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NASA NTRS1994

Acceleration of electrons and ions by strong lower-hybrid turbulence in solar flares

One of the outstanding problems in solar flare theory is how to explain the 10-20 keV and greater hard x-ray emissions by a thick target bremsstrahlung model. The model requires the acceleration mechanism to accelerate approximately 10(exp 35) electrons sec(exp -l) with comparable energies, without producing a large return current which persists for long time scales after the beam ceases to exist due to Lenz's law, thereby, producing a self-magnetic field of order a few mega-Gauss. In this paper, we investigate particle acceleration resulting from the relaxation of unstable ion ring distributions, producing strong wave activity at the lower hybrid frequency. It is shown that strong lower hybrid wave turbulence collapses in configuration space producing density cavities containing intense electrostatic lower hybrid wave activity. The collapse of these intense nonlinear wave packets saturate by particle acceleration producing energetic electron and ion tails. There are several mechanisms whereby unstable ion distributions could be formed in the solar atmosphere, including reflection at perpendicular shocks, tearing modes, and loss cone depletion. Numerical simulations of ion ring relaxation processes, obtained using a 2 1/2-D fully electromagnetic, relativistic particle in cell code are discussed. We apply the results to the problem of explaining energetic particle production in solar flares. The results show the simultaneous acceleration of both electrons and ions to very high energies: electrons are accelerated to energies in the range 10-500 keV, while ions are accelerated to energies of the order of MeVs, giving rise to x-ray emission and gamma-ray emission respectively. Our simulations also show wave generation at the electron cyclotron frequency. We suggest that these waves are the solar millisecond radio spikes. The strong turbulence collapse process leads to a highly filamented plasma producing many localized regions for particle acceleration and resulting in approximately 10(exp 17) electron 'beamlets' of width approximately equal to 10 lambda sub De which eliminates the production of large magnetic fields. In this paper, we demonstrate that the model produces an energetic electron spectrum with the right flux to account for the hard x-ray observations.

Spicer, D. S.↗

DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗

DOE OSTI2024

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

NASA NTRS2020

Lunar Transient Accelerations White Paper

Recently, the United States announced a plan to return astronauts to the moon by 2024 [8]. Lunar landing (and subsequent Mars landing) architecture was not considered when the current NASA standards and vehicle design requirements for crew injury risk were developed. Therefore a gap exists in protecting the crew in planetary landing scenarios. One of the interesting aspects of this design reference mission (DRM)is the consideration of having the crew stand during dynamic phases of flight. Although this approach was contemplated for the Apollo Lunar Module, current NASA standards do not address design solutions that allow the crew to stand. Currently, NASA uses several tools and associated limits to mitigate crew injury because of dynamic loads. Some of these tools are the Brinkley Dynamic Response Criterion (BDRC) model and Hybrid III Anthropomorphic Test Devices (ATDs) [10]; and these have several limitations for assessing spaceflight loading environments, as well as specific underlying assumptions that may not be applicable in planetary landing vehicles. The BDRC is a simple lumped mass parameter model developed by the U.S. military, and has been used primarily to evaluate injury risk associated with aircraft ejection systems. The model evaluates seat accelerations in each axis to determine injury risk. Because the model treats the human-seat-restraint system as a single system, it is contingent on a restraint system and seat with similar characteristics of the original test data underlying the model. In particular, the model requires a rigid seat with a minimum natural frequency of 15Hz, minimal seat pan padding, side supports, and multipoint harness. The BDRC model uses undamped natural frequency and damping coefficients based on these requirements and any deviations may render the model injury predictions void. In lunar landing, one expects that a minimal or even no seat with minimal restraints will be employed. In this case, the original model parameters are likely to not be applicable. For capsule-based spacecraft returning crew to Earth, the Hybrid III ATD in various sizes also is used to supplement the BDRC. This analytical tool was added to address limitations in the BDRC related to spacecraft landings while wearing a pressure garment and helmet. Although the Hybrid III ATD has additional measurement capability; head, neck,and lumbar spine responses were the only metrics included because of the limitations imposed by the model.Lunar landing acceleration limits must assume the crew is standing during landing, an orientation for which we have limited data.Although the Apollo missions did employ a standing orientation for the crew, much of the data is lost. Therefore data from othersources havebeen examined to inform lunar landing acceleration limits.

