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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 415 records · Page 23

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗

Investigation of post-breakup Coulomb acceleration using a trajectory model

Intermediate mass fragments ejected during the deexcitation of excited projectilelike fragments may promptly decay following ejection; the daughter particles that are subsequently produced are subject to interactions with the residual nucleus that affect final-state observables, a process herein referred to as post-breakup Coulomb acceleration. A simple classical Coulomb interaction model was used to study modification of 8 Be (2 + ), 5 Li (3/2 – ), 7 Li (7/2 – ), 7 Be (7/2 – ), and states in 12 B emitted following heavy-ion collisions of 28 Si + 12 C at 35 MeV/nucleon. Here, in contrast to previous work studying 8 Be (2 + ), excellent agreement between simulation and experiment was obtained using only Coulomb forces when either a Lorentzian or R-matrix line shape was used to describe the initial relative energy rather than a Gaussian. In consideration of the obtained results, improvements to the model and evaluation of experimental data are discussed as future directions, but it was concluded that the effects observed in the present data can be accurately described using only elements of classical mechanics and that the process is largely understood for a wide range of state lifetimes and mass and charge (a)symmetries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Multiple incommensurate magnetic states in the kagome antiferromagnet Na 2 Mn 3 Cl 8

The kagome lattice can host exotic magnetic phases arising from frustrated and competing magnetic interactions. However, relatively few insulating kagome materials exhibit incommensurate magnetic ordering. Here, we present a study of the magnetic structures and interactions of antiferromagnetic Na 2 Mn 3 Cl 8 with an undistorted Mn 2+ kagome network. Using neutron-diffraction and bulk magnetic measurements, we show that Na 2 Mn 3 Cl 8 hosts two different incommensurate magnetic states, which develop at T N1 = 1.6 K and T N2 = 0.6 K. Magnetic Rietveld refinements indicate magnetic propagation vectors of the form q=(q x , q y , $\frac{3}{2}$), and our neutron-diffraction data can be well described by cycloidal magnetic structures. By optimizing exchange parameters against magnetic diffuse-scattering data, we show that the spin Hamiltonian contains ferromagnetic nearest-neighbor and antiferromagnetic third-neighbor Heisenberg interactions, with a significant contribution from long-ranged dipolar coupling. This experimentally determined interaction model is compared with density-functional-theory simulations. Using classical Monte Carlo simulations, we show that these competing interactions explain the experimental observation of multiple incommensurate magnetic phases and may stabilize multi-q states. Here, our results expand the known range of magnetic behavior on the kagome lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Intersymbol Interference Investigations Using a 3D Time-Dependent Traveling Wave Tube Model

For the first time, a physics based computational model has been used to provide a direct description of the effects of the TWT (Traveling Wave Tube) on modulated digital signals. The TWT model comprehensively takes into account the effects of frequency dependent AM/AM and AM/PM conversion; gain and phase ripple; drive-induced oscillations; harmonic generation; intermodulation products; and backward waves. Thus, signal integrity can be investigated in the presence of these sources of potential distortion as a function of the physical geometry of the high power amplifier and the operational digital signal. This method promises superior predictive fidelity compared to methods using TWT models based on swept amplitude and/or swept frequency data. The fully three-dimensional (3D), time-dependent, TWT interaction model using the electromagnetic code MAFIA is presented. This model is used to investigate assumptions made in TWT black box models used in communication system level simulations. In addition, digital signal performance, including intersymbol interference (ISI), is compared using direct data input into the MAFIA model and using the system level analysis tool, SPW (Signal Processing Worksystem).

Kory, Carol L.↗

Understanding Earthquake Fault Systems Using QuakeSim Analysis and Data Assimilation Tools

We are using the QuakeSim environment to model interacting fault systems. One goal of QuakeSim is to prepare for the large volumes of data that spaceborne missions such as DESDynI will produce. QuakeSim has the ability to ingest distributed heterogenous data in the form of InSAR, GPS, seismicity, and fault data into various earthquake modeling applications, automating the analysis when possible. Virtual California simulates interacting faults in California. We can compare output from long time history Virtual California runs with the current state of strain and the strain history in California. In addition to spaceborne data we will begin assimilating data from UAVSAR airborne flights over the San Francisco Bay Area, the Transverse Ranges, and the Salton Trough. Results of the models are important for understanding future earthquake risk and for providing decision support following earthquakes. Improved models require this sensor web of different data sources, and a modeling environment for understanding the combined data.

