Results for “computation fluid dynamic modeling” — Page 22

NASA NTRS2023

Simulations of Zero Boiloff, Densification, and Solidification in a Large-Scale Liquid Hydrogen Tank

The current research utilizes an Energy of Fluid (EOF) approach to develop a finite volume-based Computation Fluid Dynamic (CFD) model to create a benchmark simulation of the densification of liquid hydrogen (LH2) in an experimental Integrated Refrigeration and Storage (IRAS) tank at the Kennedy Space Center (KSC). The computational code will incorporate the FLUENT pressure-based model with User Defined Functions (UDF) to implement the EOF model. The enhanced model will solve the energy equation in terms of internal energy and provide temperature and pressure calculations for a given tank geometry. Specifically, a convection term will be added to an already existing code for a more accurate simulation of LH2 densification. However, currently the research focuses on utilizing User Defined Scalars (UDS) with FLUENT to show the built-in solvers are as accurate as the written UDS. This will reduce the computational time down significantly by utilizing an already built in solver for the momentum equation. A custom UDF has been written to solve transient and steady state conduction, and results obtained using the UDF agree with results obtained using the built-in FLUENT solver. Currently, a convection UDF is being added and will be verified using known solutions for solidification and validated with the experimental results from the Cryogenics Test Laboratory (CTL) at KSC for the IRAS densification process.

Colin Philip Mahony↗

DOE OSTI2026

Modeling Radiolysis and Chemical Reactions during Dry Storage of Aluminum-clad Spent Nuclear Fuel

After aluminum-clad spent nuclear fuel (ASNF) is removed from the reactor, it is initially stored in spent fuel pools, which are specially designed water-filled basins that provide temporary cooling to reduce the temperature of the fuel assemblies and provide radiation shielding. ASNF continues to generate heat due to the radioactive decay of elements within the fuel, which persists for many years post-shutdown as the residual radioactive products decay into more stable elements. During the wet storage period, an oxyhydroxide layer composed of boehmite/bayerite forms on the surfaces of the aluminum cladding from exposure to water in the pools. Road-ready packaging for long-term disposition of the ASNF involves dry storage in helium backfilled DOE standard canisters (DSCs). When the ASNF is removed from water storage and dried, most of the water is removed, but some physisorbed and chemisorbed water remains in the oxyhydroxide layers. This residual water can produce hydrogen when exposed to radiation from the ASNF during dry storage. Predicting hydrogen accumulation over time in the DSCs is critical for long-term storage considerations. Previous modeling efforts have developed coupled computational fluid dynamics (CFD)-chemical models to simulate temperature, pressure, and gas phase concentrations within the DSCs. These models use the thermal field predicted by CFD as input to a radiolysis model for the gas phase and the surface oxyhydroxide layer chemistry. Given the long storage period of the DSCs and the impracticality of long-term experiments, a simulation-based approach is necessary to assess chemical evolution within the canisters. This study advances the development of a modeling framework designed to simulate the chemical evolution of spent fuel canisters. Both thermal and radiation-driven reactions are considered, with radiation kinetics quantified using G-values. Sensitivity analysis identifies key parameters influencing species composition. Reaction pathway diagrams offer insight into dominant species formation routes, enabling more effective comparisons between model predictions and experimental observations, particularly regarding the production of hydrogen. Results show that the model predicts significant hydrogen gas production with minimal oxygen generation, primarily due to hydrogen formation via boehmite pathways. These findings underscore the importance of accurately characterizing surface-bound species and radiolysis kinetics. A deeper understanding of these mechanisms is critical for evaluating the long-term safety of nuclear waste storage.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗

DOE OSTI2020

Transient Coupled Chemical-Thermal-Fluid Field Simulation for Sealed Aluminum-clad Spent Nuclear Fuel Storage Canister

As the first step toward developing three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model for unsealed and vented canister storage system, a 3D CFD model coupled with bulk gas radiolysis reactions was developed first for sealed DOE standard canisters filled with inert gas and trace amount of air and water. The workflow for constructing canister-scale 3D CFD models and coupling with gas phase radiolysis reactions were established, which can be readily extended to unsealed, vented canister storage system. This interim milestone report documents the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields and hydrogen gas concentrations within sealed DOE standard canisters over long period of time. In addition, a mesh refinement test was done to show that increasing the models mesh refinement had negligible impact upon the temperature profiles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

