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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Design Overview of a High-Pressure Helium Flow Visualization Apparatus for Blanket Cooling Studies

Cooling of the fusion blanket first wall remains a significant challenge given the adverse conditions of heat and particle flux encountered near the plasma. Helium emerges as an attractive cooling candidate because of its chemical and neutronic inertness and separability from hydrogenic species (e.g. tritium). Because of the low thermal mass of helium, optimization of these coolant channels is warranted to provide high heat transfer performance at low pumping costs. Increasingly, computational fluid dynamics (CFD) simulations are employed to model and optimize these flow channels, and accompanying experimental data are needed to validate the predictions of these models. To provide the aforementioned experimental data, a high-pressure helium flow visualization upgrade has been designed for the Helium Flow Loop Experiment facility. This apparatus was built to American Society of Mechanical Engineers boiler and pressure vessel standards to withstand operating pressure of 4 MPa and mated to high-pressure glass windows. Seedless flow visualization is performed via high-speed background oriented schlieren (BOS), with image correlation used for time-resolved two-dimensional velocimetry at frequencies in excess of 60 kHz. Rectangular flow channel test articles are additively manufactured via laser powder bed fusion and installed into this visualization apparatus, with one-sided heating supplied by resistive heaters. In conclusion, the chosen test geometries were informed by prior CFD simulations, and the helium flow structures observed via BOS (detachment, recirculation, etc.) will be used for the validation of these accompanying models, in support of the design and optimization of blanket cooling channel configurations.

Helium flow↗
DOE OSTI2021

A High-Fidelity Modeling Tool to Support the Design of Oxy-Combustors for Direct-Fired Supercritical CO2 Cycles

The challenge in the design of oxy-combustors for direct-fired supercritical CO 2 (sCO 2 ) cycles is in addressing disparate performance metrics and objectives. Key design parameters to consider include, among others, injector design for mixing and flame stability, split of recycled CO 2 diluent between injectors and cooling films, target flame temperatures to control noncondensable products, and strategies to inject the diluent CO 2 for film cooling and thermal control. In order to support novel oxy-combustor designs, a high-fidelity yet numerically efficient modeling framework based on the CRUNCH CFD® flow solver is presented, featuring key physics-based submodels relevant in this regime. For computational efficiency in modeling large kinetic sets, a flamelet/progress variable (FPV) based tabulated-chemistry approach is utilized featuring a three-stream extension to allow for the simulation of the CO 2 film cooling stream in addition to the fuel and oxidizer streams. Finite rate chemistry effects are modeled in terms of multiple progress variables for the primary flame as well as for slower-evolving chemical species such as NO x and SO x contaminants. Real fluid effects are modeled using advanced equations of states. The predictive capabilities of this computationally tractable design support tool are demonstrated on a conceptual injector design for an oxy-combustor operating near 30 MPa. Simulations results provide quantitative feedback on the effectiveness of the film cooling as well as the level of contaminants (CO, NO, and N) in the exhaust due to impurities entering from the injectors. These results indicate that this framework would be a useful tool for refining and optimizing the oxy-combustor designs as well as risk mitigation analyses.

Engineering↗
NASA NTRSDate not supplied

Rapid Aero Modeling of a Lift+Cruise UAM Configuration for Stability and Control Using Overset Grid CFD

This talk will address some aspects of an effort to develop both static and dynamic stability and control derivatives of a generic Lift+Cruise Urban Air Mobility vehicle. In particular, the use of NASA overset grid CFD packages OVERFLOW and Chimera Grid Tools allowed for rapid grid and solution generation while varying many database factors. Approach details include coarse gridding and control effectors, rotor disk modeling, convergence evaluation, and parallel execution on a midrange compute resource. A brief *description of the Rapid Aero Modeling (RAM) approach, including design of experiments and response surface modeling, will be given, and recent simulations of powered and unpowered dynamic multisine maneuvers will be presented.

Overset Grid CFD↗
NASA NTRS1987

Wind tunnel requirements for computational fluid dynamics code verification

The role of experiment in the development of Computational Fluid Dynamics (CFD) for aerodynamic flow field prediction is discussed. Requirements for code verification from two sources that pace the development of CFD are described for: (1) development of adequate flow modeling, and (2) establishment of confidence in the use of CFD to predict complex flows. The types of data needed and their accuracy differs in detail and scope and leads to definite wind tunnel requirements. Examples of testing to assess and develop turbulence models, and to verify code development, are used to establish future wind tunnel testing requirements. Versatility, appropriate scale and speed range, accessibility for nonintrusive instrumentation, computerized data systems, and dedicated use for verification were among the more important requirements identified.

