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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling and simulations of hydrodynamic shocks in a plasma flowing across randomized ICF scale laser beams

High-energy laser beams interacting with flowing plasmas can produce a plasma response that leads to deflection of the beam, beam bending. Such beams have usually a speckle structure generated by optical smoothing techniques that reduce the spatial and temporal coherence in the laser field pattern. The cumulative plasma response from laser speckles slows down the velocity of the incoming flow by momentum conservation. For slightly super-sonic flow the cumulative plasma response to the ponderomotive force exerted by the beam speckle ensemble is the strongest, such that slowing down the flow to subsonic velocities leads eventually to the generation of a shock around the cross section of the beam. This scenario has been predicted theoretically and is confirmed here by our hydrodynamic simulations in two dimensions with speckled beams and in one dimension with a reduced model. The conditions of shock generation are given in terms of the ponderomotive pressure, speckle size and the flow velocity. The nonlinear properties of the shocks are analyzed using Rankine–Hugoniot relations. According to linear theory, temporally smoothed laser beams exhibit a higher threshold for shock generation. Numerical simulations with beams that are smoothed by spectral dispersion compare well with the linear theory results, diverging from those produced by beams with only a random phase plates in the nonlinear regime. The conditions necessary for shock generation and their effects on the laser plasma coupling in inertial confinement fusion (ICF) experiments are also discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Resistive hose modes in tokamak runaway electron beams

Beams of energetic runaway electrons are generated during disruptions in tokamaks, and fluid models are used to study their effects on macroscale dynamics. Linear computations of a massless, runaway electron beam coupled to MHD plasma show that resistive hose instabilities grow faster than tearing modes at large resistivity. Eigenvalue results with reduced models of the resistive hose instability are compared with results from the full MHD and beam system, showing that the resistive hose decouples from any plasma response. An estimate of plasma temperature at which growth of the resistive hose dominates tearing for post-disruption DIII-D plasma parameters is in a physically relevant regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Evaluating the 3D PBL scheme for wind energy applications in the complex terrain of Altamont Pass, CA

In this study, the 3D PBL scheme, developed and tested during previous WETO projects, was used to simulate daily speedup flows in the Altamont Pass Wind Resource Area of California. These flows were observed as part of the WFIP2 companion experiment HilFlowS, which examined potential power output from taller turbines in the Altamont region. The regularity of the observed speedup events during the summer months, combined with the importance of terrain-induced wind acceleration, makes them a useful case study for evaluating mesoscale forecast models, which often underperform in regions of complex terrain. Compared to a traditional one-dimensional PBL scheme, the 3D PBL scheme shows evidence of improved wind speed predictions, including an ability to capture atypical wind speed profiles and reduce model overestimates of wind speed across the turbine rotor region. Such improvements will be necessary for wind farm siting and power forecasting, especially as taller turbines are deployed in complex terrain.

17 WIND ENERGY↗
DOE OSTI2026

Combined Nitrogen and Phosphorous Recovery via Electrochemical Technology Integration into Municipal Wastewater Treatment Plants

This project developed electroN-P, an electrochemical technology for recovering nitrogen and phosphorus as a fertilizer product from anaerobic digester centrate, targeting an advancement from TRL 4 to 6. The reactor was scaled 220× from a 250 mL batch cell to a continuous 4-channel system treating 55 L of wastewater, recovering >80% of phosphorus. Energy consumption under constant-voltage operation was lower than the embedded energy of conventional fertilizers, while constant-current operation produced a cost-competitive product with a smaller reactor footprint. TEA and LCA outcomes were highly sensitive to the magnesium source; alternative magnesium salt configurations projected reduced costs and embedded energy compared to sacrificial rods, but at the expense of significantly longer operation times. The recovered fertilizer performed comparably to diammonium phosphate and triple superphosphate in soil and plant trials. Integrating the technology into a whole-plant model reduced aeration energy by nearly 50%, lowering the levelized cost of water treatment from $0.668/m 3 to $0.648/m 3 . Results support targeting commercialization at smaller (≤1 MGD) facilities with high-strength digester streams. Further investigation into the transport and corrosion kinetics governing sacrificial anode wear is recommended to support continuous, longer-term operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1982

