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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA

Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2 ), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Transits of planets with small intervals in circumbinary systems

Transit times around single stars can be described well by a linear ephemeris. However, transit times in circumbinary systems are influenced both by the gravitational perturbations and the orbital phase variations of the central binary star. Adopting a coplanar analog of Kepler-16 as an example, we find that circumbinary planets can transit the same star more than once during a single planetary orbit, a phenomenon we call 'tight transits.' In certain geometric architecture, the projected orbital velocity of the planet and the secondary star can approach zero and change sign, resulting in very long transits and/or 2-3 transits during a single binary orbit. Whether tight transits are possible for a particular system depends primarily on the binary mass ratio and the orbital architecture of both the binary and the planet. We derive a time-dependent criterion to judge when tight transits are possible for any circumbinary system. These results are verified with full dynamical integrations that also reveal other tight transit characteristics, i.e., the transit durations and the intervals between tight transits. For the seven currently known circumbinary systems, we estimate these critical parameters both analytically and numerically. Due to the mutual inclination between the planet and the binary, tight transits can only occur across the less massive star B in Kepler-16, -34, -35, and -47 (for both planets). The long-term average frequency of tight transits (compared to typical transits) for Kepler-16, -34, and -35 are estimated to be several percent. Using full numerical integrations, the next tight transit for each system is predicted and the soonest example appears to be Kepler-47b and -47c, which are likely to have tight transits before 2025. These unique and valuable events often deserve special observational scrutiny.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of beam–beam effects on absolute luminosity calibrations at the CERN Large Hadron Collider

At the Large Hadron Collider (LHC), absolute luminosity calibrations obtained by the van der Meer (vdM) method are affected by the mutual electromagnetic interaction of the two beams. The colliding bunches experience relative orbit shifts, as well as optical distortions akin to the dynamic-β effect, that both depend on the transverse beam separation and must therefore be corrected for when deriving the absolute luminosity scale. In the vdM regime, the beam–beam parameter is small enough that the orbit shift can be calculated analytically. The dynamic-β corrections to the luminometer calibrations, however, had until the end of Run 2 been estimated in the linear approximation only. In this report, the influence of beam–beam effects on the vdM-based luminosity scale is quantified, together with the associated systematic uncertainties, by means of simulations that fully take into account the non-linearity of the beam–beam force, as well as the resulting non-Gaussian distortions of the transverse beam distributions. Two independent multiparticle simulations, one limited to the weak-strong approximation and one that models strong-strong effects in a self-consistent manner, are found in excellent agreement; both predict a percent-level shift of the absolute pp-luminosity values with respect to those assumed until recently in the physics publications of the LHC experiments. These results also provide guidance regarding further studies aimed at reducing the beam–beam-related systematic uncertainty on beam–beam corrections to absolute luminosity calibrations by the van der Meer method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Open Source Fault-tolerant Grid Frequency Measurement for Solar Inverters

The Discrete Fourier transform (DFT) based measurement algorithms are one of the most common measurement algorithms for grid parameter estimation such as rms, phase angle, frequency. Over the past few years, many DFT based algorithms have been developed to enhance its measurement accuracy under steady-state and/or dynamic grid conditions. For example, an adaptive band-pass filter utilizing exponential modulation filter has been proposed to reduce measurement errors at the presence of large frequency deviations. Measurement accuracy of different algorithms including FIR filter, extended Kalman filtering (EKF), and enhanced DFT method have been compared in detail under different grid conditions. Two artificial signals that have 90-degree phase difference were constructed by the Clarke transformation to address the frequency spectrum leakage of DFT. A multi-module approach was developed to enhance both steady-state and dynamic measurement accuracies, in which each module was developed to eliminate some specific errors. Besides DFT-based measurement algorithms, some signal model-based algorithms have been developed to further improve the accuracy under dynamic conditions. However, a key drawback of the state-of-the-art algorithms is that they cannot perform measurements accurately during system transient faults. In the Blue Cut Fire event, there was a phase angle jump of about 26 degrees in the voltage waveform during the transient fault. The phase angle jump fault will cause waveform discontinuity, and these algorithms will fail to provide reliable measurements during this period because they typically assume the waveform to be measured is continuous, no matter what method (DFT, PLL, EKF, FIR, or Taylor WLS) is used for estimation. In fact, the measurement errors during the system transient faults like phase-jump is not required in the IEEE Standard. As a result, although a measurement instrument can pass the strict IEEE Standard, it could still be the source of the problem in the future if we have similar system transient faults, which could happen again. Therefore, developing the fault-tolerant measurement technology is the key to solve the problem.