Marvin, Joseph G.↗
NASA NTRS1988

Wind tunnel requirements for computational fluid dynamics code verification

The role of experiment in the development of computational fluid dynamics (CFD) for aerodynamic flow field prediction is discussed. Requirements for code verification from two sources that pace the development of CFD are described for: (1) development of adequate flow modeling, and (2) establishment of confidence in the use of CFD to predict complex flows. The types of data needed and their accuracy differs in detail and scope and leads to definite wind tunnel requirements. Examples of testing to assess and develop turbulence models, and to verify code development, are used to establish future wind tunnel testing requirements. Versatility, appropriate scale and speed range, accessibility for nonintrusive instrumentation, computerized data systems, and dedicated use for verification were among the more important requirements identified.

Marvin, Joseph G.↗
DOE OSTI2024

High-Temperature Gas-Cooled Pebble-Bed Reactors Running In And Transient Modeling Capabilities Demonstration

This study presents a comprehensive benchmarking and verification effort of several thermal-hydraulic and multiphysics capabilities for high-temperature gas-cooled reactor (HTGR) applications. The first part of this effort focuses on the running-in verification of Griffin's multiphysics capabilities, specifically for simulating the evolution of Pebble Bed reactor cores from startup to equilibrium. In the absence of validation data, code-to-code comparisons are conducted with Kugelpy, showing good agreement for key quantities like maximum power density and fresh core k-eigenvalue predictions. However, discrepancies in equilibrium core predictions suggest potential issues with cross sections, underscoring the need for further refinement and evaluation. The HTTF system analysis code benchmark involves RELAP5-3D, SAM, and GAMMA+ to assess their predictive capabilities for HTTF behavior under both normal operation and pressurized conduction cooldown (PCC) transient conditions. While there is good agreement in predicting major parameters such as coolant temperature, solid temperature, and flow distribution, discrepancies in transient behavior highlight differences in modeling approaches, nodalizations, and heat transfer models. The HTTF lower plenum CFD benchmark employs nekRS to simulate flow mixing phenomena, successfully capturing relevant flow physics and demonstrating mesh independence in complex geometries. Preliminary results suggest a relatively uniform temperature field but significant unsteadiness in the flow, requiring time-averaging analyses. The GPBR200 system analysis code benchmark uses SAM's core channel and porous media models, incorporating an RCCS loop for decay heat removal. During steady-state and transient conditions, including protected de-pressurized and pressurized loss of forced cooling (DLOFC and PLOFC), both models show good agreement in predicting temperature profiles and key parameters. Notably, while the core channel model underpredicts convective heat transfer effects, both models maintain temperatures well below the TRISO fuel safety limit. These benchmarking efforts collectively enhance the predictive capabilities of the tools used in HTGR design and safety analysis, guiding developments to improve their accuracy and applicability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

An Overview of Preliminary Computational and Experimental Results for the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model

A summary of computational and experimental aeroelastic and aeroservoelastic (ASE) results for the Semi-Span Super-Sonic Transport (S4T) wind-tunnel model is presented. A broad range of analyses and multiple ASE wind-tunnel tests of the S4T have been performed in support of the ASE element in the Supersonics Program, part of NASA's Fundamental Aeronautics Program. The computational results to be presented include linear aeroelastic and ASE analyses, nonlinear aeroelastic analyses using an aeroelastic CFD code, and rapid aeroelastic analyses using CFD-based reduced-order models (ROMs). Experimental results from two closed-loop wind-tunnel tests performed at NASA Langley's Transonic Dynamics Tunnel (TDT) will be presented as well.

