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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A conservative phase-space moving-grid strategy for a 1D-2V Vlasov–Fokker–Planck Solver

In this work, we develop a conservative configuration- and velocity-space (i.e., phase-space) moving-grid strategy for the Vlasov–Fokker–Planck (VFP) equation in a planar geometry. The velocity-space grid is normalized and shifted in terms of the thermal speed and the bulk-fluid velocity, respectively. The configuration-space grid is moved according to a mesh-motion-partial-differential equation (MMPDE), which equidistributes a monitor function that is inversely proportional to the gradient-length scales of the macroscopic plasma quantities. The resulting inertial terms in the transformed VFP equations are discretized to ensure the discrete conservation of mass, momentum, and energy. To satisfy the discrete conservation theorems in the presence of phase-space mesh motion, we employ the method of discrete nonlinear constraints – explored in previous studies – but the underlying symmetries are determined in a much more efficient manner than before. The conservative grid-adaptivity strategy provides an efficient scheme that resolves important physical structures in the phase-space while controlling the computational complexity at all times. We demonstrate the favorable features of the proposed algorithm through a set of test cases of increasing complexity. The problems test independent components of the algorithms, as well as the integrated capability on settings relevant to inertial confinement fusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Production of alternate realizations of DESI fiber assignment for unbiased clustering measurement in data and simulations

A critical requirement of spectroscopic large scale structure analyses is correcting for selection of which galaxies to observe from an isotropic target list. This selection is often limited by the hardware used to perform the survey which will impose angular constraints of simultaneously observable targets, requiring multiple passes to observe all of them. In SDSS this manifested solely as the collision of physical fibers and plugs placed in plates. In DESI, there is the additional constraint of the robotic positioner which controls each fiber being limited to a finite patrol radius. A number of approximate methods have previously been proposed to correct the galaxy clustering statistics for these effects, but these generally fail on small scales. To accurately correct the clustering we need to upweight pairs of galaxies based on the inverse probability that those pairs would be observed (Bianchi & Percival 2017). This paper details an implementation of that method to correct the Dark Energy Spectroscopic Instrument (DESI) survey for incompleteness. To calculate the required probabilities, we need a set of alternate realizations of DESI where we vary the relative priority of otherwise identical targets. These realizations take the form of alternate Merged Target Ledgers (AMTL), the files that link DESI observations and targets. We present the method used to generate these alternate realizations and how they are tracked forward in time using the real observational record and hardware status, propagating the survey as though the alternate orderings had been adopted. We detail the first applications of this method to the DESI One-Percent Survey (SV3) and the DESI year 1 data. We include evaluations of the pipeline outputs, estimation of survey completeness from this and other methods, and validation of the method using mock galaxy catalogs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2021

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign would be a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR would, for the first time ever, present a need to communicate with multiple surface assets that are co-located on Mars in a coordinated effort to accomplish the unified objective of fetching, transporting, and returning samples from Mars. Currently, Mars surface assets relay data to and from Earth using a number of orbiters in what’s known as the Mars Relay Network (MRN). This network is characterized by a small number of surface assets distributed across the Martian globe and a larger number of orbiters to provide relay services. As of June 2020, there are two surface assets for which five orbiters are providing relay. During the MSR Campaign, there would be two rovers and a lander that all would require relay communication from a small number of Mars orbiters to meet the aggressive MSR timeline. The inversion of the current MRN paradigm, a system of many surface assets requiring relay and few orbiters to provide relay, necessitates the unique challenge of optimally allocating relay passes to maximize the operational capability of all assets. The allocation must consider a large number of trade variables including Mars asset operational requirements and Earth ground system constraints, including staffing schedules, operations planning across time zones, and more. To address these telecommunication challenges, the Mars Asset Relay Mission Link Allocation Design Environment (MARMLADE) tool was developed. It is a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets and quantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbiter geometry and telecommunication capabilities provided by JPL’s Telecom Orbit Analysis and Simulation Tool (TOAST) software to compute which asset should get each pass based on a series of heuristics and predictions of all assets’ states. Within MARMLADE, the user can provide inputs including the option for time-based pass splitting, fixed FWD data rate capabilities, DFE communication capabilities, and link parameters allowing for the assessment of complex operations and hardware trades using surface mission operational efficiency as a primary figure of merit. As the MSR mission concepts continue to mature, MARMLADE is being used to assess ability of all MSR elements to meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.This paper will describe the motivation and design of the MARMLADE tool and how it is being used to perform campaign and mission level trades, generate requirements, and support development of the MSR surface mission scenarios.