earthquake simulations↗

Modelling dust transport in DIII-D with DTOKS-Upgrade

Comprehensive upgrades to the dust transport code Dust in TOKamaks (DTOKS) that extend the plasma-dust interaction model are presented and compared with recent measurements of dust transport in DIII-D. Simulations incorporating variation in physical properties of graphite dust with temperature and size in a stationary plasma background suggest a substantial decrease in lifetimes due principally to thermal expansion. The trajectories of 53 dust grains identified from analysis of visible camera data taken across two similar shots were used to measure the dust particle velocity distributions. Dust tracks terminated mostly at the outer divertor strike point having a mean observation time of 2.1 ± 0.4 ms. Stochastic modelling of 200 graphite dust particles in the DIII-D tokamak performed with DTOKS-U using plasma simulations generated by OEDGE found similar behaviour, with particles ablating rapidly after acquiring a positive charge in the region close to the outer strike point, creating an acute source of neutral carbon atoms. The simulated mean lifetime, 11 ± 2 ms, showed approximate agreement with experimental observation when corrected by accounting for dust visibility and ignoring the longest trajectories 1.5 ± 0.2 ms. Synthetic diagnostic data generated from coupling the results of DTOKS-U with the visualisation software Calcam offers a powerful new tool for validation of simulations and predictive calculations of dust dynamics.

Physics↗

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗

Reliability Quantification of Advanced Stirling Convertor (ASC) Components

The Advanced Stirling Convertor, is intended to provide power for an unmanned planetary spacecraft and has an operational life requirement of 17 years. Over this 17 year mission, the ASC must provide power with desired performance and efficiency and require no corrective maintenance. Reliability demonstration testing for the ASC was found to be very limited due to schedule and resource constraints. Reliability demonstration must involve the application of analysis, system and component level testing, and simulation models, taken collectively. Therefore, computer simulation with limited test data verification is a viable approach to assess the reliability of ASC components. This approach is based on physics-of-failure mechanisms and involves the relationship among the design variables based on physics, mechanics, material behavior models, interaction of different components and their respective disciplines such as structures, materials, fluid, thermal, mechanical, electrical, etc. In addition, these models are based on the available test data, which can be updated, and analysis refined as more data and information becomes available. The failure mechanisms and causes of failure are included in the analysis, especially in light of the new information, in order to develop guidelines to improve design reliability and better operating controls to reduce the probability of failure. Quantified reliability assessment based on fundamental physical behavior of components and their relationship with other components has demonstrated itself to be a superior technique to conventional reliability approaches based on utilizing failure rates derived from similar equipment or simply expert judgment.

Shah, Ashwin R.↗

Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games

This paper introduces a novel framework for modeling interacting humans in a multi-stage game environment by combining concepts from game theory and reinforcement learning. The proposed model has the following desirable characteristics: (1) Bounded rational players, (2) strategic (i.e., players account for one anothers reward functions), and (3) is computationally feasible even on moderately large real-world systems. To do this we extend level-K reasoning to policy space to, for the first time, be able to handle multiple time steps. This allows us to decompose the problem into a series of smaller ones where we can apply standard reinforcement learning algorithms. We investigate these ideas in a cyber-battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.

Lee, Ritchie↗

Proceedings of the Workshop on Identification and Control of Flexible Space Structures, volume 1

Identification and control of flexible space structures were studied. Exploration of the most advanced modeling estimation, identification and control methodologies to flexible space structures was discussed. The following general areas were discussed: space platforms, antennas, and flight experiments; control/structure interactions - modeling, integrated design and optimization, control and stabilization, and shape control; control technology; control of space stations; large antenna control, dynamics and control experiments, and control/structure interaction experiments.

Rodriguez, G.↗

A Self-Consistent Model of the Interacting Ring Current Ions with Electromagnetic ICWs

Initial results from a newly developed model of the interacting ring current ions and ion cyclotron waves are presented. The model is based on the system of two bound kinetic equations: one equation describes the ring current ion dynamics, and another equation describes wave evolution. The system gives a self-consistent description of ring current ions and ion cyclotron waves in a quasilinear approach. These two equations were solved on a global scale under non steady-state conditions during the May 2-5, 1998 storm. The structure and dynamics of the ring current proton precipitating flux regions and the wave active zones at three time cuts around initial, main, and late recovery phases of the May 4, 1998 storm phase are presented and discussed in detail. Comparisons of the model wave-ion data with the Polar/HYDRA and Polar/MFE instruments results are presented..

Khazanov, G. V.↗

Optical model calculations of heavy-ion target fragmentation

The fragmentation of target nuclei by relativistic protons and heavy ions is described within the context of a simple abrasion-ablation-final-state interaction model. Abrasion is described by a quantum mechanical formalism utilizing an optical model potential approximation. Nuclear charge distributions of the excited prefragments are calculated by both a hypergeometric distribution and a method based upon the zero-point oscillations of the giant dipole resonance. Excitation energies are estimated from the excess surface energy resulting from the abrasion process and the additional energy deposited by frictional spectator interactions of the abraded nucleons. The ablation probabilities are obtained from the EVA-3 computer program. Isotope production cross sections for the spallation of copper targets by relativistic protons and for the fragmenting of carbon targets by relativistic carbon, neon, and iron projectiles are calculated and compared with available experimental data.