Silva, Walter A.↗
DOE OSTI2021

Studies of Alternative Ventilation Configurations to Mitigate Airborne Exposure Risks in Office Spaces

The objective of this study was to evaluate the impact of alternative ventilation configurations on airflow patterns and potential exposure risks in office spaces. Two existing conference rooms at Sandia NM were modeled using Computational Fluid Dynamics (CFD) simulations to characterize airflow patterns and potential airborne exposure risks in well-mixed and once-through (through-flow) ventilation conditions. Multiple scenarios were studied to evaluate the impact of occupancy, Plexiglass barriers, and a modified-return airflow configuration. Experimental and visualization tests were also conducted to validate the well-mixed and through-flow models and findings. The simulations demonstrated that the modified-return airflow configuration that promoted through-flow conditions reduced pathogen concentrations within the space compared to the well-mixed airflow configuration; occupancy reduction only reduced the number of exposed individuals, and Plexiglass barriers had almost no effect. The experimentally measured air speeds at nine anemometer locations generally matched the simulated airflow velocities, and a fog-purge visualization test was also consistent with simulated results of plume movement and dissipation. The visualization tests demonstrated improvements in air change rate with the modified return, which promoted through-flow conditions, versus the original well-mixed ventilation configuration. The results of this study demonstrate that minor modifications to a space that promote through-flow conditions can improve air quality and reduce pathogen concentrations. Additional airflow modeling and testing of alternative occupied space configurations are recommended to further inform room designs that mitigate airborne exposure risks for occupants.

42 ENGINEERING↗
NASA NTRS1993

Comparison of liquid rocket engine base region heat flux computations using three turbulence models

The flow in the base region of launch vehicles is characterized by flow separation, flow reversals, and reattachment. Computation of the convective heat flux in the base region and on the nozzle external surface of Space Shuttle Main Engine and Space Transportation Main Engine (STME) is an important part of defining base region thermal environments. Several turbulence models were incorporated in a CFD code and validated for flow and heat transfer computations in the separated and reattaching regions associated with subsonic and supersonic flows over backward facing steps. Heat flux computations in the base region of a single STME engine and a single S1C engine were performed using three different wall functions as well as a renormalization-group based k-epsilon model. With the very limited data available, the computed values are seen to be of the right order of magnitude. Based on the validation comparisons, it is concluded that all the turbulence models studied have predicted the reattachment location and the velocity profiles at various axial stations downstream of the step very well.

Kumar, Ganesh N.↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
NASA NTRS2011

Gas Generator Feedline Orifice Sizing Methodology: Effects of Unsteadiness and Non-Axisymmetric Flow

Engine LH2 and LO2 gas generator feed assemblies were modeled with computational fluid dynamics (CFD) methods at 100% rated power level, using on-center square- and round-edge orifices. The purpose of the orifices is to regulate the flow of fuel and oxidizer to the gas generator, enabling optimal power supply to the turbine and pump assemblies. The unsteady Reynolds-Averaged Navier-Stokes equations were solved on unstructured grids at second-order spatial and temporal accuracy. The LO2 model was validated against published experimental data and semi-empirical relationships for thin-plate orifices over a range of Reynolds numbers. Predictions for the LO2 square- and round-edge orifices precisely match experiment and semi-empirical formulas, despite complex feedline geometry whereby a portion of the flow from the engine main feedlines travels at a right-angle through a smaller-diameter pipe containing the orifice. Predictions for LH2 square- and round-edge orifice designs match experiment and semi-empirical formulas to varying degrees depending on the semi-empirical formula being evaluated. LO2 mass flow rate through the square-edge orifice is predicted to be 25 percent less than the flow rate budgeted in the original engine balance, which was subsequently modified. LH2 mass flow rate through the square-edge orifice is predicted to be 5 percent greater than the flow rate budgeted in the engine balance. Since CFD predictions for LO2 and LH2 square-edge orifice pressure loss coefficients, K, both agree with published data, the equation for K has been used to define a procedure for orifice sizing.

Rothermel, Jeffry↗
NASA NTRS2008

Rolls-Royce Low Noise Highly Variable Cycle Nozzle for Next Generation Supersonic Aircraft

An overview of the work performed by Rolls-Royce under contract NNL08AA29C is presented. The work includes computational fluid dynamic (CFD) analysis for, and design of, a highly variable cycle exhaust model for the Supersonic project (NRA NN06ZEA001N). The CFD analysis shows that the latest design improvements to the clam shell doors have increased flow through the ejector over that achieved with previous designs.

Sokhey, Jack S.↗
NASA NTRS2012

Quasi One-Dimensional Unsteady Modeling of External Compression Supersonic Inlets

The AeroServoElasticity task under the NASA Supersonics Project is developing dynamic models of the propulsion system and the vehicle in order to conduct research for integrated vehicle dynamic performance. As part of this effort, a nonlinear quasi 1-dimensional model of an axisymmetric external compression supersonic inlet is being developed. The model utilizes compressible flow computational fluid dynamics to model the internal inlet segment as well as the external inlet portion between the cowl lip and normal shock, and compressible flow relations with flow propagation delay to model the oblique shocks upstream of the normal shock. The external compression portion between the cowl-lip and the normal shock is also modeled with leaking fluxes crossing the sonic boundary, with a moving CFD domain at the normal shock boundary. This model has been verified in steady state against tunnel inlet test data and it s a first attempt towards developing a more comprehensive model for inlet dynamics.