Lee, Charles↗
DOE OSTI2021

Interferometric Constraints on Spacelike Coherent Rotational Fluctuations

Precision measurements are reported of the cross-spectrum of rotationally induced differential position displacements in a pair of colocated 39 m long, high-power Michelson interferometers. One arm of each interferometer is bent 90° near its midpoint to obtain sensitivity to rotations about an axis normal to the plane of the instrument. The instrument achieves quantum-limited sensing of spatially correlated signals in a broad frequency band extending beyond the 3.9-MHz inverse light travel time of the apparatus. For stationary signals with bandwidth Δf>10 kHz, the sensitivity to rotation-induced strain h of classical or exotic origin surpasses CSDδh

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Bayesian Inference for the Seismic Moment Tensor Using Regional Waveforms and Teleseismic- P Polarities with a Data-Derived Distribution of Velocity Models and Source Locations

The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. Here, we incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.

58 GEOSCIENCES↗
NASA NTRS1975

Effect of attitude constraints on solar-electric geocentric transfers

The present work assesses the increase in flight time and fuel consumption due to introducing attitude constraints on both the thrust vector and the plane of the solar cell arrays on geocentrically orbiting spacecraft. A modified version of the SECKSPOT computer program calculates nearly time-optimal trajectories for the constrained case of zero pitch and roll. Unconstrained cases are generated with the SECKSPOT code. It is concluded that with a pitch constraint but without a roll constraint, power would not be a function of thrust direction, and so the time-optimal thruster direction would be along the projection of the primer vector in the plane normal to the radius vector. The roll constraint would cause power to become a function of thrust angle and sun angle. For certain sun angles the locus of the ratio of power to maximum power is concave and thus there may be jumps in the control angle. Comparisons are made for a SERT-C type mission between constrained and unconstrained cases in an inverse square gravity field.

Sackett, L. L.↗
NASA NTRS1988

Part 1: Aspects of lithospheric evolution on Venus. Part 2: Thermal and collisional histories of chondrite parent bodies

The geological evolution of distinctly different kinds of solar system objects is addressed. Venus has been observed over the past decade by orbital radars on both American and Soviet spacecraft. These surface measurements provide clues to the structure and evolution of the lithosphere. The parent bodies of chondritic meteorites, thought to resemble asteroids, represent the other end of the size spectrum of terrestrial objects. Their early thermal and collisional histories may be constrained by the chemical and textural record preserved in meteorite samples. Impact craters on Venus have been observed by the Soviet Venera 15/16 spacecraft. A formalism is presented by which the size-frequency distribution of impact craters may be used to estimate upper bounds on the mean global rates of volcanic resurfacing and lithospheric recycling on that planet over the past several hundred million years. The impact crater density reported from Venera observations, if valid for the entire Venus surface, indicates a mean volcanic flux no greater than 2 cu km/y, corresponding to a maximum average rate of resurfacing of about 4 km/b.y. For the lowest estimated mean crater retention age of the surface of Venus imaged by Venera 15/16, the rate of lithospheric recycling on Venus does not exceed 1.5 sq km/y. Ordinary chondrite meteorites show textural and chemical patterns indicative of varying intensities of thermal metamorphism. The conventional onion-shell model, which envisions highly metamorphosed material in the core and less intensely heated rocks near the surface, predicts an inverse relation between peak temperature and cooking rate, but none has been observed. A metamorphosed-planetesimal model is devised to explain this discrepancy, whereby heating occurs in planetesimals a few kilometers in radius which then accrete to form 100-km-radius parent bodies. Cooling rates are then randomly controlled by burial depth. Thermal and collisional constraints are examined, and the model is found to be applicable only to highly insulating Al-26-rich planetesimals that remain closely aggregated upon accretion. An alternative model is presented, in which onion-shell parent bodies are collisionally fragmented during metamorphism and then gravitationally reassembled. If reassembly times are short, then cooling rates would be determined by burial depth in the reaccreted parent body. This model, unlike previous ones, can explain both coherent and incoherent cooling of Breccia clasts by collisions during or after metamorphism, respectively.

