-fidelity response for transonic flows.

Bret K Stanford↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRSDate not supplied

Scale Resolving Simulations of Viking '75 Reentry Capsule Wake Flow

Scale resolving simulations of static wake tests of the '75 Viking capsule have been performed using two unstructured grid flow solvers, US3D and PyFR. Simulations with several resolutions have been performed at varying Mach numbers with the aim of solver validation for capsule wake flows as well as establishing simulation best practices.

Rathakrishnan Bhaskaran↗
NASA NTRSDate not supplied

Comparison of OVERFLOW Computational and Experimental Results for a Blunt Mars Entry Vehicle Concept during Supersonic Retropropulsion

Simulations of unsteady supersonic retropropulsion (SRP) flow over a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) blunt-body vehicle were performed using the OVERFLOW Computational Fluid Dynamics (CFD) solver. High-fidelity flow solver techniques, including Detached Eddy Simulation (DES) turbulence modeling and Adaptive Mesh Refinement (AMR), were employed to obtain improved realism in CFD predictions. Simulation conditions and geometry configurations were designed to match specific runs in the Descent System Study (DSS) wind tunnel testing (WTT) campaign. The accuracy of each simulation is assessed by direct comparison to experimental data. Comparisons of computational predictions of the SRP flowfield and bow shock shape to experimental schlieren imaging show reasonable prediction of mean shock shape, with approximately 10% similarity in shock standoff distance for selected conditions, as well as similarity in local, time-varying fluctuations of the shock-plume interaction. Comparisons of discrete measurements of surface pressure coefficient (Cp) indicate CFD accuracy within approximately 10% of the experiment across the majority of the model heatshield, with larger variations at some of the heatshield edge locations with stronger flow unsteadiness. Simulated unsteadiness of these chaotic flows, which were highly dynamic and multi-modal, was shown to be within 20-40% of experimentally-measured pressure standard deviation (SD) for the majority of the sampled locations.

Supersonic Retropropulsion↗
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

Accurate modeling of boundary-layer transition is an important aspect of developing greener air transport technologies. In that regard, transition models based on auxiliary transport equations offer a robust approach that is easily integrated into the Reynolds-averaged Navier-Stokes (RANS) solvers. Recent workshops under NATO and AIAA have identified the verification of transport-equations-based transition modeling as a critical aspect of reducing the scatter between the predictions of different CFD codes. Follow-on work has highlighted the need for highly dense grids to achieve an asymptotic convergence of transition related flow metrics. The present work examines the role of automatic near-body mesh adaptation capability in the NASA OVERFLOW CFD solver to enable verification studies in an efficient manner, and for establishing best practices for designing grids for the RANS-based transition models. A sensor function relevant to the Langtry-Menter \gamma-Re_{\theta t}\ transition model has been identified and used for error-based mesh adaptation for canonical configurations comprising a flat plate, and the S809 and NLR-7301 airfoils. The efficacy of the mesh adaptation approach is assessed for flow conditions involving multiple transition scenarios such as natural transition, separation-induced transition, and shock-induced transition. The results from this exploratory study indicate that the meshes adapted using the proposed sensor provide solutions that approach the references solutions obtained with uniformly refined hand-crafted meshes, in terms of the chosen metrics, and yield modest yet significant savings in grid count. We also highlight areas for improvement in the grid adaptation methodology within OVERFLOW.

CFD↗
NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

Accurate modeling of boundary-layer transition is an important aspect of developing greener air transport technologies. In that regard, transition models based on auxiliary transport equations offer a robust approach that is easily integrated into the Reynolds-averaged Navier-Stokes (RANS) solvers. Recent workshops under NATO and AIAA have identified the verification of transport-equations-based transition modeling as a critical aspect of reducing the scatter between the predictions of different CFD codes. Follow-on work has highlighted the need for highly dense grids to achieve an asymptotic convergence of transition related flow metrics. The present work examines the role of automatic near-body mesh adaptation capability in the NASA OVERFLOW CFD solver to enable verification studies in an efficient manner, and for establishing best practices for designing grids for the RANS-based transition models. A sensor function relevant to the Langtry-Menter \gamma-Re_{\theta t}\ transition model has been identified and used for error-based mesh adaptation for canonical configurations comprising a flat plate, and the S809 and NLR-7301 airfoils. The efficacy of the mesh adaptation approach is assessed for flow conditions involving multiple transition scenarios such as natural transition, separation-induced transition, and shock-induced transition. The results from this exploratory study indicate that the meshes adapted using the proposed sensor provide solutions that approach the references solutions obtained with uniformly refined hand-crafted meshes, in terms of the chosen metrics, and yield modest yet significant savings in grid count. We also highlight areas for improvement in the grid adaptation methodology within OVERFLOW.

CFD↗
NASA NTRSDate not supplied

Comparison of OVERFLOW Computational and Experimental Results for a Blunt Mars Entry Vehicle Concept during Supersonic Retropropulsion

Simulations of unsteady supersonic retropropulsion (SRP) flow over a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) blunt-body vehicle were performed using the OVERFLOW Computational Fluid Dynamics (CFD) solver. High-fidelity flow solver techniques, including Detached Eddy Simulation (DES) turbulence modeling and Adaptive Mesh Refinement (AMR), were employed to obtain improved realism in CFD predictions. Simulation conditions and geometry configurations were designed to match specific runs in the Descent System Study (DSS) wind tunnel testing (WTT) campaign. The accuracy of each simulation is assessed by direct comparison to experimental data. Comparisons of computational predictions of the SRP flowfield and bow shock shape to experimental schlieren imaging show reasonable prediction of mean shock shape, with approximately 10% similarity in shock standoff distance for selected conditions, as well as similarity in local, time-varying fluctuations of the shock-plume interaction. Comparisons of discrete measurements of surface pressure coefficient (Cp) indicate CFD accuracy within approximately 10% of the experiment across the majority of the model heatshield, with larger variations at some of the heatshield edge locations with stronger flow unsteadiness. Simulated unsteadiness of these chaotic flows, which were highly dynamic and multi-modal, was shown to be within 20-40% of experimentally-measured pressure standard deviation (SD) for the majority of the sampled locations.