CFD Modeling of High-Flux Plate-and-Frame Membrane modules for industrial carbon capture

In this work, we explore the use of CO2-selective flat sheet membranes for capturing CO2 from point sources. We employ Computational Fluid Dynamics (CFD) models to design high-flux plate-and-frame membrane modules to achieve uniform flow distribution among membrane elements, minimize dead-end zones, and ease the common concentration polarization issue for gas separation membranes. The goal is to drive membrane technology improvements by providing better module designs for given membrane properties and operating conditions.

Dosso, Cheick↗

DOE OSTI2024

CFD Modeling of High-Flux Plate-and-Frame Membrane Modules for Industrial Carbon Capture

In this work, we explore the use of CO2-selective flat sheet membranes for capturing CO2 from point sources. We employ Computational Fluid Dynamics (CFD) models to design high-flux plate-and-frame membrane modules to achieve uniform flow distribution among membrane elements, minimize dead-end zones, and ease the common concentration polarization issue for gas separation membranes. The goal is to drive membrane technology improvements by providing better module designs for given membrane properties and operating conditions.

Dosso, Cheick↗

DOE OSTI2024

CFD modeling of high-flux plate-and-frame membrane modules for industrial carbon capture

In this work, we explore the use of CO2-selective flat sheet membranes for capturing CO2 from point sources. We employ Computational Fluid Dynamics (CFD) models to design high-flux plate-and-frame membrane modules to achieve uniform flow distribution among membrane elements, minimize dead-end zones, and ease the common concentration polarization issue for gas separation membranes. The goal is to drive membrane technology improvements by providing better module designs for given membrane properties and operating conditions.

Dosso, Cheick↗

DOE OSTI2025

Advanced Computational Modeling of High-Level Waste Vitrification at the Hanford Site

The U.S. Department of Energy (DOE) has selected vitrification for stabilizing legacy tank waste at the Hanford site, where radioactive waste from plutonium production was historically stored in underground tanks. This waste will be separated into low-activity waste (LAW) and high-level waste (HLW) fractions and processed at the Waste Treatment and Immobilization Plant (WTP). At WTP, glass melters are used for the vitrification of radioactive tank waste, transforming it into a stable borosilicate glass form for safe long-term storage. The melter vessel is constructed from highly durable and heat-resistant materials, where the vitrification process occurs. The main regions that are modeled are the melt pool, plenum, cold cap, riser/discharge chamber, and surrounding structure with insulation layers. Forced convection induced by air bubblers at the base of the melter ensure uniform temperature distribution and provide heat to the cold cap layer. The cold cap is a region of reacting batch feed that floats on top of the molten glass and is where the batch-to-glass reactions occur. Joule heating provided by electrodes mounted along the vertical walls of the melter and immersed directly in the glass, generates the necessary heat for the net endothermic conversion processes that occur in the cold cap. The high temperatures, radioactivity, and opaque nature of the glass prevent direct observation inside the melters. Therefore, computational models are essential for providing insight into factors that affect melter throughput. Thermocouples in the plenum provide operators with plenum temperature measurements. Operational adjustments include bubbling rate, voltage supplied to the electrodes, feed adjustments, and glass removal rate. Different computational fluid dynamics (CFD) models have been developed, each serving a specific purpose. There are CFD models of different scale melters, as well as models that capture the two-phase flow interfaces of rising bubbles in the molten glass or models with a simplified molten glass region so that the surrounding structure and plenum can be feasibly incorporated. Pilot-scale melter models have been developed to serve as validation of the methods employed in the simulation of the full-scale WTP melters. Models incorporating resolved bubbling are used to develop momentum source terms to implement into a single phase, multi-region, steady-state flow model that is being validated by measured process parameters such as glass production rate, voltage, input power, plenum temperatures, etc. The resolved bubbling model uses the multiphase volume of fluid approach to model the system with a high-resolution interface capturing scheme to maintain sharp interfaces between the molten glass and the air phase. The suite of CFD models is continually being improved to incorporate more realistic physics and achieve faster turnaround time. For example, an incremental controller is implemented to automatically adjust electrode voltage within the simulation to a molten glass set point temperature of 1150°C. Newer models feature improved meshes to ensure conformal meshes between regions and eliminate unnecessary mesh refinement in areas that are not of interest (such as boundary layers in offgas ports). Instead of explicitly modeling the structural, refractory, and insulation layers of the melter, a thermal resistance approach is used with published correlations used for boundary conditions. The development of robust and efficient CFD models will be instrumental in enabling the WTP to successfully fulfill its mission of safely stabilizing legacy nuclear waste.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗

DOE OSTI2024

A comprehensive numerical investigation on spray models for Direct-Injection Spark-Ignition engines

Gasoline direct-injection spark-ignition (DISI) engines generate a large portion of their unburned hydrocarbon (UHC) and soot emissions during the cold-start phase. A predictive computational fluid dynamics (CFD) modeling framework can be used to understand the physical processes that characterize fuel spray evolution and fuel-film formation at cold start conditions, which can help to reduce engine-out particulate emissions. This study systematically evaluated spray submodels and developed a set of simulation best practices for physical-numerical submodels with the goal of enabling accurate simulations of liquid spray behavior in a DISI engine. Three comprehensive experimental datasets containing free-spray projected liquid volume (PLV), liquid volume fraction (LVF), and near-field X-ray radiography data were used to validate the simulation results and evaluate the spray submodels. Systematic analysis delved into injected parcel distribution, droplet collision, spray breakup, and evaporation via a detailed assessment of the relevant spray submodels. Moreover, the effects of turbulence models and the initial turbulent flow properties on the liquid spray evolution were examined. Based on extensive calibration efforts, a set of simulation best practices for the free spray was developed and validated against the PLV/LVF data. Simulation results indicated that the uniform distribution for parcel initialization, coupled with appropriate droplet collision submodels, provides an improved spray morphology compared to the cluster distribution. The findings also underscored the importance of calibrating the Kelvin-Helmholtz Rayleigh-Taylor (KH-RT) breakup model constants and droplet heat transfer coefficient scaling factor to achieve favorable agreement regarding measured liquid penetration and spray widths. In conclusion, this study marks a substantial stride towards accurately predicting fuel film evolution and soot formation within DISI engine performance.

ECN Spray G↗

NASA NTRSDate not supplied

Transition prediction in hypersonic regime on complex geometries with RANS-based models

In the near future, RANS computations will continue to play a significant role in the design of hypersonic vehicles with complex geometries. Therefore, it is imperative to continue testing, benchmarking, and refining the RANS models. In the present study, we evaluate RANS-like, transport equations-based models for predicting laminar-turbulent transition over a full-scale scale model of the BOLT flight configuration that was tested in the CUBRC LENS-II wind tunnel test facility. Based on the availability of the experimental results, comparisons are made between computations from several pre-existing transition models and Computational Fluid Dynamics (CFD) codes, with an emphasis on using the same computational meshes and flow conditions for all computations. The analysis covers the sensitivity of the transition predictions to the input parameters for five different transition models (four mainstream models designed for low-speed flows and a uniquely high-speed model), grid resolution, and the details of model implementation across three different flow solvers. The results show that the phenomenological models can describe significant aspects of the measured transition front. However, a number of additional improvements are required before these models can offer more reliable estimates of transition in high-speed flows.

CFD↗

NASA NTRSDate not supplied

Transition Prediction in Hypersonic Regime on Complex Geometries With Rans-Based Models

In the near future, RANS computations will continue to play a significant role in the design of hypersonic vehicles with complex geometries. Therefore, it is imperative to continue testing, benchmarking, and refining the RANS models. In the present study, we evaluate RANS-like, transport equations-based models for predicting laminar-turbulent transition over a full-scale scale model of the BOLT flight configuration that was tested in the CUBRC LENS-II wind tunnel test facility. Based on the availability of the experimental results, comparisons are made between computations from several pre-existing transition models and Computational Fluid Dynamics (CFD) codes, with an emphasis on using the same computational meshes and flow conditions for all computations. The analysis covers the sensitivity of the transition predictions to the input parameters for five different transition models (four mainstream models designed for low-speed flows and a uniquely high-speed model), grid resolution, and the details of model implementation across three different flow solvers. The results show that the phenomenological models can describe significant aspects of the measured transition front. However, a number of additional improvements are required before these models can offer more reliable estimates of transition in high-speed flows.