The residue-measure criterion for model reduction in the analysis of the NASA Space Shuttle's digital flight control system

A residue-measure criterion model reduction technique is applied to the vehicle dynamics model used in the design and analysis of the NASA Space Shuttle's digital flight control system. As implemented in this study the residue-measure technique involved an a priori residue calculation with control system biasing. The predictions of the reduced model are compared to vehicle level dynamic stability test data. These comparisons show an excellent correlation of the dominant spectral and response features between the model and test data. In addition, the application of the reduction technique to various Shuttle mission flight phases is demonstrated.

Gluch, D. P.↗
NASA NTRS1991

A Markov model reduction technique for fault tolerant processor reliability analysis

A fault tolerant processor (FTP) plays a key role in many high performance, safety-critical control system applications. Realistic modeling of an FTP is crucial to gaining a high degree of confidence in the reliability and safety analysis of such a system. While fidelity is clearly a major consideration, a practical model must also be kept to a moderate size to allow its incorporation into the overall system model. This paper presents a systematic reduction technique that starts from a complex, detailed model of a triple, redundant FTP and produces a low order approximation of very high accuracy. The existence of two distinct time scales represents the key to the success of the technique. No eigenvalue solution or coordinate transformation are needed. The reduced model captures all the important features of the detailed model, is amenable to an analytical solution and provides insight into the reconfiguration behavior of an FTP.

Schor, Andrei L.↗
DOE OSTI2023

Data-driven Whitney forms for structure-preserving control volume analysis

Control volume analysis models physics via the exchange of generalized fluxes between subdomains. Here, we introduce a scientific machine learning framework adopting a partition of unity architecture to identify physically-relevant control volumes, with generalized fluxes between subdomains encoded via Whitney forms. The approach provides a differentiable parameterization of geometry which may be trained in an end-to-end fashion to extract reduced models from full field data while exactly preserving physics. The architecture admits a data-driven finite element exterior calculus allowing discovery of mixed finite element spaces with closed form quadrature rules. An equivalence between Whitney forms and graph networks reveals that the geometric problem of control volume learning is equivalent to an unsupervised graph discovery problem. The framework is developed for manifolds in arbitrary dimension, with examples provided for H(div) problems in $\mathbb{R}$ establishing convergence and structure preservation properties. Finally, we consider a lithium-ion battery problem where we discover a reduced finite element space encoding transport pathways from high-fidelity microstructure resolved simulations. The approach reduces the 5.89M finite element simulation to 136 elements while reproducing pressure to under 0.1% error and preserving conservation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Implications of a Pervasive Climate Model Bias for Low‐Cloud Feedback

Abstract How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low‐cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low‐cloud feedback (0.45 W , 90% range 0.18–0.72 W ), about twice the mean value (0.22 W ) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean‐state bias: models underestimate the low‐cloud response to warming because (a) they systematically underestimate present‐day tropical marine low‐cloud amount, and (b) the low‐cloud sensitivity to warming is proportional to this present‐day low‐cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections.

58 GEOSCIENCES↗
DOE OSTI2024

Development of a neural network model for peeling–ballooning stability analysis in the KSTAR tokamak pedestals

The neural network model, MISHKA-NN is developed to mitigate the computational burden associated with the linear ideal magnetohydrodynamic (MHD) stability analysis of the pedestal based on the peeling–ballooning (P–B) model. By utilizing both 1D plasma profiles (current density, pressure gradient, and safety factor) and 0D parameters (plasma geometry, total current, and toroidal mode number), the model predicts linear growth rate of edge-localized ideal MHD instability in a given equilibrium state. By enabling the prediction of each instability within a second, the model reduces the time required for plotting a pedestal P–B stability diagram (j - α diagram) from approximately 100 CPU hours to a few CPU minutes. Notably, even with the utilization of parametric pressure and current profiles and plasma boundary shapes for the training dataset, the model shows a satisfactory level of performance in benchmarking the j - α diagram for the reconstructed equilibrium from a KSTAR tokamak experiment. We anticipate the model to serve as a versatile alternative to 2D linear MHD stability codes, alleviating numerical costs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2013

Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS

The Mars Exploration Rovers (MERs), Spirit and Opportunity, far exceeded their original drive distance expectations and have traveled, at the time of this reporting, a combined 29 kilometers across the surface of Mars. The Rover Sequencing and Visualization Program (RSVP), the current program used to plan drives for MERs, is only a kinematic simulator of rover movement. Therefore, rover response to various terrains and soil types cannot be modeled. Although sandbox experiments attempt to model rover-terrain interaction, these experiments are time-intensive and costly, and they cannot be used within the tactical timeline of rover driving. Imaging techniques and hazard avoidance features on MER help to prevent the rover from traveling over dangerous terrains, but mobility issues have shown that these methods are not always sufficient. ARTEMIS, a dynamic modeling tool for MER, allows planned drives to be simulated before commands are sent to the rover. The deformable soils component of this model allows rover-terrain interactions to be simulated to determine if a particular drive path would take the rover over terrain that would induce hazardous levels of slip or sink. When used in the rover drive planning process, dynamic modeling reduces the likelihood of future mobility issues because high-risk areas could be identified before drive commands are sent to the rover, and drives planned over these areas could be rerouted. The ARTEMIS software consists of several components. These include a preprocessor, Digital Elevation Models (DEMs), Adams rover model, wheel and soil parameter files, MSC Adams GUI (commercial), MSC Adams dynamics solver (commercial), terramechanics subroutines (FORTRAN), a contact detection engine, a soil modification engine, and output DEMs of deformed soil. The preprocessor is used to define the terrain (from a DEM) and define the soil parameters for the terrain file. The Adams rover model is placed in this terrain. Wheel and soil parameter files can be altered in the respective text files. The rover model and terrain are viewed in Adams View, the GUI for ARTEMIS. The Adams dynamics solver calls terramechanics subroutines in FORTRAN containing the Bekker-Wong equations.

Trease, Brian P.↗
NASA NTRS2009

Integrated Vehicle Ground Vibration Testing in Support of Launch Vehicle Loads and Controls Analysis

NASA has conducted dynamic tests on each major launch vehicle during the past 45 years. Each test provided invaluable data to correlate and correct analytical models. GVTs result in hardware changes to Saturn and Space Shuttle, ensuring crew and vehicle safety. Ares I IVGT will provide test data such as natural frequencies, mode shapes, and damping to support successful Ares I flights. Testing will support controls analysis by providing data to reduce model uncertainty. Value of testing proven by past launch vehicle successes and failures. Performing dynamic testing on Ares vehicles will provide confidence that the launch vehicles will be safe and successful in their missions.

Tuma, Margaret L.↗
DOE OSTI2025

DG2DAG: Learning Directed Acyclic Graphs from Functional Priors

Physics-based systems-of-systems models are computationally expensive. Reduced graphical models can decrease computational complexity, but may not proffer an end-to-end model from upstream inputs to downstream outputs. We consequently are interested in reducing models on directed graphs to models on a directed acyclic subgraph such that preserves accurate reconstruction of nodes. The consequence is a model with a topological ordering, providing a one-way flow of computation, and a causal interpr

Voronin, Alexey [Sandia National Laboratories (SNL↗
NASA NTRS2014

Twin Jet Effects on Noise of Round and Rectangular Jets: Experiment and Model

Many subsonic and supersonic aircraft concepts proposed by NASA's Fundamental Aeronautics Program have asymmetric, integrated propulsion systems. The asymmetries in the exhaust of these propulsion systems create an asymmetric acoustic field. The asymmetries investigated in the current study are from twin jets and rectangular nozzles. Each effect produces its own variation of the acoustic field. An empirical model was developed to predict the acoustic field variation from round twin jets with twin jet spacing from 2.6 to 5.6, where s is the center-to-center spacing over the jet diameter. The model includes parameters to account for the effects of twin jet spacing, jet static temperature ratio, flight Mach number, frequency, and observer angle (both polar and azimuthal angles). The model was then applied to twin 2:1 and 8:1 aspect ratio nozzles to determine the impact of jet aspect ratio. For the round and rectangular jets, the use of the model reduces the average magnitude of the error over all frequencies, observation angles, and jet spacings by approximately 0.5dB when compared against the assumption of adding two jets incoherently.

