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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

MFLOP to GFLOP: The Impact on High Fidelity Based Computational Aeroelasticity

Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft which can experience complex flow/structure interactions. Even at moderate angles of attack supersonic aircraft can experience vortex induced aeroelastic oscillations. Near transonic speeds buffet associated structural oscillations are possible. Aircraft flying in transonic regime may experience a dip in the flutter speed. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional supercomputers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overco me the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations. Modifications required in both fluids and structural solvers in order to run efficiently on parallel computers will be discussed. Implementation of moving grids and fluid/structural interface on parallel computers will be discussed.

Guruswamy, Guru P.↗
NASA NTRS1994

Fluid/Structure Interaction Studies of Aircraft Using High Fidelity Equations on Parallel Computers

Abstract Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional super computers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

Guruswamy, Guru↗
DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗
NASA NTRS1994

Progress in Computational Aeroelasticity Using High Fidelity Flow and Structural Equations on Parallel Computers

Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional supercomputers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENASERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

Guruswamy, Guru P.↗
DOE OSTI2024

Validation Exercise of a Coarse Finite Element Model of Laser Welds

The objective of this project is to validate low-fidelity models of 304L to 304L stainless steel partial-penetration laser welds for thin sheets. Low-fidelity means that the weld is represented by coarsely meshed element blocks. Here, the hexahedral element size is approx imately half the weld penetration depth. The material behavior of the block is represented by a J2 plasticity model with a Voce hardening function. The source of the data used in this work is an extensive experimental study conducted by Sharlotte Kramer (1528) and published in 2015. Figure 1 shows a cross-section of the weld of interest. The nominal thickness of the sheets is 0.063 in. while the target penetration depth of the weld is in the range of 0.028 to 0.032 in., extending about half the sheet thickness. Uniaxial tension tests provided data for calibration of base material and weld models. Results of two validation geometries were also provided. The principal validation geometry is shown in Fig. 2. It consists of a plate specimen with in-plane dimensions 6 in × 2.875 in loaded in tension. A circular plug with a 1.5 in. diameter was cut from the center of the plate and then welded in place. The details of the welding schedule are given. An important assumption is that the welds in the calibration and validation specimens have similar geometric and material properties as those in the validation tests. The task was to first calibrate models for the base material and the welds and then simulate the validation tests until the point of weld first failure.

36 MATERIALS SCIENCE↗
NASA NTRS2013

The Simplified Aircraft-Based Paired Approach With the ALAS Alerting Algorithm

This paper presents the results of an investigation of a proposed concept for closely spaced parallel runways called the Simplified Aircraft-based Paired Approach (SAPA). This procedure depends upon a new alerting algorithm called the Adjacent Landing Alerting System (ALAS). This study used both low fidelity and high fidelity simulations to validate the SAPA procedure and test the performance of the new alerting algorithm. The low fidelity simulation enabled a determination of minimum approach distance for the worst case over millions of scenarios. The high fidelity simulation enabled an accurate determination of timings and minimum approach distance in the presence of realistic trajectories, communication latencies, and total system error for 108 test cases. The SAPA procedure and the ALAS alerting algorithm were applied to the 750-ft parallel spacing (e.g., SFO 28L/28R) approach problem. With the SAPA procedure as defined in this paper, this study concludes that a 750-ft application does not appear to be feasible, but preliminary results for 1000-ft parallel runways look promising.

Perry, Raleigh B.↗
DOE OSTI2026

High-Fidelity, Low-Dissipation/Symmetry-Preserving Numerical Scheme for Solving the Euler Equations with Unstructured, Metric-Based Mesh Adaptation

This work presents an overview of a high-fidelity compressible Euler solver that utilizes the continuous Galerkin (CG) method with added artificial numerical diffusion for stabilization to solve a variety of unsteady and steady benchmark inviscid flow problems. This work shows that discretizing the Euler equations with this CG approach and first order basis functions produces a cost-effective stencil as well as simple well-posed boundary conditions. We show through convergence testing with manufactured solutions that the reduced stencil of CG, combined with the low amount of artificial diffusion required when using the stabilization method outlined in this work, leads to stable and highly accurate results for a variety of unsteady and steady applications. When combined with the adaptive mesh refinement approach used for many of the cases in this work, our results show that the flow solver achieves even more accurate results. A variety of inviscid flow cases are presented in this work, including transient 2D cases with complex shock structures and several steady 3D airfoils sections with a constant span.