Supersonic Retropropulsion↗
NASA NTRSDate not supplied

Scale-Resolving Simulations of Viking '75 Reentry Capsule Wake Flow

Scale resolving simulations of static wake tests of the '75 Viking capsule have been performed using two unstructured grid flow solvers, US3D and PyFR. Simulations with several resolutions have been performed at varying Mach numbers with the aim of solver validation for capsule wake flows as well as establishing simulation best practices.

Rathakrishnan Bhaskaran↗
NASA NTRSDate not supplied

CEA2022: A Modernization of NASA Glenn’s Software CEA (Chemical Equilibrium with Applications)

The software program “Chemical Equilibrium with Applications” (CEA) is used to solve chemical equilibrium, and compute thermodynamic and transport properties of the resulting mixture, and also has special solvers dedicated to rocket, shock, and detonation problems. We have recently completed a full re-write of CEA with modernization and improvements, called “CEA2022”. In this paper, we will give an overview of CEA2022’s features, and discuss some of the fundamental equations used by CEA2022, as well as the fundamental assumptions, in order to provide users with a complete understanding of the software’s methodology. Several enhancements have been made to the software, including modern software development practices, interface improvements, and additional features. The feature enhancements include: running cases in parallel with thread safe solves, thermodynamic and transport database updates, and allowing for negative and inert reactants. In terms of interface improvements, we have made CEA a reusable library by adding APIs for multiple languages, including Python, Matlab, Excel, Fortran, and C. The subroutine interface allows for integration with other applications, including flow-solver integration (i.e. with CFD). We also compare results between CEA2022 and the previous version (CEA2) as a validation of the new software.

chemical equilibrium↗
NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over NACA-0012 and OAT15A Airfoils

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012, OAT15A airfoils at different angles of attack covering pre- and post-buffet-onset regimes. The freestream Mach number is 0.75, and the Reynolds number based on the chord length is 10 and 3million, respectively. The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre- and post- buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the spanwise direction, WMLES predictions show buffeting consistent with the experiment.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over NACA-0012 and OAT15A Airfoils

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012, OAT15A airfoils at different angles of attack covering pre- and post-buffet-onset regimes. The freestream Mach number is 0.75, and the Reynolds number based on the chord length is 10 and 3million, respectively. The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre- and post- buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the spanwise direction, WMLES predictions show buffeting consistent with the experiment.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs using Conjugate Heat Transfer

Thermal management for battery is important for electric aircraft because battery temperature is critically important to vehicle safety, and it also has direct impact on the efficiency of the battery system. Because ambient air is a readily available resource for aircraft, this paper considers an active cooling concept with forced convection of ambient air through the battery pack. Conjugate heat transfer analysis is used to solve the coupled aero-thermal problem, which consists of a finite-volume computational fluid dynamics solver for the fluid domain, and a conduction heat transfer solver for the solid domain. A mixed Neumann and Dirichlet boundary condition is developed for the fluid-solid interface, which allows the solid domain to completely submerge in the fluid domain. A gradient-based optimization method is adopted, and the discrete adjoint approach implemented in DAFoam is used to efficiently compute the gradients. The aero-thermal coupling for primal analysis and gradient computation is handled using the OpenMDAO-based MPhys framework. A constant heat source is prescribed for the battery cells, and the battery shape (design variable) is optimized to minimize cooling pump power and battery weight (composite objective function) while keeping the battery temperature below a threshold (constraint). The optimized design achieves a 44.6% and 1.5% reduction in the cooling pump power and battery weight, respectively, and the maximal temperature constraint is satisfied. This work has the potential to reduce battery-pack weight, improve performance, and reduce the weight of thermal management systems for electric vertical take-off and landing aircraft.

thermal management↗
NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs Using Conjugate Heat Transfer

Thermal management for battery is important for electric aircraft because battery temperature is critically important to vehicle safety, and it also has direct impact on the efficiency of the battery system. Because ambient air is a readily available resource for aircraft, this paper considers an active cooling concept with forced convection of ambient air through the battery pack. Conjugate heat transfer analysis is used to solve the coupled aero-thermal problem, which consists of a finite-volume computational fluid dynamics solver for the fluid domain, and a conduction heat transfer solver for the solid domain. A mixed Neumann and Dirichlet boundary condition is developed for the fluid-solid interface, which allows the solid domain to completely submerge in the fluid domain. A gradient-based optimization method is adopted, and the discrete adjoint approach implemented in DAFoam is used to efficiently compute the gradients. The aero-thermal coupling for primal analysis and gradient computation is handled using the OpenMDAO-based MPhys framework. A constant heat source is prescribed for the battery cells, and the battery shape (design variable) is optimized to minimize cooling pump power and battery weight (composite objective function) while keeping the battery temperature below a threshold (constraint). The optimized design achieves a 44.6% and 1.5% reduction in the cooling pump power and battery weight, respectively, and the maximal temperature constraint is satisfied. This work has the potential to reduce battery-pack weight, improve performance, and reduce the weight of thermal management systems for electric vertical take-off and landing aircraft.

heat transfer↗