Noise↗
DOE OSTI2024

Membrane-based Carbon Capture Process Optimization using CFD Modeling

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibility and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability. To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity re-quires a multi-stage membrane process, where the optimal network configuration of the mem-brane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules.

Pedrozo, Hector A.↗
DOE OSTI2024

Membrane-based carbon capture process optimization using CFD modeling

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibility and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability. To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity requires a multi-stage membrane process, where the optimal network configuration of the membrane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules. Numerical results indicate that the optimal design includes three membrane stages, and the capture cost is 45.4 $/t-CO2.

Pedrozo, Hector A.↗
DOE OSTI2026

ab initio Sub-Mechanism Development for Cyclopentene Oxidation

To accurately predict low-temperature oxidation behavior, chemical kinetics mechanisms must contain complete reaction networks that include detailed consumption reactions of intermediates produced directly from hydroperoxyalkyl radicals, Q̇OOH, which undergo competing unimolecular reactions and bimolecular reactions with O2. Rates of chain-branching are governed by the flux between the two competing pathways, and inherently depend on temperature, pressure, and oxygen concentration. Neglect of consumption pathways for major oxidation intermediates leads to mechanism truncation error that is ameliorated by expanding the level of detail included in sub-mechanisms and employing ab initio methods for computing rates of elementary reactions and thermochemical properties of species involved. In the present work, an ab initio-derived sub-mechanism is developed using AutoMech to model the chemical kinetics of cyclopentene, a major product of cyclopentane oxidation. The ab initio sub-mechanism builds on a detailed mechanism developed using Reaction Mechanism Generator (RMG) for the specific purpose of determining the extent to which replacing cyclopentene-specific reactions and species with quantum chemical computations reduces model inaccuracies resulting from mechanism truncation error. In an effort to minimize interference from other reactions present during the formation of cyclopentene from cyclopentyl + O2, providing a narrower experimental scope, the model is compared against speciation measurements from jet-stirred reactor (JSR) experiments on cyclopentene oxidation. The experiments utilize vacuum ultraviolet-absorption spectroscopy and mass spectrometry for isomer-resolved speciation of intermediates at 835 Torr from 700 – 950 K. [O2]-dependent experiments were also conducted from 0.057 – 2.01 · 1018 molecules cm–3 at 825 K to examine the influence of oxygen on species profiles. Model predictions using the ab initio-revised mechanism yielded significant improvements in species profiles for both the temperature- and [O2]-dependent measurements, owing in part to increased rates of HOȮ and H2O2 production, which underscores the influence of theoretical calculations of reaction rates involving species produced from Ṙ + O2 such as cyclopentene.

AutoMech↗
DOE OSTI2021

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide

Understanding the structure and properties of refractory oxides is critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active learner, which is initialized by x-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. Furthermore, a multiphase potential is generated for a canonical example of the archetypal refractory oxide, HfO 2 , by drawing a minimum number of training configurations from room temperature to the liquid state at similar to 2900 degrees C. The method significantly reduces model development time and human effort.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Data-driven profile prediction for DIII-D

A new, fully data-driven algorithm has been developed that uses neural networks to predict plasma pro les on a scale of τ E into the future given actuators and the present plasma state. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model is accurate on average, with q predictions the worst and pressure predictions the best. The model can run in milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It also is a candidate for doing phase-space exploration without going through the DIIID database or complicated and computationally expensive simulation codes. Here, a reduced model using only realtime diagnostics has also been developed and formed the basis for a model-predictive control algorithm implemented and successfully tested on DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