Doetsch, Kevin [ORNL] (ORCID:0000000267051705)↗
NASA NTRS2004

Validation of NASCAP-2K Spacecraft-Environment Interactions Calculations

The recently released Nascap-2k, version 2.0, three-dimensional computer code models interactions between spacecraft surfaces and low-earth-orbit, geosynchronous, auroral, and interplanetary plasma environments. It replaces the earlier three-dimensional spacecraft interactions codes NASCAP/GEO, NASCAP/LEO, POLAR, and DynaPAC. Nascap-2k has improved numeric techniques, a modern user interface, and a simple, interactive satellite surface definition module (Object ToolKit). We establish the accuracy of Nascap-2k both by comparing computed currents and potentials with analytic results and by comparing Nascap-2k results with published calculations using the earlier codes. Nascap-2k predicts Langmuir-Blodgett or Parker-Murphy current collection for a nearly spherical (100 surfaces) satellite in a short Debye length plasma depending on the absence or presence of a magnetic field. A low fidelity (in geometry and time) Nascap-2k geosynchronous charging calculation gives the same results as the corresponding low fidelity NASCAP/GEO calculation. A high fidelity calculation (using the Nascap-2k improved geometry and time stepping capabilities) gives higher potentials, which are more consistent with typical observations. Nascap-2k predicts the same current as a function of applied potential as was observed and calculated by NASCAP/LEO for the SPEAR I rocket with a bipolar sheath. A Nascap-2k DMSP charging calculation gives results similar to those obtained using POLAR and consistent with observation.

Davis, V. A.↗
DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium opacity data. These data, however, are expensive to produce at relatively low fidelity. It is even more so at high fidelity such that the opacity calculations can contribute 95 % of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative per cent error of the peak radiative temperature of the hohlraum of approximately 1 % to 4 % while achieving a 19.4 $\times$ speed up.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Configuration

Model validation is the process of determining the degree of accuracy between the real world and the model. The result of model validation can be used to either improve the model through calibration or quantify the model-form uncertainty. This work focuses on estimating the model-form uncertainty through an area metric for a hypersonic cone-slice-flap geometry configuration given the uncertainty in both the simulation and experimental data. A procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs is outlined. The work also assesses the impact of using different high-fidelity solvers and different turbulence models. The goal of performing this comparison is to provide a quantifiable measurement of the accuracy of each solver and turbulence model for this type of design. A low-fidelity analysis is also performed to get a model-form uncertainty for this type of analysis. The two high-fidelity CFD solvers used here are VULCAN-CFD and FUN3D, and the low-fidelity results comes from Cart3D.The experimental data comes from the 20-inch Mach 6 Tunnel located at NASA Langley Research Center. The work here shows that the models used tend to under-predict the drag and moment aerodynamic coefficients for this type of design leading to a model-form uncertainty estimate which can account for up to an 86% of the total uncertainty in the model predictions.

Laura M White↗
NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Configuration

Model validation is the process of determining the degree of accuracy between the real world and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap geometry configuration given uncertainty in both the simulation and experimental data. A procedure will be presented that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs. The work will also assess the impact between using different high-fidelity solvers and different turbulence models.The goal of performing this comparison is to provide a quantifiable measurement for the accuracy of each solver and turbulence model for this type of design. A low-fidelity analysis will also be performed to get a model-form uncertainty for the lower fidelity analysis. The two high-fidelty CFD solvers used are VULCAN-CFD and FUN3D and the low-fidelity results will come from Cart3D. The experimental data came from the 20-inch Mach 6Tunnel located at NASA Langley Research Center.

Laura M. White↗
NASA NTRS2023

Analytical Design and Performance Estimation Methods for Aircraft Permanent Magnet Synchronous Machines

The design of an electric motor drivetrain is a complex multiphysics problem. Low fidelity motor drivetrain sizing can be a key tool in the design cycle of an electric motor drivetrain and for system level studies of aircraft configurations. However, low fidelity sizing can lead to misleading results if all the physics involved in a motor design are not properly accounted for. This paper provides details on modeling approaches for initial design and sizing of permanent magnet synchronous electric machines. The goal of this paper is to provide the reader an understanding of the key principles of motor design and some modeling approaches to perform initial sizing of an electric motor and its inverter.

