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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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NASA NTRS1992

HSR combustion analytical research

Increasing the pressure and temperature of the engines of a new generation of supersonic airliners increases the emissions of nitrogen oxides (NO(x)) to a level that would have an adverse impact on the Earth's protective ozone layer. In the process of evolving and implementing low emissions combustor technologies, NASA LeRC has pursued a combustion analysis code program to guide combustor design processes, to identify potential concepts of the greatest promise, and to optimize them at low cost, with short turnaround time. The computational analyses are evaluated at actual engine operating conditions. The approach is to upgrade and apply advanced computer programs for gas turbine applications. Efforts were made in further improving the code capabilities for modeling the physics and the numerical methods of solution. Then test cases and measurements from experiments are used for code validation.

Nguyen, H. Lee↗

NASA NTRS1997

A DSMC Study of Low Pressure Argon Discharge

Work toward a self-consistent plasma simulation using the DSMC (Direct Simulation Monte Carlo) method for examination of the flowfields of low-pressure high density plasma reactors is presented. Presently, DSMC simulations for these applications involve either treating the electrons as a fluid or imposing experimentally determined values for the electron number density profile. In either approach, the electrons themselves are not physically simulated. Self-consistent plasma DSMC simulations have been conducted for aerospace applications but at a severe computational cost due in part to the scalar architectures on which the codes were employed. The present work attempts to conduct such simulations at a more reasonable cost using a plasma version of the object-oriented parallel Cornell DSMC code, MONACO, on an IBM SP-2. Due to availability of experimental data, the GEC reference cell is chosen to conduct preliminary investigations. An argon discharge is chosen to conduct preliminary investigations. An argon discharge is examined thus affording a simple chemistry set with eight gas-phase reactions and five species: Ar, Ar(+), Ar(*), Ar(sub 2), and e where Ar(*) is a metastable.

Hash, David B.↗

NASA NTRS2005

Highly Reliable, High-Speed, Unidirectional Serial Data Links

Highly reliable, high-speed, unidirectional serial data-communication subsystems have been proposed to be installed in an upgrade of the computing systems aboard the space shuttles. The basic design concept of these serial data links is also adaptable to terrestrial use in applications in which there are requirements for highly reliable serial data communications. The hardware and software aspects of the architecture of the data links are dictated largely by a requirement, in the original space-shuttle application, for one computer to monitor the memory transactions and memory contents of other computers in real time with high reliability and without reliance on requests for retransmission. To minimize weight while affording a capability to transfer data at a required rate of 2.56 x 10(exp 8) bits per second, it was decided that the links would be serial ones of the fiber-channel type. [Fiber channel denotes a type of serial computer bus that is used to connect a computer (usually a supercomputer) with a high-speed data storage device. Depending on the specific application, the physical connection between the transmitter and receiver could be made via an optical fiber or a twisted pair of wires.] Heretofore, fiber-channel links have ordinarily been bidirectional and have operated under protocols that provide for receiving stations to detect errors and request retransmission when necessary. In the present case, the time taken by processing to request retransmission would conflict with the requirement for real-time transfer of data. To ensure reliability without retransmission, a link according to the proposal would utilize a modified version of the normal fiberchannel character set in conjunction with forward error correction by means of a Reed-Solomon code (see figure). The Reed-Solomon encoding and decoding and the translations between the normal and modified character sets would be effected by logic circuitry external to the fiber-channel transmitter and receiver, which would be commercial off-the-shelf units. The receiving end of the link could detect and correct errors at a rate as high as 4 million times per second, if necessary. The receiver detects uncorrectable double-byte errors. It has been estimated that uncorrectable-error rate would amount to one failure in about 10(exp 19) characters.