Jeffrey T Somers↗

NASA NTRS2014

High-Energy Pulsar Models: Developments and New Questions

The past few years have seen a major advance in observational knowledge of high-energy (HE) pulsars. The Fermi Large Area Telescope (LAT) and AGILE have increased the number of known gamma-ray pulsars by an order of magnitude, its members being divided roughly equally among millisecond pulsars (MSPs), young radio-loud pulsars, and young radio-quiet pulsars. Many new and diverse emission characteristics are being measured, while radio and X-ray follow-up observations increase the pulsar detection rate and enrich our multiwavelength picture of these extreme sources. The wealth of new data has provided impetus for further development and improvement of existing theoretical pulsar models. Geometric light curve (LC) modelling has uncovered three broad classes into which HE pulsars fall: those where the radio profile leads, is aligned with, or lags the gamma-ray profile. For example, the original MSP and original black widow system are members of the second class, requiring co-located emission regions and thereby breaking with traditional notions of radio emission origin. These models imply narrow accelerator gaps in the outer magnetosphere, indicating copious pair production even in MSP magnetospheres that were previously thought to be pair-starved. The increased quality and variety of the LCs necessitate construction of ever more sophisticated models. We will review progress in global magnetosphere solutions which specify a finite conductivity on field lines above the stellar surface, filling the gap between the standard vacuum and force-free (FF; plasma-filled) models. The possibility of deriving phase-resolved spectra for the brightest pulsars, coupled with the fact that the HE pulsar population is sizable enough to allow sampling of various pulsar geometries, will enable much more stringent testing of future radiation models. Reproduction of the observed phase-resolved behavior of this disparate group will be one of the next frontiers in pulsar science, impacting on our understanding of particle acceleration, emission, and magnetosphere geometry. One may now also study evolutionary trends of the measured or inferred quantities, and probe pulsar visibility and population properties such as radiation beam sizes of different pulsar classes, as well as the distribution of spin-down power, gamma-ray luminosity, conversion efficiency, spectral index, and cutoff energy across the population. Lastly, the recent detection of very-high-energy (VHE) pulsations from the Crab pulsar generated quite a few ideas to explain this emission, leading to an extension of standard models and possibly even a bridge between the physics of pulsars and pulsar wind nebulae (PWNe).

gamma rays:↗

DOE OSTI2025

Viral Dynamic Models During COVID‐19: Are We Ready for the Next Pandemic?

Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Electrochemical Characterization of Molten Salt Chemistry During Atmospheric Ingressions

This report describes the salt chemistry and sedimentation studies that were completed in FY23 as part of the Pyrochemical Fuel Cycles – ANL project. The primary objective of these activities was to employ electrochemical methods in order to quantify and gain insights into the reaction mechanisms associated with the interaction of O2 and moisture impurities with the molten chloride salts used for the pyrochemical processing of nuclear materials. If not monitored and controlled, the ingression of these atmospheric impurities can lead to changes in the salt redox conditions and initiate the formation of oxide particles, potentially resulting in operational and safeguards challenges. By elucidating the mechanisms underlying the formation of oxide particles, we can effectively monitor the conditions of the salt and design systems to mitigate the influence of atmospheric O2 and H2O. To achieve these goals, we designed a test system that allowed for precisely controlled gas ingressions into the molten salt vessel. This system included on-line monitoring provided by electrochemical probes that enable near real-time measurements of the salt conditions throughout the course of the experiment. Using this system, we systematically varied experimental conditions, including O2/Ar flow rates and O2 concentrations, to comprehensively understand their impact on solid particle formation. We also used a particle size and shape analyzer to characterize the particles that were generated. Tests in FY23 concentrated on the formation of CeO2 particles, but we also prepared a separate apparatus targeting UO2 particles for use in FY24. We additionally conducted extensive modeling activities in support of this work. This included multiphysics simulations of the gas ingressions along with the development of a machine learning workflow to enable accelerated molecular dynamics modeling of the molten salt chemistry of LiCl-KCl-UCl3. The combination of experimental and modeling tools has allowed us to begin to get a more complete understanding of the reactions that occur when atmospheric ingressions occur in pyroprocessing systems.

Guo, Jicheng↗

NASA NTRS1972

Apollo oxygen tank stratification analysis, volume 2

An analysis of flight performance of the Apollo 15 cryogenic oxygen tanks was conducted with the variable grid stratification math model developed earlier in the program. Flight conditions investigated were the CMP-EVA and one passive thermal control period which exhibited heater temperature characteristics not previously observed. Heater temperatures for these periods were simulated with the math model using flight acceleration data. Simulation results (heater temperature and tank pressure) compared favorably with the Apollo 15 flight data, and it was concluded that tank performance was nominal. Math model modifications were also made to improve the simulation accuracy. The modifications included the addition of the effects of the tank wall thermal mass and an improved system flow distribution model. The modifications improved the accuracy of simulated pressure response based on comparisons with flight data.

Barton, J. E.↗

DOE OSTI2022

GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling

We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multistart gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 s. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of ${ \mathcal O }({10}^{5})$ lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1987

Accurate pressure gradient calculations in hydrostatic atmospheric models

A method for the accurate calculation of the horizontal pressure gradient acceleration in hydrostatic atmospheric models is presented which is especially useful in situations where the isothermal surfaces are not parallel to the vertical coordinate surfaces. The present method is shown to be exact if the potential temperature lapse rate is constant between the vertical pressure integration limits. The technique is applied to both the integration of the hydrostatic equation and the computation of the slope correction term in the horizontal pressure gradient. A fixed vertical grid and a dynamic grid defined by the significant levels in the vertical temperature distribution are employed.

