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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analysis of Defense Waste Processing Facility Sample: Off-Gas Condensate Tank Sample Batch 27862

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (now Savannah River Mission Completion (SRMC)), through a Technical Task Request (X-TTR-H-00107), to characterize the “as-received” Off-Gas Condensate Tank (OGCT) Sample identified as sample batch 27862, which was delivered to SRNL Shielded Cells on April 30, 2021. This OGCT report is the third of three sample characterization reports dealing with this Defense Waste Processing Facility (DWPF) Recycle Diversion Project. The other two recycling stream characterization reports are the Recycle Collection Tank (RCT) and the Slurry Mix Evaporator Condensate Tank (SMECT) sample report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

An Integrated Approach to Scaling Task-Based Runtime Systems for Next Generation Engineering problems

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. Such problems may have complex coupled multi-physics models, as well as computationally and communication intensive algorithms not often seen in standard academic problems, while also needing high-demand I/O for analysis purposes. In such cases even codes with good existing scaling properties may need significant changes, addressed in a cross-cutting way to solve such problems at extreme scales. These challenges relate to system components that were not previously problematic on less complex problems and/or at smaller computational scales. This paper illustrates these challenges for Uintah, a highly scalable asynchronous many-task runtime system being applied to the modeling of a 1000 MWe ultra-supercritical coal boiler. In order to model this formidable, production engineering problem, not only was an integrated approach needed that built upon existing scalable components in areas such as complex stencil calculations and linear algebra, but the significant challenges of high demand I/O, complex task graphs, and infrastructure inefficiencies also had to be addressed. This integrated approach allowed this problem to run on 256K CPU cores on Mira, with good weak scaling to 512K CPU cores and similar strong scaling, except for radiation. The need for strong scaling of a new ray tracing-based radiation model required substantial Uintah infrastructure improvements before 119K CPU cores and 7.5K GPUs on Titan could be used, with scaling out to 256K CPU cores and 16K GPUs. The resulting code demonstrates not only excellent overall scalability but a significant improvement in overall performance for the full-scale, production boiler problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2016

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons: multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.

Hong, Seokyong↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment FY2020: Part 3

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Sup- port for Advanced Non-Light Water Reactors.” The tasks completed for this report are: Task2c: Explicit modeling of pebble transient temperature response. In this simulation, the 400 MWth Pebble-Bed Modular Reactor (PBMR) design, PBMR- 400, experiences a 20-second power ramp from 100% to 150% power. This is followed by a similar reduction in the power back to 100%. Several multiscale pebble coupling approaches are tested with one pebble per mesh element in the active core region. The results show good conservation behavior and the stability of the coupling.; Extended scope part 1: An assessment of the computational efficiency of the Discontinuous Finite Element Method (DFEM) heat transfer solver shows good scalability. The DFEM solver is a factor of 4 more expensive in solution time than the Finite Element Method (FEM) solver for heat transfer problems due to the increased number of degrees of freedom. Nonetheless, the DFEM approach provides the user with the flexibility to model gap heat transfer problems.; Extended scope part 2: The GapHeatTransferInterfaceMaterial was improved to give the user increased flexibility with the modeling of heat transfer through gaps with the DFEM solver. A number of gap parameters can now be coupled both through functions and variables.; Extended scope part 3: Demonstration of how the gap width between hexagonal fuel cells can be calculated during a heat-up transient and used in the GapHeatTransferInterface model. A full-domain DFEM model with gap expansion is coupled to a SubApp that models the thermal expansion of the base plate. The results show the expected physical behavior, although have not been fully bench-marked at this point in time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Analysis of Defense Waste Processing Facility Sample: Slurry Mix Evaporator Condensate Tank Sample Batch 4638

The Savannah River National Laboratory (SRNL) was requested by Savannah River Remediation (SRR, now Savannah River Mission Completion (SRMC)), through a Technical Task Request, to characterization the “as-received” Slurry Mix Evaporator Condensate Tank (SMECT) sample identified as sample batch 4638, which was delivered to SRNL Shielded Cells on March 19, 2021. The SMECT characterization data will be used as input to the Defense Waste Processing Facility (DWPF) Recycle Diversion Project. This SMECT report is the second of three sample characterization reports that will be used for this DWPF Project. The other two DWPF characterization reports involve the characterization of the Recycle Collection Tank (RCT) sample and the future Off-Gas Condensate Tank (OGCT) characterization report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

New Insights into Structural Evolution of LiNiO 2 Revealed by Operando Neutron Diffraction

