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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A reactive tracer method for predicting EGS reservoir geometry and thermal lifetime: development and field validation

The project summarized here (DOE Award No. DE-EE0006764) was intended to develop a methodology for predicting advective heat transfer in fracture-dominated crystalline rock. Our goal was to determine if a combination of inert and reactive tracers could adequately constrain the effective heat transfer surface area between an injection-production well pair. Our approach consisted of: 1. developing a novel computational framework; 2. Performing heat and tracer experiments at meso-scale; and 3. Comparing predictions of advective heat transfer to the “true” thermal breakthrough measured at the Altona site. Below is a summary of project activities/findings, a summary of project tasks, and a conclusion

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Comparison of Zirconium Redistribution in BISON EBR-II Models Using FIPD and IMIS Databases with Experimental Post Irradiation Examination

Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Experimental spectra analysis in THM with the help of simulation based on the Geant4 framework

The Coulomb barrier and electron screening cause difficulties in directly measuring nuclear reaction cross sections of charged particles in astrophysical energies. The Trojan-horse method has been introduced to solve the difficulties as a powerful indirect tool. In order to understand experimental spectra better, Geant4 is employed to simulate the method for the first time. Validity and reliability of the simulation are examined by comparing the experimental data with simulated results. Lastly, the Geant4 simulation can give useful information to understand the experimental spectra better in data analysis and is beneficial to the design for future related experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Final report of a scalable, automated, semipermanent seismic array (SASSA) method for detecting CO 2 extent during geologic CO 2 injection

The scalable, automated, semipermanent seismic array (SASSA) project led and managed by the Energy & Environmental Research Center (EERC) was designed as a 3-year proof-of-concept study to evaluate and demonstrate an innovative application of the seismic method. The concept was to use a sparse surface array of 96 nodal seismic sensors paired with a single, remotely operated active seismic source at a fixed location to monitor for CO 2 saturation changes in a subsurface reservoir by processing the data for time-lapse changes at individual, strategically chosen reservoir reflection points. The combination of autonomous equipment and modern processing algorithms was used to apply the seismic method in a manner different from the normal paradigm of collecting a spatially dense data set to produce an image. It was used instead to monitor individual, strategically chosen reservoir reflection points for detectable signal character changes that could be attributed to the passing of a CO 2 saturation front or, possibly, changes in reservoir pressure. Data collection occurred over the course of 1 year at an oil field undergoing CO 2 injection for enhanced oil recovery (EOR) and focused on four overlapping “five-spot” EOR injector–producer patterns. Selection, procurement, configuration, installation, and testing of project equipment and collection of five baseline data sets were completed in advance of CO 2 injection within the study area. Weekly remote data collection produced 41 incremental time-lapse records for each of the 96 nodes. Validation was provided by two methods: 1) a conventional 2-D seismic line acquired through the center of the study area before injection started and again after the project ended and processed in a time-lapse manner and 2) by CO 2 saturation maps created from reservoir simulations based on injection and production history matching. Interpreted results were encouraging but mixed, with indications of changes likely due to the presence of CO 2 on some node reflection points where and when effects would be expected and noneffects where no CO 2 was expected, while results at some locations where simulation outputs suggested CO 2 should be present were ambiguous. Acquisition noise impacted interpretation of data at several locations. Many lessons learned were generated by the study to inform and improve results on a follow-up study. The ultimate aim of the project was to evaluate whether deployment of a SASSA technology can provide a useful and cost-effective monitoring solution for future CO 2 injection projects. The answer appears to be affirmative, with the expectation that lessons learned applied to future iterations, together with technology advances, will likely result in significant improvements.

58 GEOSCIENCES↗
DOE OSTI2016

Determination of errors in derived magnetic field directions in geosynchronous orbit: results from a statistical approach