Grimm, Robert E.↗
DOE OSTI2026

Synthetic modeling of soft x-ray emissivity for magnetic island analysis in LTX-β

We present a synthetic soft x-ray (SXR) forward-modeling framework that characterizes the emissivity structure of rotating magnetic islands in the Lithium Tokamak eXperiment-β across space and time. Magnetic islands associated with tearing modes produce modulations in line-integrated SXR brightness. Traditional tomographic methods struggle to resolve these structures in devices with limited sightlines, resulting in an under-determined inversion problem, or otherwise necessitate equilibrium reconstruction data, preventing their use in active control. Our approach removes this challenge by combining a fully three-dimensional ray-tracing model with a time-dependent emissivity prescription of a $m/n$ = 2/1 magnetic island. The model is validated through helical island geometry, rotation measured by magnetic diagnostics, and equilibrium constraints from PSI-Tri reconstructions. Synthetic brightness signals are generated for each photodiode sightline from a modeled emissivity profile and directly compared with experimental data from a tangential midplane SXR array. By fitting the synthetic diagnostic output to the observed brightness across time, we simultaneously infer key geometric parameters-such as island radial location, island width, and rotation frequency-without requiring full tomographic reconstruction, all with an average absolute deviation of less than 3%. This work demonstrates that forward modeling with a single tangential array can extract key island parameters in a spherical tokamak, providing a computationally efficient alternative to conventional SXR tomography and providing a pathway toward real-time magnetic-island characterization in future devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2011

Modeling Kepler Transit Light Curves as False Positives: Rejection of Blend Scenarios for Kepler-9, and Validation of Kepler-9 d, a Super-Earth-Size Planet in a Multiple System

Light curves from the Kepler Mission contain valuable information on the nature of the phenomena producing the transit-like signals. To assist in exploring the possibility that they are due to an astrophysical false positive we describe a procedure (BLENDER) to model the photometry in terms of a blend rather than a planet orbiting a star. A blend may consist of a background or foreground eclipsing binary (or star-planet pair) whose eclipses are attenuated by the light of the candidate and possibly other stars within the photometric aperture. We apply BLENDER to the case of Kepler-9 (KIC 3323887), a target harboring two previously confirmed Saturn-size planets (Kepler-9 b and Kepler-9 c) showing transit timing variations, and an additional shallower signal with a 1.59 day period suggesting the presence of a super-Earth-size planet. Using BLENDER together with constraints from other follow-up observations we are able to rule out all blends for the two deeper signals and provide independent validation of their planetary nature. For the shallower signal, we rule out a large fraction of the false positives that might mimic the transits. The false alarm rate for remaining blends depends in part (and inversely) on the unknown frequency of small-size planets. Based on several realistic estimates of this frequency, we conclude with very high confidence that this small signal is due to a super-Earth-size planet (Kepler-9 d) in a multiple system, rather than a false positive. The radius is determined to be 1.64(exp)(sub-14),R, and current spectroscopic observations are as yet insufficient to establish its mass.

PLANETARY SYSTEMS↗
NASA NTRS1992

The effects of video compression on acceptability of images for monitoring life sciences experiments

Future manned space operations for Space Station Freedom will call for a variety of carefully planned multimedia digital communications, including full-frame-rate color video, to support remote operations of scientific experiments. This paper presents the results of an investigation to determine if video compression is a viable solution to transmission bandwidth constraints. It reports on the impact of different levels of compression and associated calculational parameters on image acceptability to investigators in life-sciences research at ARC. Three nonhuman life-sciences disciplines (plant, rodent, and primate biology) were selected for this study. A total of 33 subjects viewed experimental scenes in their own scientific disciplines. Ten plant scientists viewed still images of wheat stalks at various stages of growth. Each image was compressed to four different compression levels using the Joint Photographic Expert Group (JPEG) standard algorithm, and the images were presented in random order. Twelve and eleven staffmembers viewed 30-sec videotaped segments showing small rodents and a small primate, respectively. Each segment was repeated at four different compression levels in random order using an inverse cosine transform (ICT) algorithm. Each viewer made a series of subjective image-quality ratings. There was a significant difference in image ratings according to the type of scene viewed within disciplines; thus, ratings were scene dependent. Image (still and motion) acceptability does, in fact, vary according to compression level. The JPEG still-image-compression levels, even with the large range of 5:1 to 120:1 in this study, yielded equally high levels of acceptability. In contrast, the ICT algorithm for motion compression yielded a sharp decline in acceptability below 768 kb/sec. Therefore, if video compression is to be used as a solution for overcoming transmission bandwidth constraints, the effective management of the ratio and compression parameters according to scientific discipline and experiment type is critical to the success of remote experiments.