Cole, Robert M.↗

NASA NTRSDate not supplied

“Cold-Flow” Experiments Supporting CFD of Mixing Flowfields for High-Speed Fuel Injectors for Scramjet Applications

Recent flight demonstrations of supersonic combustion ramjet (scramjet) vehicles prove their increasing promise for military (rapid response and strike capability on global scale), aerospace (safer and more affordable access to space), and civil aviation (hypersonic point-to-point transport) applications. Currently, these technologies are still in their early development stages with commercial interest and investment at only a fraction of that of government organizations such as NASA and DoD. To advance hypersonic air-breathing propulsion technologies to the technology readiness levels necessary for access to space or widespread commercialization, further government investments and university engagement are needed over the next decade and likely beyond. This is because designing scramjet propulsion devices capable of robust high-speed air-breathing operation, characterized by rapid fuel and air mixing, short combustion times, and ensuring stable flame, has proven difficult. In this presentation, after a brief engineering-level introduction to scramjets technology, we will discuss one of the key challenges in high-speed propulsion design, namely the fuel injector design. Attempts at improving fuel injection to enhance fuel-air mixing while simultaneously reducing engine thrust losses have received a great deal of attention over the years. Although some amount of loss is thermodynamically unavoidable and occurs due to the desired effect of molecular mixing of fuel and air, any losses beyond this minimum required amount reduces the thrust potential of the engine. The Enhanced Injection and Mixing Project (EIMP) at NASA Langley Research Center aims at addressing this design challenge by analyzing the performance of a number of baseline and novel fuel injectors for high-speed applications. The project leverages computational and experimental capabilities with the goal to investigate scramjet fuel injection and mixing physics, improve our understanding of the underlying physical process, and develop enhancement strategies relevant to hypersonic flight Mach numbers. The talk will discuss the current computational and experimental research approaches using one of the baseline injectors considered by the EIMP as an example.

Scramjet↗

DOE OSTI2024

Scaling and performance portability of the particle-in-cell scheme for plasma physics applications through mini-apps targeting exascale architectures

We perform a scaling and performance portability study of the particle-in-cell scheme for plasma physics applications through a set of mini-apps we name "Alpine", which can make use of exascale computing capabilities. The mini-apps are based on Independent Parallel Particle Layer, a framework that is designed around performance portable and dimension independent particles and fields. We benchmark the simulations with varying parameters such as grid resolutions (5123 to 20483) and number of simulation particles (109 to 1011) with the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter. While the scaling and portability study helps identify the performance critical components of the particle-in-cell scheme in the current state-of-the-art computing architectures, the mini-apps by themselves can be used to develop new algorithms and optimize their high performance implementations targeting exascale architectures.

Muralikrishnan, Sriramkrishnan↗

NASA NTRS1990

The application of CFD to rotary wing flow problems

Rotorcraft aerodynamics is especially rich in unsolved problems, and for this reason the need for independent computational and experimental studies is great. Three-dimensional unsteady, nonlinear potential methods are becoming fast enough to enable their use in parametric design studies. At present, combined CAMRAD/FPR analyses for a complete trimmed rotor soltution can be performed in about an hour on a CRAY Y-MP (or ten minutes, with multiple processors). These computational speeds indicate that in the near future many of the large CFD problems will no longer require a supercomputer. The ability to convect circulation is routine for integral methods, but only recently was it discovered how to do the same with differential methods. It is clear that the differential CFD rotor analyses are poised to enter the engineering workplace. Integral methods already constitute a mainstay. Ultimately, it is the users who will integrate CFD into the entire engineering process and provide a new measure of confidence in design and analysis. It should be recognized that the above classes of analyses do not include several major limiting phenomena which will continue to require empirical treatment because of computational time constraints and limited physical understanding. Such empirical treatment should be included, however, into the developing CFD, engineering level analyses. It is likely that properly constructed flow models containing corrections from physical testing will be able to fill in unavoidable gaps in the experimental data base, both for basic studies and for specific configuration testing. For these kinds of applications, computational cost is not an issue. Finally, it should be recognized that although rotorcraft are probably the most complex of aircraft, the rotorcraft engineering community is very small compared to the fixed-wing community. Likewise, rotorcraft CFD resources can never achieve fixed-wing proportions and must be used wisely. Therefore the fixed-wing work must be gleaned for many of the basic methods.