This study aims to statistically estimate the errors in local magnetic field directions that are derived from electron directional distributions measured by Los Alamos National Laboratory geosynchronous (LANL GEO) satellites. First, by comparing derived and measured magnetic field directions along the GEO orbit to those calculated from three selected empirical global magnetic field models (including a static Olson and Pfitzer 1977 quiet magnetic field model, a simple dynamic Tsyganenko 1989 model, and a sophisticated dynamic Tsyganenko 2001 storm model), it is shown that the errors in both derived and modeled directions are at least comparable. Second, using a newly developed proxy method as well as comparing results from empirical models, we are able to provide for the first time circumstantial evidence showing that derived magnetic field directions should statistically match the real magnetic directions better, with averaged errors< ~2°, than those from the three empirical models with averaged errors >~5°. In addition, our results suggest that the errors in derived magnetic field directions do not depend much on magnetospheric activity, in contrast to the empirical field models. Finally, as applications of the above conclusions, we show examples of electron pitch angle distributions observed by LANL GEO and also take the derived magnetic field directions as the real ones so as to test the performance of empirical field models along the GEO orbits, with results suggesting dependence on solar cycles as well as satellite locations. This study demonstrates the validity and value of the method that infers local magnetic field directions from particle spin-resolved distributions.

58 GEOSCIENCES↗
DOE OSTI2019

Machine learning based design space exploration for hybrid main-memory design

We develop a machine learning (ML) based design space exploration (DSE) method that builds predictive models for various responses of a hybrid main-memory system. To overcome the challenges associated with latency, capacity, and power of memory systems in future extreme-scale machines, the hybrid memory architectures are being considered in which novel non-volatile memory (NVM) systems augment the traditional DRAM. However, way before their actual design and implementation, these emerging hybrid memory systems need to be simulated and analyzed to fully understand their capabilities and limitations. As the conventional architectural-level memory simulators require significant amounts of computational costs and time, we propose to utilize ML techniques for developing various memory-response models that can instantly provide a predicted response corresponding to any new memory configuration. Specifically, in this work, we apply four supervised ML techniques to build regression models for memory latency, bandwidth, power, and total read/write responses. The training and validation data for the ML methods are generated using NVMain memory simulator for DRAM, NVM, and their hybrid combinations. We demonstrate the results of the ML based memory-DSE method in terms of the learning curve characteristics for hyperparameter tuning and the statistical error analyses of the designed predictive models.

Sen, Satyabrata↗
DOE OSTI2022

Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review

Recent years have seen a substantial increase in the application of machine learning (ML) for automated analysis of nondestructive examination (NDE) data. One of the applications of interest is the use of ML for the analysis of data from in-service inspection of welds in nuclear power and other industries. These types of inspections are performed in accordance with criteria described in the ASME Boiler and Pressure Vessel Code and require the use of reliable NDE techniques. The rapid growth in ML methods and the diversity of possible approaches indicate a need to assess the current capabilities of ML and automated data analysis for NDE and identify any gaps or shortcomings in current ML technologies as applied to the automated analysis of NDE data. In particular, there is a need to determine the impact of ML on the NDE reliability. This paper discusses the findings from a literature survey on the current state of ML for the automated analysis of data from ultrasonic NDE of weld flaws. It discusses an overview of ultrasonic NDE as used for weld inspections in nuclear power and other industries. Herein, data sets and ML models used in the literature are summarized, along with a generally applicable workflow for ML. Findings on the capabilities, limitations and potential gaps in feature selection, data selection, and ML model optimization are discussed. The paper identified several needs for quantifying and validating the performance of ML methods for ultrasonic NDE, including the need for common data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Muon capture in nuclei: An ab initio approach based on Green's function Monte Carlo methods

An ab initio Green's function Monte Carlo (GFMC) method is introduced for calculating total rates of muon weak capture in light nuclei with mass number $A ≤ 12$. As a first application of the method, we perform a calculation of the rate in $^3$$\textbf{H}$ and $^4$$\textbf{He}$ in a dynamical framework based on realistic two- and three-nucleon interactions and realistic nuclear charge-changing weak currents. The currents include one- and two-body terms induced by $π$- and $ρ$-meson exchange, and $N$-to-$Δ$ excitation, and are constrained to reproduce the empirical value of the Gamow-Teller matrix element in tritium. We investigate the sensitivity of theoretical predictions to current parametrizations of the nucleon axial and induced pseudoscalar form factors as well as to two-body contributions in the weak currents. The large uncertainties in the measured $^4$$\textbf{He}$ rates obtained from bubble-chamber experiments (carried out over 50 years ago) prevent us from drawing any definite conclusions. No data exist for $^3$$\textbf{H}$, but results are compared to those of a recent Faddeev calculation as a validation of the present GFMC method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