CFD↗

NASA NTRS1999

Cryogenic Fuel Tank Draining Analysis Model

One of the technological challenges in designing advanced hypersonic aircraft and the next generation of spacecraft is developing reusable flight-weight cryogenic fuel tanks. As an aid in the design and analysis of these cryogenic tanks, a computational fluid dynamics (CFD) model has been developed specifically for the analysis of flow in a cryogenic fuel tank. This model employs the full set of Navier-Stokes equations, except that viscous dissipation is neglected in the energy equation. An explicit finite difference technique in two-dimensional generalized coordinates, approximated to second-order accuracy in both space and time is used. The stiffness resulting from the low Mach number is resolved by using artificial compressibility. The model simulates the transient, two-dimensional draining of a fuel tank cross section. To calculate the slosh wave dynamics the interface between the ullage gas and liquid fuel is modeled as a free surface. Then, experimental data for free convection inside a horizontal cylinder are compared with model results. Finally, cryogenic tank draining calculations are performed with three different wall heat fluxes to demonstrate the effect of wall heat flux on the internal tank flow field.

Greer, Donald↗

NASA NTRS2016

Impact of an Exhaust Throat on Semi-Idealized Rotating Detonation Engine Performance

A computational fluid dynamic (CFD) model of a rotating detonation engine (RDE) is used to examine the impact of an exhaust throat (i.e., a constriction) on performance. The model simulates an RDE which is premixed, adiabatic, inviscid, and which contains an inlet valve that prevents backflow from the high pressure region directly behind the rotating detonation. Performance is assessed in terms of ideal net specific impulse which is computed on the assumption of lossless expansion of the working fluid to the ambient pressure through a notional diverging nozzle section downstream of the throat. Such a semi-idealized analysis, while not real-world, allows the effect of the throat to be examined in isolation from, rather than coupled to (as it actually is) various loss mechanisms. For the single Mach 1.4 flight condition considered, it is found that the addition of a throat can yield a 9.4 percent increase in specific impulse. However, it is also found that when the exit throat restriction gets too small, an unstable type of operation ensues which eventually leads to the detonation failing. This behavior is found to be somewhat mitigated by the addition of an RDE inlet restriction across which there is an aerodynamic loss. Remarkably, this loss is overcome by the benefits of the further exhaust restrictions allowed. The end result is a configuration with a 10.3 percent improvement in ideal net specific thrust.

combustion↗

NASA NTRS2016

Impact of an Exhaust Throat on Semi-Idealized Rotating Detonation Engine Performance

A computational fluid dynamic (CFD) model of a rotating detonation engine (RDE) is used to examine the impact of an exhaust throat (i.e. a constriction) on performance. The model simulates an RDE which is premixed, adiabatic, inviscid, and which contains an inlet valve that prevents backflow from the high pressure region directly behind the rotating detonation. Performance is assessed in terms of ideal net specific impulse which is computed on the assumption of lossless expansion of the working fluid to the ambient pressure through a notional diverging nozzle section downstream of the throat. Such a semi-idealized analysis, while not real-world, allows the effect of the throat to be examined in isolation from, rather than coupled to (as it actually is) various loss mechanisms. For the single Mach 1.4 flight condition considered, it is found that the addition of a throat can yield a 9.4 percent increase in specific impulse. However, it is also found that when the exit throat restriction gets too small, an unstable type of operation ensues which eventually leads to the detonation failing. This behavior is found to be somewhat mitigated by the addition of an RDE inlet restriction across which there is an aerodynamic loss. Remarkably, this loss is overcome by the benefits of the further exhaust restrictions allowed. The end result is a configuration with a 10.3 percent improvement in ideal net specific thrust.