Thomas F. Tallerico↗
NASA NTRSDate not supplied

Uncertainty Reduction With Multi-Model Monte Carlo for Crystal Plasticity Simulations of Additively Manufactured Metals

In this work, multi-model Monte Carlo estimators are developed to reduce uncertainty in quantities of interest (QoIs) extracted from crystal plasticity simulations of additively manufactured (AM) metals. A significant concern in AM parts is uncertainty in mechanical properties caused in part by complex microstructures that arise from the AM process. Quantifying uncertainty in microstructure-sensitive behavior using experiments alone is costly, especially when mechanical allowables must be established. Quantitative relationships among microstructure, micromechanical metrics like slip accumulation, crack initiation, and failure are also difficult to capture with limited experiments. Crystal plasticity material models instead enable computational prediction of micromechanical stress and strain fields given a discretized microstructure. However, high-fidelity finely discretized crystal plasticity simulations are computationally expensive, while lower-fidelity models are less accurate and generally biased, making uncertainty quantification and reduction computationally difficult as well. Multi-model Monte Carlo methods leverage correlations between high- and low-fidelity models to produce unbiased estimators for QoIs with reduced uncertainty relative to standard Monte Carlo. Crystal plasticity QoIs considered in this work include yield strength and the mean and extreme values of micromechanical fields that are relevant to crack initiation. Multi-model Monte Carlo estimators are developed for each individual QoI and several groups of QoIs. The results of this work establish relationships among model correlations, sample allocation, and uncertainty reduction for different combinations of QoIs and demonstrate a trend of less uncertainty reduction as QoIs become more sensitive to local microstructure. Limitations from using pilot samples to estimate model covariances and train low-fidelity models are also addressed. The uncertainty reduction achieved by multi-model Monte Carlo is an important step toward using computational mechanics models to predict microstructure-sensitive crack initiation and failure in AM parts.

uncertainty quantification↗
DOE OSTI2023

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Recent advances in modeling large-scale, complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. Here in this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and a low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.

MATHEMATICS AND COMPUTING↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
DOE OSTI2021

Multi-fidelity kinetic theory-based approach for the prediction of particle attrition: Application to jet cup attrition system

The timescale difference between the “fast” flow dynamics of fluidized bed reactors and the relatively “slow” rate of particle degradation makes the direct computational prediction of attrition challenging. An approach to this challenge is a multi-fidelity strategy where a high fidelity model for the flow dynamics is coupled with a lower fidelity model for the long-time resolution of the bulk attrition of the reactor inventory. We implement this approach using high-fidelity kinetic theory simulations to calculate the flow dynamics which are post-processed to calculate the frequency and intensity of the particle-particle and particle-wall collisions (e.g. collision energy spectra). This is combined with the particle breakage properties to construct the coefficients for a low-fidelity model [e.g. Monazam et al., 2018, Powder Technology 340, p. 528-536]. Simulations are performed of a jet cup attrition system containing Canadian hematite (Monazam et al. 2018). Furthermore, these are first analyzed using the collision energy spectra. Quantitative predictions of the mass loss are made using a low-fidelity model derived from the collision-spectra and a calibrated material breakage coefficient. The results are found to compare favorably with the experimental measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2013

GFEChutes Lo-Fi

NASA needed to provide a software model of a parachute system for a manned re-entry vehicle. NASA has parachute codes, e.g., the Descent Simulation System (DSS), that date back to the Apollo Program. Since the space shuttle did not rely on parachutes as its primary descent control mechanism, DSS has not been maintained or incorporated into modern simulation architectures such as Osiris and Antares, which are used for new mission simulations. GFEChutes Lo-Fi is an object-oriented implementation of conventional parachute codes designed for use in modern simulation environments. The GFE (Government Furnished Equipment), low-fidelity (Lo-Fi) parachute model (GFEChutes Lo-Fi) is a software package capable of modeling the effects of multiple parachutes, deployed concurrently and/or sequentially, on a vehicle during the subsonic phase of reentry into planetary atmosphere. The term "low-fidelity" distinguishes models that represent the parachutes as simple forces acting on the vehicle, as opposed to independent aerodynamic bodies. GFEChutes Lo-Fi was created from these existing models to be clean, modular, certified as NASA Class C software, and portable, or "plug and play." The GFE Lo-Fi Chutes Model provides basic modeling capability of a sequential series of parachute activities. Actions include deploying the parachute, changing the reefing on the parachute, and cutting away the parachute. Multiple chutes can be deployed at any given time, but all chutes in that case are assumed to behave as individually isolated chutes; there is no modeling of any interactions between deployed chutes. Drag characteristics of a deployed chute are based on a coefficient of drag, the face area of the chute, and the local dynamic pressure only. The orientation of the chute is approximately modeled for purposes of obtaining torques on the vehicle, but the dynamic state of the chute as a separate entity is not integrated - the treatment is simply an approximation. The innovation in GFEChutes Lo-Fi is to use an object design that closely followed the mechanical characteristics and structure of a physical system of parachutes and their deployment mechanisms. Software objects represent the components of the system, and use of an object hierarchy allows a progression from general component outlines to specific implementations. These extra chutes were not part of the baseline deceleration sequence of drogues and mains, but still had to be simulated. The major innovation in GFEChutes Lo-Fi is the software design and architecture.

Gist, Emily↗