Caradonna, F. X.↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

DOE OSTI2025

Storage-Induced Collapse of Lignin Macromolecular Structure and Its Impacts on the Biorefinery

Lignin plays a vital role in the economics of biorefineries, serving as a source of process energy and a feedstock for sustainable fuels and chemical production. While understanding lignin’s chemical composition is crucial, emerging evidence suggests that a more comprehensive understanding of its macromolecular structure is critical to explaining its complex behavior in the biorefinery. This study investigated the partial collapse of the lignin network in corn stover feedstock after harvest and storage as a result of the microbial digestion of hemicellulose. Fluorescence microscopy was used to detect the collapse of lignin in terms of lignin’s inter-molecular interaction and the re-orientation of lignin’s chromophores, by the changes in lignin’s fluorescence lifetime, anisotropy, and the number of effective emitters. With minimal sample perturbation, our in-situ microscopic results revealed lignin's coil-globule transition phenomena, which was only previously predicted by molecular dynamics modeling extracted lignin in solvent. This collapse of lignin macromolecular structure was confirmed by results from NMR, IR, Raman, and powder X-ray diffraction. We also investigated the impact of this storage-induced collapse on the downstream biorefinery processes. Our study revealed that the two major approaches for lignin valorization in the lignin-first biorefinery model, namely monomer extraction and milled wood lignin extraction, were negatively impacted by the lignin collapse. As changes during storage are a source of feedstock variability, our study highlights the importance of understanding the effect of feedstock handling on biorefinery operations and economics.

09 BIOMASS FUELS↗

NASA NTRS1992

Multidisciplinary computational aerosciences

As the challenges of single disciplinary computational physics are met, such as computational fluid dynamics, computational structural mechanics, computational propulsion, computational aeroacoustics, computational electromagnetics, etc., scientists have begun investigating the combination of these single disciplines into what is being called multidisciplinary computational aerosciences (MCAS). The combination of several disciplines not only offers simulation realism but also formidable computational challenges. The solution of such problems will require computers orders of magnitude larger than those currently available. Such computer power can only be supplied by massively parallel machines because of the current speed-of-light limitation of conventional serial systems. Even with such machines, MCAS problems will require hundreds of hours for their solution. To efficiently utilize such a machine, research is required in three areas that include parallel architectures, systems software, and applications software. The main emphasis of this paper is the applications software element. Examples that demonstrate application software for multidisciplinary problems currently being solved at NASA Ames Research Center are presented. Pacing items for MCAS are discussed such as solution methodology, physical modeling, computer power, and multidisciplinary validation experiments.

Kutler, Paul↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

LAN: A Materials Notation for Two-Dimensional Layered Assemblies

Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation, and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not yet fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with several example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture, and transfer of materials knowledge in the area of 2D layered materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2019

Cluster Analysis of Spectroscopic Line Profiles and EUV Emission in RMHD Simulations and Observations of the Solar Atmosphere

Spatially-resolved observations from the IRIS, SDO/AIA, and other space mission and ground-based telescopes, coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar atmosphere. To better understand the dynamical and thermodynamic properties in the simulation data and their connection to observations, it is essential to determine similarities in the behaviors of the synthesized and observed emission. However, the complexity of observational data and physical processes makes comparison of observations and modeling results difficult. In this work, we show the initial results of application of K-Means clustering (unsupervised machine learning) algorithm to two different problems: 1) recognition of the typical spectroscopic line profiles observed by IRIS during solar flares and their typical dynamic behavior; 2) recognition of shocks and heating events in synthetic AIA emission data obtained from StellarBox quiet-Sun simulations. The average silhouette width technique for the KMeans algorithm is utilized in different ways to obtain optimal numbers of clusters. We discuss application of the emission clustering to visualizations of the computational volume, understanding its evolutionary trends and behavior patterns, and inversion (reconstruction) of physical properties of the solar atmosphere from synthesizes emission data.

Sadykov, Viacheslav↗

DOE OSTI2022

Anti-$\mathscr{PT}$-symmetric qubit: Decoherence and entanglement entropy

We investigate the dynamics of a general two-level based anti-parity-time (anti-$\mathscr{PT}$)-symmetric qubit and study its decoherence as well as entanglement entropy properties. We compare our findings with that of the corresponding parity-time ($\mathscr{PT}$)-symmetric and Hermitian qubits. To begin, we consider the time-dependent Dyson map to find the exact analytical dynamics for a general non-Hermitian qubit system weakly coupled with a thermal bath for pure dephasing, before specializing it to the case of a general anti-$\mathscr{PT}$-symmetric qubit. Basing the comparison under the same coupling strength or increasing the non-Hermiticity, we observe that the decoherence function and entanglement entropy of the anti-$\mathscr{PT}$-symmetric qubit decays and grows more slowly, respectively, compared to the $\mathscr{PT}$-symmetric and Hermitian qubits. Similarly, the corresponding variance and area of Fisher information are much higher compared to the $\mathscr{PT}$-symmetric and Hermitian qubits. These results demonstrate that anti-$\mathscr{PT}$-symmetric qubits may be better suited for quantum computing and quantum information processing applications than conventional Hermitian or even $\mathscr{PT}$-symmetric qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