Townsend, L. W.↗

Superscaling variable and neutrino energy reconstruction from theoretical predictions to experimental limitations

We introduce the novel approach of using the superscaling variable as an observable and an analysis tool in the context of charged current neutrino-nucleus interactions. We study the relation between the superscaling variable and the removal energy, in addition to other fundamental parameters of the neutrino-nucleus interaction models. In the second half of the paper, we discuss the experimental viability of this measurement following a study of neutrino energy and missing momentum reconstruction. We show that the superscaling variable is measurable in neutrino interaction experiments provided that the proton is detected in the final state. We discuss the resolution of this measurement, and the limitation imposed by the proton’s detection threshold. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗

Time-Dependent Traveling Wave Tube Model for Intersymbol Interference Investigations

For the first time, a computational model has been used to provide a direct description of the effects of the traveling wave tube (TWT) on modulated digital signals. The TWT model comprehensively takes into account the effects of frequency dependent AM/AM and AM/PM conversion, gain and phase ripple; drive-induced oscillations; harmonic generation; intermodulation products; and backward waves. Thus, signal integrity can be investigated in the presence of these sources of potential distortion as a function of the physical geometry of the high power amplifier and the operational digital signal. This method promises superior predictive fidelity compared to methods using TWT models based on swept-amplitude and/or swept-frequency data. The fully three-dimensional (3D), time-dependent, TWT interaction model using the electromagnetic code MAFIA is presented. This model is used to investigate assumptions made in TWT black-box models used in communication system level simulations. In addition, digital signal performance, including intersymbol interference (ISI), is compared using direct data input into the MAFIA model and using the system level analysis tool, SPW.

Kory, Carol L.↗

Traveling-Wave Tube Amplifier Model to Predict High-Order Modulation Intersymbol Interference

Demands for increased data rates in satellite communications necessitate higher order modulation schemes, larger system bandwidth, and minimum distortion of the modulated signal as it is passed through the traveling wave tube amplifier (TWTA). One type of distortion that the TWTA contributes to is intersymbol interference (ISI), and this becomes particularly disruptive with wide-band, complex modulation schemes. It is suspected that in addition to the dispersion of the TWT, frequency dependent reflections due to mismatches within the TWT are a significant contributor to ISI. To experimentally investigate the effect of these mismatches within the physical TWT on ISI would be prohibitively expensive, as it would require manufacturing numerous amplifiers in addition to the acquisition of the required digital hardware. In an attempt to develop a more accurate model to correlate IS1 with the TWTA and the operational signal, a fully three-dimensional (3D), time-dependent, TWT interaction model has been developed using the electromagnetic particle-in-cell (PIC) code MAFIA (solution of Maxwell's equations by the Finite-Integration-Algorithm). The model includes a user defined slow-wave circuit with a spatially tapered region of loss to implement a sever, and spatially varied geometry (such as helical pitch) to implement a phase velocity taper. The model also includes user defined input/output coupling and an electron beam contained by solenoidal, electrostatic, or periodic permanent magnet (PPM) focusing allowing standard or novel TWTs to be investigated. This model comprehensively takes into account the effects of frequency dependent nonlinear distortions (MAM and AMPM); gain ripple due to frequency dependent reflections at the input/output coupling, severs, and mismatches from dynamic pitch variations; drive induced oscillations; harmonic generation; intermodulation products; and backward waves.

Kory, Carol L.↗

Coupling Processes Between Atmospheric Chemistry and Climate

This is the final report for NAS5-97039 for work performed between December 1996 and November 1999. The overall objective of this project is to improve the understanding of coupling processes among atmospheric chemistry, aerosol and climate, all important for quantitative assessments of global change. Among our priority are changes in ozone and stratospheric sulfate aerosol, with emphasis on how ozone in the lower stratosphere would respond to natural or anthropogenic changes. The work emphasizes two important aspects: (1) AER's continued participation in preparation of, and providing scientific input for, various scientific reports connected with assessment of stratospheric ozone and climate. These include participation in various model intercomparison exercises as well as preparation of national and international reports. (2) Continued development of the AER three-wave interactive model to address how the transport circulation will change as ozone and the thermal properties of the atmosphere change, and assess how these new findings will affect our confidence in the ozone assessment results.

Ko, Malcolm↗

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