Carroll, John J.↗

DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗

NASA NTRS2000

Nonlinear Shock Acceleration and Photon Emission in Supernova Remnants

We have extended a simple model of nonlinear diffusive shock acceleration (Berezhko & Ellison 1999: Ellison &, Berezhko 1999a) to include the injection and acceleration of electrons and the production of photons from bremsstrahlung, synchrotron, inverse Compton, and pion-decay processes. We argue that, the results of this model, which is simpler to use than more elaborate ones, offer a significant improvement, over test-particle, power-law spectra which are often used in astrophysical applications of diffusive shock acceleration. With an evolutionary supernova remnant (SNR) model to obtain shock parameters as functions of ambient interstellar medium parameters and time, we predict broad-band continuum photon emission from supernova remnants in general, and SN1006 in particular, showing that our results compare well with the more complete time-dependent and spherically symmetric nonlinear model of Berezhko, Ksenofontov, & Petukhov (1999a). We discuss the implications nonlinear shock acceleration has for X-ray line emission, and use our model to describe how ambient conditions determine the TeV/radio flux ratio, an important parameter for gamma-ray observations of radio SNRs.

Ellison, Donald C.↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Modeling betatron radiation using particle-in-cell codes for plasma wakefield accelerator diagnostics

The analysis of plasma wakefield acceleration experimental measurements, particularly in the characterization of photons emitted through the betatron radiation mechanism, requires the development of accurate numerical models. These computational models are crucial for supporting modern instrumentation designed to measure the single-shot, double-differential angular-energy radiation spectra resulting from interactions between beams and plasmas. Motivated by the needs of such applications, this paper presents detailed numerical models of betatron radiation generated in beam-plasma acceleration experiments. These models are based on the integration of the Liénard-Wiechert (LW) potentials, applied to computed particle trajectories. The particle trajectories are generated using three distinct methods: first, by tracking particles through idealized fields in the blowout regime of PWFA; second, by obtaining trajectories using the fast quasistatic particle-in-cell (PIC) code quickpic; and third, obtaining trajectories from the fully self-consistent PIC code osiris. To ensure the accuracy and reliability of these models, the paper includes various benchmark tests using analytical expressions, as well as employing the PIC code epoch, which takes an alternative approach by using a Monte Carlo quantum electrodynamics (QED)-based radiation model. Additionally, the paper presents simulations of the expected experimental betatron radiation spectra, taking into account parameters relevant to PWFA and plasma photocathode experiments at the SLAC FACET-II facility.

Yadav, M. [University of California, Los Angeles, ↗

NASA NTRS1986

Structural analysis of the controlled impact demonstration of a jet transport airplane

An analytical method has been developed for assessing crash dynamics of large transport aircraft, such as in the NASA Controlled Impact Demonstration (CID) jet crash test on December 1, 1984. The DYCAST nonlinear finite-element computer code was used in a series of progressively more difficult tasks to model complete transport aircraft crashes. Single aircraft frames and fuselage section vertical drop tests were modeled and analyzed to obtain comparisons with experimental data and to develop hybrid element crash springs for use in the large CID model. Predictions of crash and acceleration levels from a symmetric CID model agreed well with data from the CID experiment.

Fasanella, E. L.↗

NASA NTRS2022

Extreme Energetic Particle Events By Superflare-Associated CMES From Solar-Like Stars

Discovery of frequent superflares on active cool stars opened a new avenue in understanding the properties of eruptive events and their impact on exoplanetary environments. Solar data suggest that coronal mass ejections (CMEs) should be associated with superflares on active solar-like planet hosts and produce solar/stellar energetic particle (SEP/StEP) events. Here, we apply the 2D Particle Acceleration and Transport in the Heliosphere model to simulate the SEPs accelerated via CME-driven shocks from the Sun and young solar-like stars. We derive the scaling of SEP fluence and hardness of energy spectra with CME speed and associated flare energy. These results have crucial implications for the prebiotic chemistry and expected atmospheric biosignatures from young rocky exoplanets as well as the chemistry and isotopic composition of circumstellar disks around infant solar-like stars.

Junxiang Hu↗

DOE OSTI2025

Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor

This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). We develop a new PMSM model that consists of velocity and acceleration to lump the system information and an external disturbance into a disturbance. The extended state observer (ESO) is designed to estimate the velocity, acceleration, and disturbance. By estimating the state variables and disturbance using the ESO, the model predictive control (MPC) finds the optimal control input by predicting future system behavior. Additionally, the direct current controller is designed so that the direct current converges to zero. Because the proposed method is not designed based on the cascade structure from the viewpoint of velocity control, the optimization control for the velocity and currents can be defined. Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.

42 ENGINEERING↗