WarpX: Delivering Unprecedented Particle-in-Cell Simulation Capability

Since 1939, more than 30 percent of all Nobel prizes in physics and four of the past 14 prizes in chemistry have been awarded for work with particle accelerators. These tools are now used to treat cancer and produce medical supplies, support research in applied fields such as pharmaceuticals, create microcircuits, and sterilize food—and the applications list continues to grow. However, the size of the facilities needed to house these machines—which are often dozens of kilometers long—has limited their future potential. Thus, researchers are motivated to create novel particle accelerator designs that provide new imaging capability but have a much smaller footprint. The Exascale Computing Project’s WarpX application captures the full complexity of the acceleration processes within plasma-based design, supporting the rapid and cost-efficient development of tens of thousands of new particle accelerators for various applications from improving human health to nuclear fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Solving reaction dynamics with quantum computing algorithms

The description of quantum many-body dynamics is extremely challenging on classical computers, as it can involve many degrees of freedom. However, the time evolution of quantum states is a natural application for quantum computers that are designed to efficiently perform unitary transformations. Here, in this paper, we study quantum algorithms for response functions, relevant for describing different reactions governed by linear response. We focus on nuclear-physics applications and consider a qubit-efficient mapping on the lattice, which can efficiently represent the large volumes required for realistic scattering simulations. For the case of a contact interaction, we develop an algorithm for time evolution based on the Trotter approximation that scales logarithmically with the lattice size and is combined with quantum phase estimation. We eventually focus on the nuclear two-body system and a typical response function relevant for electron scattering as an example. We also investigate ground-state preparation and examine the total circuit depth required for a realistic calculation and the hardware noise level required to interpret the signal.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2002

Photoionization and High Density Gas

We present results of calculations using the XSTAR version 2 computer code. This code is loosely based on the XSTAR v.1 code which has been available for public use for some time. However it represents an improvement and update in several major respects, including atomic data, code structure, user interface, and improved physical description of ionization/excitation. In particular, it now is applicable to high density situations in which significant excited atomic level populations are likely to occur. We describe the computational techniques and assumptions, and present sample runs with particular emphasis on high density situations.

Kallman, T.↗

DOE OSTI2023

Classroom aerosol dispersion modeling: experimental assessment of a low-cost flow simulation tool

The purpose of this study was to assess the utility of a low-cost flow simulation tool for an indoor air modeling application by comparing its outputs with the results of a physical experiment, as well as those from a more advanced computational fluid dynamics (CFD) software package. In this study, five aerosol dispersion tests were performed in two different classrooms by releasing a CO 2 tracer gas from six student locations. Resultant steady-state concentrations were monitored at 13 locations around the periphery of the room. Subsequently, the experiments were modeled using both a low-cost tool (SolidWorks Flow Simulation) and a more sophisticated tool (STAR-CCM+). Models were evaluated based on their ability to predict the experimentally measured concentrations at the 13 monitoring locations by calculating four performance parameters commonly used in the evaluation of dispersion models: fractional mean bias (FB), normalized mean-square error (NMSE), fraction of predicted value within a factor of two (FAC2), and normalized absolute difference (NAD). The more sophisticated model performed better in 15 of the 20 possible cases (five tests at four parameters each), with parameters meeting acceptance criteria in 19 of 20 cases. However, the lower-cost tool was only slightly worse, with parameters meeting acceptance criteria in 18 of 20 cases, and it performed better than the other tool in 3 of 20 cases. Because it provides useful results at a fraction of the monetary and training cost and is already widely accessible to many institutions, such a tool may be worthwhile for many indoor aerosol dispersion applications, especially for students or researchers just beginning CFD modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as Internet of Things (IoT), edge computing, and usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN’s (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optics. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded to fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. In conclusion, the development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

97 MATHEMATICS AND COMPUTING↗