Haines, Richard F.↗
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion

Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Analysis of the density field around a supersonic conical projectile using quantitative schlieren

Quantitative schlieren imaging is a flow measurement technique that is capable of measuring density fields throughout refractive flowfields. The technique was applied here to measure the density field surrounding supersonic conical projectiles in free flight. Shock waves attached to a supersonic conical projectile offer a simple geometry with the well-established Taylor–Maccoll analytical flow solution to which these experimental measurements were compared. The schlieren images recorded a projection of the index of refraction field surrounding the 10° half-angle cones which was converted to density first through an Abel inversion and then the Gladstone–Dale law. Three Abel inversion methods—two-point, three-point, and arbitrary ray axisymmetric projection (ARAP)—were applied to deconvolute the three-dimensional flow within the constrained axisymmetric flow field. The resulting reconstructed density profiles were compared to the Taylor–Maccoll solution, parameterized by cone geometry and Mach number. The experimental density fields demonstrated strong agreement with the theoretical profiles. Experimental consistency was confirmed across various projectile speeds, demonstrating quantitative schlieren’s capability to accurately reconstruct the density of the flow field, even within the resolution constraints imposed by high-speed imaging. In conclusion, an assessment of experimental uncertainties in the density reconstruction was performed.

Abel Inversion↗
DOE OSTI2020

Tomographic Reconstruction of the Neutron Time-Energy Spectrum from a Dense Plasma Focus

A dense plasma focus (DPF) nuclear fusion device is an attractive pulsed neutron source in many applications, due to its relatively large neutron yield, produced in a short time duration. In order to design a DPF that generates neutrons within a specified time profile, or generates a neutron energy spectrum with specific properties, it is necessary to be able to characterize and model the results of the fusion process releasing the neutrons. The time-energy spectrum of fusion neutrons is an ideal quantity to use to validate multiphysics codes that simulate the pinch and fusion processes, because it is a quantity that requires the physics of each of the stages leading up to and ending in the fusion reaction to be simulated correctly. In particular, since DPF fusion neutrons are not monoenergetic -- and there can often be several fusion pinches creating neutrons -- a computer simulation matching high quality neutron spectrum measurements provides great confidence in the fidelity of the simulation. In order to make such a comparison, it is first necessary to have quality measurements from which to infer the spectrum. In this work we pose neutron spectroscopy as the classical tomographic inverse problem from neutron time of flight data at multiple distances, but enhanced by using an additional measurement of the time profile of the fusion pinch near its source and by using detector pairs set up in a geometry that allows for scatter background subtraction. The detector pairs enhance the quality of the time of flight measurements, and the additional constraint posed by the measured time profile allows for reconstructions discretized as finely as the time measurements and in energy as finely as 100 keV, without the problem being underdetermined. We present results from a Deuterium-fueled DPF at the U.S. Department of Energy's Nevada National Security Site and show that we can infer the time-energy spectrum from our measurements for both single and multi-pinch fusion reactions with equal fidelity.

Catenacci, Jared↗
DOE OSTI2020

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗
DOE OSTI2021

Consensus Equilibrium for Subsurface Delineation

Heterogeneity and insufficient site characterization limit our knowledge of the subsurface. Inversion techniques, which minimize the mismatch between observations and model predictions, have become an essential tool of subsurface characterization. Most optimization-based approaches fail to incorporate various implicit priors and capture the geological complexity. We overcome these limitations by deploying the plug-and-play and consensus equilibrium (CE) strategies, which provide a flexible framework for image reconstruction. Our CE methodology for spatial delineation of geologic formations consists of an image denoiser and a variational auto-encoder (deep learning-based emulator). The former ameliorates the reconstruction noise, yielding well-defined geological structures; its mathematical equivalence with the proximal operator allows the deployment of advanced denoisers (e.g., CNN-based denoiser) that do not correspond to a regularization objective. The latter defines a geology prior that imposes a geological constraint, for example, continuity and shape of geological features, onto the reconstructed image. Here, we conduct a series of numerical experiments dealing with transient two-dimensional flow driven by a pumping well and natural hydraulic head gradient. They demonstrate the CE framework's ability to delineate, both probabilistically and deterministically, complex subsurface environments with sufficient quality.

42 ENGINEERING↗
DOE OSTI2022

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