LiNiO 2 (LNO) represents the end member in the compositional space of the LiNi 1-x-y Mn x Co y O 2 (as x and y approach zero) cathode system. Despite its high theoretical specific capacity (275 mAh/g), LNO suffers from phase transitions with large volume change and unfavorable reactions upon electrochemical cycling, which restricts its practical use in the application of lithium-ion batteries. While the contributing factor to the structural instability is commonly linked to the undesired volume collapse associated with the H2-H3 phase transition, detailed analysis of structural evolution following the entire route of phase transitions (H1-M-H2-H3) in real time under battery operating conditions remains a challenging task. In this work, we employ operando neutron diffraction to study the structural changes (crystal lattice, Li/Ni–O bond length, O–Ni–O bond angles, and LiO 2 /NiO 2 layer thickness) of LNO cathode in a home-built Li x NiO 2 || graphite full cell during Li + de-/intercalation. In particular, the anomalous increase(decrease) of Ni–O(Li–O) bond length at high SOC (>~85 %) in the H3 phase is discussed in the context of O 2- (2p)→Ni 4+ (3d) negative charge transfer.

25 ENERGY STORAGE↗
DOE OSTI2019

eSHAFTS: Integrated and graphical drug design software based on 3D molecular similarity

With the development of computer technology, computer‐aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three‐dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug‐ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html . © 2019 Wiley Periodicals, Inc.

He, Gaoqi↗
DOE OSTI2024

A B-spline based gradient-enhanced micropolar implicit material point method for large localized inelastic deformations

The quasi-brittle response of cohesive-frictional materials in numerical simulations is commonly represented by softening plasticity or continuum damage models, either individually or in combination. However, classical models, particularly when coupled with non-associated plasticity, often suffer from ill-posedness and a lack of objectivity in numerical simulations. Moreover, the performance of the finite element method significantly degrades in simulations involving finite strains when mesh distortion reaches excessive levels. This represents a challenge for modeling cohesive-frictional materials, given their tendency to experience strongly localized deformations, such as those occurring during shear band dominated failure. Hence, accurate modeling of the response of cohesive-frictional solids is a demanding task. To address these challenges, we present an extension of the material point method (MPM) for the unified gradient-enhanced micropolar continuum, aiming at the analysis of finite localized inelastic deformations in cohesive-frictional materials. The generalized gradient-enhanced micropolar continuum formulation is employed to tackle challenges related to localization and softening material behavior, while the MPM addresses issues arising from excessive deformations. The method utilizes a B-spline formulation for the rigid background mesh to mitigate the well-known cell crossing errors of the MPM. To demonstrate the performance of the method, 2D and 3D numerical studies on localized failure in sandstone in plane strain compression and triaxial extension tests are presented. A comparison with finite element results confirms the suitability of the formulation. Moreover, an efficient numerical implementation of the formulation is presented, and it is demonstrated that the additional MPM specific overhead is negligible.

B-spline↗
DOE OSTI2016

Bayesian calibration of coarse-grained forces: Efficiently addressing transferability

Generating and calibrating forces that are transferable across a range of state-points remains a challenging task in coarse-grained (CG) molecular dynamics. In this work, we present a coarse-graining workflow, inspired by ideas from uncertainty quantification and numerical analysis, to address this problem. The key idea behind our approach is to introduce a Bayesian correction algorithm that uses functional derivatives of CG simulations to rapidly and inexpensively recalibrate initial estimates f{sub 0} of forces anchored by standard methods such as force-matching. Taking density-temperature relationships as a running example, we demonstrate that this algorithm, in concert with various interpolation schemes, can be used to efficiently compute physically reasonable force curves on a fine grid of state-points. Importantly, we show that our workflow is robust to several choices available to the modeler, including the interpolation schemes and tools used to construct f{sub 0}. In a related vein, we also demonstrate that our approach can speed up coarse-graining by reducing the number of atomistic simulations needed as inputs to standard methods for generating CG forces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗
DOE OSTI2017

Scaling Up Data-Parallel Analytics Platforms: Linear Algebraic Operation Cases

Linear algebraic operations such as matrix manipulations form the kernel of many machine learning and other crucial algorithms. Scaling up as well as scaling out such algorithms are key to supporting large scale data analysis that require efficient processing over millions of data samples. To this end, we present, ARION, a hardware acceleration based approach for scaling-up individual tasks of Spark, a popular data-parallel analytics platform. We support both linear algebraic operations of between two dense matrices, and between sparse and dense matrices in distributed environments. ARION provides a flexible control of acceleration according to matrix density, along with efficient scheduling based on runtime resource utilization. We demonstrate the benefit of our approach for general matrix multiplication operations over large matrices with up to four billion elements by using Gramian matrix computation that is commonly used in machine learning. Experiments show that our approach achieves more than 2× and 1.5× end-to-end performance speedups for dense and sparse matrices, respectively, and up to 57.04× faster computation compared to MLlib, a state of the art Spark-based implementation. This work is sponsored in part by the NSF under the grants: CNS-1565314, CNS-1405697, and CNS-1615411. The manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Xu, Luna↗
DOE OSTI2014

Compiler Optimization for Extreme-Scale Scripting

The data-driven task parallelism execution model can support parallel programming models that are well suited for large-scale distributed-memory parallel computing, for example, simulations and analysis pipelines running on clusters and clouds. We describe a novel compiler intermediate representation and optimizations for this execution model, including adaptions of standard techniques alongside novel techniques. These techniques are applied to Swift/T, a high-level scripting language for flexible data flow composition of functions, which may be serial or use lower-level parallel programming models such as MPI and OpenMP. This paper presents preliminary results, indicating that our compiler optimizations reduce communication overhead by 70% to 93% on distributed-memory systems.