14 SOLAR ENERGY↗
DOE OSTI2017

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient ( K c ) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, K c has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly K c for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly K c data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), K c values had large seasonal variation across all land covers. The spatial variability of K c was well explained by latitude, suggesting site factors are a major control on K c . Seasonally, K c increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly K c in all land covers, except in EBF. During the peak growing season, forests had the highest K c values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for K c by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. Here, the K c models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extraction of Vibration Data with Imaging

To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Application of bi-Helmholtz nonlocal elasticity and molecular simulations to the dynamical response of carbon nanotubes

The nonlocal theory of elasticity is employed for the study of the free vibrations of carbon nanotubes (CNT). For the first time, a bi-Helmholtz operator has been used instead of the standard Helmholtz operator in a nonlocal beam model. Alongside the continuum formulation and its numerical solution, atomistic Molecular Dynamics (MD) simulations have been conducted in order to directly evaluate the eigenfrequencies of vibrating CNTs with a minimum of adjustable parameters. Our results show that the bi-Helmholtz operator is the most appropriate one to fit MD simulation results. However, the estimation of vibration eigenfrequencies from molecular simulations still remains an open (albeit well-posed) problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A new estimator of the deceleration parameter from galaxy rotation curves

The nature of dark energy can be probed by the derivative Q=dq(z)/dz|{sub 0} at redshift z = 0 of the deceleration parameter q(z). It is probably static if Q<1 or dynamic if Q>2.5, supporting ΛCDM or Λ=(1−q)H{sup 2}, respectively, where H denotes the Hubble parameter. We derive q=1−(4πa{sub 0}/cH){sup 2}, enabling a determination of q(z) by measuring Milgrom’s parameter, a{sub 0}(z), in galaxy rotation curves, equivalent to the coefficient A in the Tully–Fisher relation V{sub c}{sup 4}=AM{sub b} between a rotation velocity V {sub c} and a baryonic mass M {sub b}. We infer that dark matter should be extremely light, with clustering limited to the size of galaxy clusters. The associated transition radius to non-Newtonian gravity can conceivably be probed in a freefall Cavendish-type experiment in space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Optimizers for stabilizing likelihood-free inference

A growing number of applications in particle physics and beyond use neural networks as unbinned likelihood ratio estimators applied to real or simulated data. Precision requirements on the inference tasks demand a high-level of stability from these networks, which are affected by the stochastic nature of training. We show how physics concepts can be used to stabilize network training through a physics-inspired optimizer. In particular, the energy conserving descent (ECD) optimization framework uses classical Hamiltonian dynamics on the space of network parameters to reduce the dependence on the initial conditions while also stabilizing the result near the minimum of the loss function. We develop a version of this optimizer known as , which has few free hyperparameters with limited ranges guided by physical reasoning. We apply to representative likelihood-ratio estimation tasks in particle physics and find on average that it out-performs the widely used Adam optimizer. We expect that ECD will be a useful tool for wide array of data-limited problems, where it is computationally expensive to exhaustively optimize hyperparameters and mitigate fluctuations with ensembling.

Monte Carlo methods↗
DOE OSTI2015

Sequential data assimilation for single-molecule FRET photon-counting data

Data assimilation is a statistical method designed to improve the quality of numerical simulations in combination with real observations. Here, we develop a sequential data assimilation method that incorporates one-dimensional time-series data of smFRET (single-molecule Förster resonance energy transfer) photon-counting into conformational ensembles of biomolecules derived from “replicated” molecular dynamics (MD) simulations. A particle filter using a large number of “replicated” MD simulations with a likelihood function for smFRET photon-counting data is employed to screen the conformational ensembles that match the experimental data. We examine the performance of the method using emulated smFRET data and coarse-grained (CG) MD simulations of a dye-labeled polyproline-20. The method estimates the dynamics of the end-to-end distance from smFRET data as well as revealing that of latent conformational variables. The particle filter is also able to correct model parameter dependence in CG MD simulations. We discuss the applicability of the method to real experimental data for conformational dynamics of biomolecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks

Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. Here, a widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Use of Wedge Absorbers in MICE

Wedge absorbers are needed to obtain longitudinal cooling in ionization cooling. They also can be used to obtain emittance exchanges between longitudinal and transverse phase space. There can be large exchanges in emittance, even with single wedges. In the present note we explore the use of wedge absorbers in the MICE experiment to obtain transverse–longitudinal emittance exchanges within present and future operational conditions. The same wedge can be used to explore “direct” and “reverse” emittance exchange dynamics, where direct indicates a configuration that reduces momentum spread and reverse is a configuration that increases momentum spread. Analytical estimated and ICOOL and G4BeamLine simulations of the exchanges at MICE parameters are presented. Large exchanges can be obtained in both reverse and direct configurations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Quantum spin ice response to a magnetic field in the dipole-octupole pyrochlore Ce 2 Zr 2 O 7