Kopasakis, George↗
NASA NTRS2018

Helium Pressurization Modeling of the EDU Submerged Diffuser Using Thermal Desktop

NASA is currently working on developing its predictive modeling capabilities to support future missions that require long term storage of cryogenic propellants. Through the Evolvable Cryogenics project, Development and Validation of Analysis Tools (DVAT) task, both CFD and multi-node fluid modeling tools are being validated against to increase the following cryogenic fluid management technologies: Self-Pressurization, Pressure Control, Helium Pressurization, de-stratification, jet mixing, Tank and transfer line chill-down, tank filling and transfer. NASA is using previous and future micro-gravity experiments to validate both types of models. Recent activities include: Validation of the Tank Pressure Control Experiment (TPCE), Hydrogen and Helium pressurization, and Completion of the Zero Boil Off Tank (ZBOT) experiment on ISS. Future activities include validation of the ZBOT experiment with both CFD and multi-node analysis tools, validation of the Cryogenic Demonstration System of the Robotic Refueling Mission (RRM3).

low-gravity fluid modeling↗
DOE OSTI2019

Chapter 6: Modeling and Simulation of Wind-Farm Flows

The subject of this chapter is the modeling and simulation of the flow through full wind farms, also known as wind plants, which are collections of many wind turbines within a region working together to supply power to the grid like any other type of power plant. This topic is interesting and exciting because of the increasing variety of and uses for wind plant flow modeling and simulation. For example, Figure 6.1 shows the computed flow field from a high-fidelity simulation of the Lillgrund offshore wind farm using computational fluid dynamics (CFD) with large-eddy simulation (LES) turbulence modeling and actuator line rotor aerodynamics models. There are many ways to model this type of flow, ranging from very simple models of wind turbine wakes and their interactions that can run in a matter of seconds on a laptop to high-fidelity, turbulence -resolving LES of the atmospheric flow and wakes that require days of use of thousands of cores on a high-performance computing cluster. The uses of wind plant flow simulations are equally varied. Wind plant layout designers run hundreds or thousands of simulations over all possible wind directions and speeds using simple models to predict a future wind plant's annual energy production. Researchers use LESs of the wind plant to study phenomena such as wind turbine wake interactions with each other and with the atmospheric boundary layer (ABL). As computational resources become more readily available, it is even becoming common for turbine manufacturers to use CFD of some form to model wind plants in complex terrain to help in the wind turbine siting and selection process. We are beginning to see the expensive, high-fidelity tools being used to create data and knowledge that improves or enables the creation of new cheaper, lower fidelity tools. We are also beginning to see the low- and high-fidelity tools used together to perform uncertainty quantification of energy estimates and optimization of wind plant layouts. As new computing technologies mature, such as graphics processing unit (GPU)-based computing, the future of full wind plant flow modeling is exciting.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Multiobjective Multidisciplinary Optimization of Low-Boom Supersonic Transports Using Multifidelity Models

A multidisciplinary optimization (MDO) method has been developed to design a computational fluid dynamics (CFD) based low-boom configuration that can be obtained from a Pareto solution of a low-fidelity multiobjective MDO problem with mission constraints. This paper refines the developed MDO method using multifidelity models for CFD-based multiobjective MDO. The refined MDO method can generate a low-boom configuration that satisfies the mission requirements, has the lowest takeoff gross weight and the longest range for the low-boom mission, trims the low-boom cruise flight with fuel redistributions, and has a reversed equivalent area distribution closely matching a low-boom target with ground noise level below 70 PLdB. The validity of the refined MDO method is demonstrated by a design study of a low-boom supersonic transport that carries 40 passengers, flies a low-boom mission with cruise Mach of 1.7 and range of 3500 nm, and cruises overwater at Mach 1.8 with range of 3882 nm. Moreover, the refined MDO method eliminates the difference between the assumed cruise weight for CFD-based low-boom inverse design optimization and the estimated cruise weight of the optimal inverse design solution with respect to the mission requirements.

multidisciplinary optimization↗