Armstrong, Timothy G.↗
DOE OSTI2019

ADVERT: An Asynchronous Runtime for Fine-Grained Network Systems

The Data Vortex Network, a novel fine-grained high-radix interconnect, provides the performance and injection rates required by the fine-grained, unpredictable communication patterns of irregular applications. This paper presents ADVERT, an asynchronous runtime system that provides performance and productivity for the fine-grained Data Vortex Network. ADVERT integrates a lightweight memory manager (DVMem) for the user accessible SRAM integrated in the network interface, and a communication library (DVComm) that implements active messaging primitives (remote execution, and, based on this, get and put routines). ADVERT hides the complexity of controlling all the network hardware features through the low-level Data Vortex programming interface, while providing comparable performance. We discuss ADVERT's design and present an analysis of three exemplar irregular kernels (Triangle Count, Page Rank, and GUPS). ADVERT can provide the communication substrate required to build emerging higher level asynchronous many tasking runtimes and partitioned global address space libraries on top of the Data Vortex Network.

Friese, Ryan D.↗
DOE OSTI2014

Acciona Solar Technology Performance Evaluation: Cooperative Research and Development Final Report, CRADA Number CRD-10-384

Under this agreement, NREL will work with Acciona to conduct joint testing, evaluation, and data collection related to Acciona's solar technologies and systems. This work includes, but is not limited to, testing and evaluation of solar component and system technologies, data collection and monitoring, performance evaluation, reliability testing, and analysis. This work will be conducted at Acciona's Nevada Solar One (NSO) power plant and NREL test facilities. Specific projects will be developed on a task order basis. Each task order will identify the name of the project and deliverables to be produced under the task order. Each task order will delineate an estimated completion date based on a project's schedule. Any reports developed under this CRADA must be reviewed by both NREL and Acciona and approved by each organization prior to publication of results or documents.

14 SOLAR ENERGY↗
DOE OSTI2016

Operational Simulation Tools and Long Term Strategic Planning for High Penetrations of PV in the Southeastern United States

Increasing levels of distributed and utility scale Solar Photovoltaics (PV) will have an impact on many utility functions, including distribution system operations, bulk system performance, business models and scheduling of generation. In this project, EPRI worked with Southern Company Services and its affiliates and the Tennessee Valley Authority to assist these utilities in their strategic planning efforts for integrating PV, based on modeling, simulation and analysis using a set of innovative tools. Advanced production simulation models were used to investigate operating reserve requirements. To leverage existing work and datasets, this last task was carried out on the California system. Overall, the project resulted in providing useful information to both of the utilities involved and through the final reports and interactions during the project. The results from this project can be used to inform the industry about new and improved methodologies for understanding solar PV penetration, and will influence ongoing and future research. This report summarizes each of the topics investigated over the 2.5-year project period.

14 SOLAR ENERGY↗
DOE OSTI2016

New Aqueous Binders for Lithium-ion Batteries

This final report summarizes the research effort of the CRADA between PPG Industries and Argonne National Laboratory (CRADA # C1400501 and Amendment 1 - ACK 85C11, Rev. 1), and completes the requirements of Task #5 in the CRADA. The results from Argonne represent a combined effort between the MERF and CAMP Facility (Materials Engineering Research Facility and the Cell Analysis, Modeling and Prototyping Facility). The key points of this report can be briefly summarized as: The multicomponent aqueous binder for NCM cathodes developed by PPG shows promising results when used on the CAMP Facility’s pilot-scale coater; NCM523 cathode electrodes exposed to water exhibit a voltage anomaly in the first two formation cycles that appears to have little effect on the material capacity, but needs to be explored further; the experimental LFP electrode produced by PPG does cycle electrochemically, but with reduced capacity and an uncharacteristic voltage profile; there is no obvious correlation between the pH and zeta-potential of materials that are commonly used in the lithium-ion battery; lithium ions are more readily dissolved out of NCM523 by water than the transition metals (by two orders of magnitude), and suggests that the plating bath may need to be spiked with a lithium salt; the approach of creating sub-micron size NCM523 particles via ball milling results in a cathode material with poor electrochemical performance.

25 ENERGY STORAGE↗