The pyrochlore magnet Ce 2 Zr 2 O 7 has attracted much attention as a quantum spin ice candidate whose novelty derives in part from the dipolar-octupolar nature of the Ce 3+ pseudospin-1/2 degrees of freedom it possesses. We report heat capacity measurements on single crystal samples of Ce 2 Zr 2 O 7 down to T~0.1K in a magnetic field along the [$1,\bar{1},0$] direction. These measurements show that the broad hump in the zero-field heat capacity moves higher in temperature with increasing field strength and is split into two separate humps by the [$1,\bar{1},0$] magnetic field at ~2T. These separate features are due to the decomposition of the pyrochlore lattice into effectively decoupled chains for fields in this direction: One set of chains (α chains) is polarized by the field while the other (β chains) remains free. This situation is similar to that observed in the classical spin ices Ho 2 Ti 2 O 7 and Dy 2 Ti 2 O 7 , but with the twist that here the strong transverse exchange interactions produce substantial quantum effects. Our theoretical modeling suggests that the β chains are close to a critical state, with nearly-gapless excitations. We also report elastic and inelastic neutron scattering measurements on single crystal Ce 2 Zr 2 O 7 in [$1,\bar{1},0$] and [0,0,1] magnetic fields at temperatures down to T=0.03K. The elastic scattering behaves consistently with the formation of independent chains for a [$1,\bar{1},0$] field, while the [0,0,1] field produces a single field-induced elastic magnetic Bragg peak at (0,2,0) and equivalent wavevectors, indicating a polarized spin ice state for fields above ~3T. For both [$1,\bar{1},0$] and [0,0,1] magnetic fields, our inelastic neutron scattering results show an approximately dispersionless continuum of scattering that increases in both energy and intensity with increasing field strength. By modeling the complete set of experimental data using numerical linked cluster and semiclassical molecular dynamics calculations, we demonstrate the dominantly multipolar nature of the exchange interactions in Ce 2 Zr 2 O 7 and the smallness of the parameter θ, which controls the mixing between dipolar and octupolar degrees of freedom. Finally, these results support previous estimates of the microscopic exchange parameters and place strong constraints on the theoretical description of this prominent spin ice candidate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Induced Quasi-Dynamic Disorder in a Structure of Rhenium Ion-Implanted Quartz Glass

The ultraviolet (UV) absorption spectra were studied in UV–Vis–IR quartz glasses exposed to the rhenium ion irradiation at the energy of 30 keV. An increase in ion fluence (5 × 10{sup 15}–5 × 10{sup 17} cm{sup 2}) is found to result in a characteristic fan-shaped broadening of the optical absorption edge of glasses. The results were interpreted in the context of the generalized Urbach rule (the approximation of ion radiation induced quasi-dynamic disorder). The effective radiation disorder cross section of a glass matrix was evaluated from the dose dependence of characteristic Urbach energy. This parameter was found to vary between 1.07 × 10{sup –17}–1.2 × 10{sup –18} cm{sup 2} with increasing fluence. The optical gap width of implanted samples was estimated via the Tautz method for allowed direct interband transitions. The relationship between the second-order deformation potential constants, revealing the correlation between the Urbach energy and the optical gap width in a SiO{sub 2} quartz glass matrix was established using the equivalence principle of static and dynamic structural disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

HeCS-SZ: The hectospec survey of Sunyaev–Zeldovich-selected clusters

We estimate cluster masses and velocity dispersions for 123 clusters from optical spectroscopy to compare the Sunyaev–Zeldovich (SZ) mass proxy and dynamical masses. Our new survey, HeCS-SZ (Hectospec Cluster Survey of SZ-selected clusters), includes 7721 new or remeasured redshifts from MMT/Hectospec observations of 21 SZ-selected clusters at redshifts z = 0.05–0.20. We supplement the Hectospec data with spectra from the Sloan Digital Sky Survey (SDSS) and cluster data from the Cluster Infall Regions in SDSS project and the Hectospec Cluster Survey, our Hectospec survey of clusters selected by X-ray flux. We measure the scaling relation between velocity dispersion and SZ mass estimates from the integrated Compton parameter for an SZ-complete sample of 83 clusters. The observed relation agrees very well with a simple virial scaling from mass (based on SZ) to velocity dispersion. The SZ mass estimates (calibrated with hydrostatic X-ray mass estimates) are not significantly biased compared to dynamical mass estimates under the assumption of small velocity bias of galaxies compared to dark matter particles. Significant mass bias in SZ mass estimates could relieve tension between cosmological results from Planck SZ cluster counts and Planck CMB data. In principle, SZ mass bias and velocity bias of galaxies could conspire to yield good agreement, but the required velocity bias is σ{sub galaxy}≈0.77σ{sub DM}, outside the range of recent models of velocity bias in the literature. More likely, SZ mass bias and velocity bias are both small, and the tension between SZ cluster counts and CMB data requires another explanation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling

This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