propulsion↗

NASA NTRS2002

A Comparative Study of Three Methodologies for Modeling Dynamic Stall

During the past two decades, there has been an increased reliance on the use of computational fluid dynamics methods for modeling rotors in high speed forward flight. Computational methods are being developed for modeling the shock induced loads on the advancing side, first-principles based modeling of the trailing wake evolution, and for retreating blade stall. The retreating blade dynamic stall problem has received particular attention, because the large variations in lift and pitching moments encountered in dynamic stall can lead to blade vibrations and pitch link fatigue. Restricting to aerodynamics, the numerical prediction of dynamic stall is still a complex and challenging CFD problem, that, even in two dimensions at low speed, gathers the major difficulties of aerodynamics, such as the grid resolution requirements for the viscous phenomena at leading-edge bubbles or in mixing-layers, the bias of the numerical viscosity, and the major difficulties of the physical modeling, such as the turbulence models, the transition models, whose both determinant influences, already present in static maximal-lift or stall computations, are emphasized by the dynamic aspect of the phenomena.

Sankar, L.↗

DOE OSTI2020

Efficient bi-directional coupling of 3D computational fluid-particle dynamics and 1D Multiple Path Particle Dosimetry lung models for multiscale modeling of aerosol dosimetry

The development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. One-dimensional (1D) models successfully predict overall deposition averages but fail to accurately predict local deposition. Computational fluid-particle dynamics (CFPD) models provide site-specific predictions but at a computational cost that prohibits whole lung predictions. Thus, there is a need for developing multiscale strategies to provide a realistic subject-specific picture of the fate of inhaled aerosol in the lungs. CT-based 3D/CFPD models of the large airways were bidirectionally coupled with individualized 1D Navier-Stokes airflow and particle transport based upon the widely used Multiple Path Particle Dosimetry Model (MPPD). Distribution of airflows among lobes was adjusted by measured lobar volume changes observed in CT images between FRC and FRC + 1.5 L. Additionally, as a test of the effectiveness of the coupling procedures, deposition modeling of previous 1 µm aerosol exposure studies was performed. The complete coupled model was run for 3 breaths, with the computation-intense portion being the 3D CFD Lagrangian particle tracking calculation. The average deposition per breath was 11% in the combined multiscale model with site-specific doses available in the CFPD portion of the model and airway- or region-specific deposition available for the MPPD portion. In conclusion, the key methods developed in this study enable predictions of ventilation heterogeneities and aerosol deposition across the lungs that are not captured by 3D or 1D models alone. Overall, these methods can be used as the foundation for multi-scale modeling of the full respiratory system.

60 APPLIED LIFE SCIENCES↗

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 NTRS2012

Aerothermal Ground Testing of Flexible Thermal Protection Systems for Hypersonic Inflatable Aerodynamic Decelerators

Flexible TPS development involves ground testing and analysis necessary to characterize performance of the FTPS candidates prior to flight testing. This paper provides an overview of the analysis and ground testing efforts performed over the last year at the NASA Langley Research Center and in the Boeing Large-Core Arc Tunnel (LCAT). In the LCAT test series, material layups were subjected to aerothermal loads commensurate with peak re-entry conditions enveloping a range of HIAD mission trajectories. The FTPS layups were tested over a heat flux range from 20 to 50 W/cm with associated surface pressures of 3 to 8 kPa. To support the testing effort a significant redesign of the existing shear (wedge) model holder from previous testing efforts was undertaken to develop a new test technique for supporting and evaluating the FTPS in the high-temperature, arc jet flow. Since the FTPS test samples typically experience a geometry change during testing, computational fluid dynamic (CFD) models of the arc jet flow field and test model were developed to support the testing effort. The CFD results were used to help determine the test conditions experienced by the test samples as the surface geometry changes. This paper includes an overview of the Boeing LCAT facility, the general approach for testing FTPS, CFD analysis methodology and results, model holder design and test methodology, and selected thermal results of several FTPS layups.

Bruce, Walter E., III↗