VTO FY20Q4 quarterly report

Project Objective.This project has three objectives: (1) tuning microstructures of 3D-printed SSE separators,(2) determining material and processing compatibilities with cathode printing,and (3)3D printing of sintering-free SSE separators.Project Impact.All-solid-state lithium batteries are difficult to process due to the brittleness of ceramic materials, poor solid-solid contact, and electrolyte-electrode stability issues. As a result, the energy and power density and also cycling stability are far from satisfying. This project will address fabrication difficulties by using state-of-the-art 3D-printing techniques that can introduce 3D interfaces and architectures to enhance solid-solid contact and reduce charge transfer resistance. Success will benefit the DOE by establishing the best manufacturing methods for ASSBsto achieve VTO goals on the performance of beyond Li-ion batteries. Approach.The project employs 3D-printing techniques to manufacture SSEs and related components for ASSBs. The team starts with direct ink writing (DIW) to develop ink recipes with desired rheological properties and explore post-sintering approaches to achieve high densification. In parallel with DIW3D printing, the team also explores other 3D printing options, such as projection microstereolithography (PuSL). Both sintering and sintering-free approaches can gain benefit from 3D printing and therefore will be investigated.Out-Year Goals.The team will determine particle sizes and morphologies as well as post-processing conditions that deliver good ionic conductivity and charge transfer properties of LLZTO separators. They will examine co-sintering of LLZTO/cathode and determine the effects of material, particle size, surface coating, and sintering conditions.Collaborations.Microstructures, ionic conductivities, and mechanical properties will be provided to the LLNL simulation team, led by PI B. Wood, for establishing and validating phase-field modeling methods.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Ice-Sheet / Ocean Interaction Model for Greenland Fjords Using High-Order Discontinuous Galerkin Methods (Final Report)

This report summarizes the development and validation of the Non-hydrostatic Unified Model of the Ocean (NUMO). The overarching science goal of the project was to advance Earth System science by proposing a more realistic representation of physical processes in a Greenland fjord. To achieve this objective, we proposed to build a new, separate, high-resolution ocean model to realistically represent the complex three-dimensional physics of the fjord, its bathymetry, coastlines using continuous/discontinuous Galerkin (CG/DG) methods. The report describes the technical details of model components, i.e., choice of equations set, time integration scheme, boundary conditions, spatial discretization methods, mesh generation. We show a suite of validation results for test cases ranging from the Kovasznay flow, through the lock-exchange, simplified two-dimensional fjord, to a three-dimensional density current on a slope. Results are compared with the literature. The report is concluded with a discussion of efforts taken to perform a simulation of Sermilik Fjord with realistic bathymetry data.

58 GEOSCIENCES↗
DOE OSTI2015

Computer-aided pulmonary image analysis in small animal models

Purpose: To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. Methods: The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors’ system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. Results: 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT’09 data set. Conclusions: The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Validating Nuclear Data Uncertainties Obtained from a Statistical Analysis of Experimental Data with the “Physical Uncertainty Bounds” Method

Concerns within the nuclear data community led to substantial increases of Neutron Data Standards (NDS) uncertainties from its previous to the current version. For example, those associated with the NDS reference cross section 239 Pu(n,f) increased from 0.6–1.6% to 1.3–1.7% from 0.1–20 MeV. These cross sections, among others, were adopted, e.g., by ENDF/B-VII.1 (previous NDS) and ENDF/B-VIII.0 (current NDS). There has been a strong desire to be able to validate these increases based on objective criteria given their impact on our understanding of various application uncertainties. Here, the “Physical Uncertainty Bounds” method (PUBs) by Vaughan et al. is applied to validate evaluated uncertainties obtained by a statistical analysis of experimental data. We investigate with PUBs whether ENDF/B-VII.1 or ENDF/B-VIII.0 239 Pu(n,f) cross-section uncertainties are more realistic given the information content used for the actual evaluation. It is shown that the associated conservative (1.5–1.8%) and minimal realistic (1.1–1.3%) uncertainty bounds obtained by PUBs enclose ENDF/B-VIII.0 uncertainties and indicate that ENDF/B-VII.1 uncertainties are underestimated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage

Metal–organic frameworks (MOFs) are porous materials synthesized by combining inorganic and organic molecular building blocks into crystalline networks of distinct topologies. Due to the combinatorial possibilities, there are millions of possible MOF structures. Aiming to exploit their exceptional tunability, surface areas and pore volumes, researchers have investigated MOFs for storage of gaseous fuels such as hydrogen for over a decade, but a suitable MOF to store hydrogen at ambient conditions has not yet been found. Here, we sought to rapidly determine the viability of using MOFs for hydrogen storage at recently proposed, cryogenic operating conditions. We constructed a large and structurally diverse set of 13 512 potential MOF structures based on 41 different topologies and used molecular simulation to determine MOF hydrogen deliverable capacities between 100 bar/77 K and 5 bar/160 K. The highest volumetric deliverable capacity was 57 g L-1 of MOF, which surpasses the 37 g L-1 of tank of the incumbent technology (compressing hydrogen to 700 bar at ambient temperature). To validate our in silico MOF construction method, we synthesized a new isoreticular family of MOFs (she-MOF-x series) based on the she topology, which is extremely rare among MOFs. To validate our hydrogen storage predictions, we activated and measured hydrogen adsorption on she-MOF-1 and NU-1103. The latter MOF showed outstanding stability and a good combination of volumetric and gravimetric performance, presenting 43.2 g L-1 of MOF and 12.6 wt% volumetric and gravimetric deliverable capacities, respectively.

08 HYDROGEN↗
DOE OSTI2014

Uncertainty-based Estimation of the Secure Range for ISO New England Dynamic Interchange Adjustment

The paper proposes an approach to estimate the secure range for dynamic interchange adjustment, which assists system operators in scheduling the interchange with neighboring control areas. Uncertainties associated with various sources are incorporated. The proposed method is implemented in the dynamic interchange adjustment (DINA) tool developed by Pacific Northwest National Laboratory (PNNL) for ISO New England. Simulation results are used to validate the effectiveness of the proposed method.

forecast errors↗
DOE OSTI2015

Multiscale modeling and characterization for performance and safety of lithium-ion batteries

Lithium-ion batteries are highly complex electrochemical systems whose performance and safety are governed by coupled nonlinear electrochemical-electrical-thermal-mechanical processes over a range of spatiotemporal scales. Gaining an understanding of the role of these processes as well as development of predictive capabilities for design of better performing batteries requires synergy between theory, modeling, and simulation, and fundamental experimental work to support the models. This paper presents the overview of the work performed by the authors aligned with both experimental and computational efforts. In this paper, we describe a new, open source computational environment for battery simulations with an initial focus on lithium-ion systems but designed to support a variety of model types and formulations. This system has been used to create a three-dimensional cell and battery pack models that explicitly simulate all the battery components (current collectors, electrodes, and separator). The models are used to predict battery performance under normal operations and to study thermal and mechanical safety aspects under adverse conditions. This paper also provides an overview of the experimental techniques to obtain crucial validation data to benchmark the simulations at various scales for performance as well as abuse. We detail some initial validation using characterization experiments such as infrared and neutron imaging and micro-Raman mapping. In addition, we identify opportunities for future integration of theory, modeling, and experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

A Fully Automated Method for CT-on-Rails-Guided Online Adaptive Planning for Prostate Cancer Intensity Modulated Radiation Therapy

Purpose: This study was designed to validate a fully automated adaptive planning (AAP) method which integrates automated recontouring and automated replanning to account for interfractional anatomical changes in prostate cancer patients receiving adaptive intensity modulated radiation therapy (IMRT) based on daily repeated computed tomography (CT)-on-rails images. Methods and Materials: Nine prostate cancer patients treated at our institution were randomly selected. For the AAP method, contours on each repeat CT image were automatically generated by mapping the contours from the simulation CT image using deformable image registration. An in-house automated planning tool incorporated into the Pinnacle treatment planning system was used to generate the original and the adapted IMRT plans. The cumulative dose–volume histograms (DVHs) of the target and critical structures were calculated based on the manual contours for all plans and compared with those of plans generated by the conventional method, that is, shifting the isocenters by aligning the images based on the center of the volume (COV) of prostate (prostate COV-aligned). Results: The target coverage from our AAP method for every patient was acceptable, while 1 of the 9 patients showed target underdosing from prostate COV-aligned plans. The normalized volume receiving at least 70 Gy (V{sub 70}), and the mean dose of the rectum and bladder were reduced by 8.9%, 6.4 Gy and 4.3%, 5.3 Gy, respectively, for the AAP method compared with the values obtained from prostate COV-aligned plans. Conclusions: The AAP method, which is fully automated, is effective for online replanning to compensate for target dose deficits and critical organ overdosing caused by interfractional anatomical changes in prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