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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GraphTango: A Hybrid Representation Format for Efficient Streaming Graph Updates and Analysis

Abstract Streaming graph processing performs batched updates and analytics on a time-evolving graph. The underlying representation format of the graph largely determines the throughputs of these updates and analytics phases. Existing representation formats usually employ variations of hash tables or adjacency lists. However, a recent study showed that the adjacency-list-based approaches perform poorly on heavy-tailed graphs, and the hash table-based approaches suffer on short-tailed graphs. We propose GraphTango, a hybrid representation format that provides excellent update and analytics throughput regardless of the graph’s degree distribution. GraphTango dynamically switches among three different formats based on a vertex’s degree: (i) Low-degree vertices store the edges directly with the neighborhood metadata, confining accesses to a single cache line, (2) Medium-degree vertices use adjacency lists, and (3) High-degree vertices use hash tables as well as adjacency lists. In this case, the adjacency list provides fast traversal during the analytics phase, while the hash table provides constant-time lookups during the update phase. We further optimized the performance by designing an open-addressing-based hash table that fully utilizes every fetched cache line. In addition, we developed a thread-local lock-free memory pool that allows fast growing/shrinking of the adjacency lists and hash tables in a multi-threaded environment. We evaluated GraphTango with the help of the SAGA-Bench framework and compared it with four other representation formats: Stinger, Degree-aware Robin Hood Hashing, and two adjacency list-based formats with different workload balancing scheme. On average, GraphTango provides 4.5x higher insertion throughput, 3.2x higher deletion throughput, and 1.1x higher analytics throughput over the next best format. Furthermore, we integrated GraphTango with the state-of-the-art graph processing frameworks DZiG and RisGraph. Compared to the vanilla DZiG and vanilla RisGraph , [ GraphTango + DZiG ] and [ GraphTango + RisGraph ] reduces the average batch processing time by 2.3x and 1.5x, respectively.

Ahmed, Alif↗
DOE OSTI2019

The Klein–Gordon Oscillator in (1+2)-dimensions Gurses space–time backgrounds

In this article, we investigate the relativistic quantum dynamics of spin-0 massive charged-free particles in (1+2)-dimensions Gurses space–time Gurses (1994) backgrounds, and analyze the effects on the energy eigenvalue. We solve the Klein–Gordon Oscillator in the considered framework without interactions using the Nikiforov–Uvarov (NU) method and evaluate the energy eigenvalues and corresponding eigenfunctions, in detail.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Non-dimensional performance and safety parameters for heat pipes

The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.

42 - ENGINEERING↗
DOE OSTI2014

Addressing trend-related changes within cumulative effects studies in water resources planning

Summarized herein are 28 case studies wherein trend-related causative physical, social, or institutional changes were connected to consequential changes in runoff, water quality, and riparian and aquatic ecological features. The reviewed cases were systematically evaluated relative to their identified environmental effects; usage of analytical frameworks, and appropriate models, methods, and technologies; and the attention given to mitigation and/or management of the resultant causative and consequential changes. These changes also represent important considerations in project design and operation, and in cumulative effects studies associated therewith. The cases were grouped into five categories: institutional changes associated with legislation and policies (seven cases); physical changes from land use changes in urbanizing watersheds (eight cases); physical changes from land use changes and development projects in watersheds (four cases); physical, institutional, and social changes from land use and related policy changes in river basins (three cases); and multiple changes within a comprehensive study of land use and policy changes in the Willamette River Basin in Oregon (six cases). A tabulation of 110 models, methods and technologies used in the studies is also presented. General observations from this review were that the features were unique for each case; the consequential changes were logically based on the causative changes; the analytical frameworks provided relevant structures for the studies, and the identified methods and technologies were pertinent for addressing both the causative and consequential changes. One key lesson was that the cases provide useful, “real-world” illustrations of the importance of addressing trend-related changes in cumulative effects studies within water resources planning. Accordingly, they could be used as an “initial tool kit” for addressing trend-related changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Combination of life cycle assessment, risk assessment and human biomonitoring to improve regulatory decisions and policy making for chemicals

Prior to market entry, new chemical substances are assessed for their risk to human health and the environment. Conventional risk assessment (RA) is limited in scope, i.e. it usually does not cover the entire life cycle of a substance, nor does it take into account sustainability aspects such as the amount of raw materials and energy required to produce the substance. Life cycle assessment (LCA) can provide this pivotal information to support an informed decision on the sustainability of a new substance. Unfortunately, LCA has had little regulatory application up to now. We believe that increasing the focus on combined use of LCA and life cycle-based RA could lead to improved regulatory long-term decisions for marketed chemicals. Inclusion of human biomonitoring could increase the robustness of such decisions even further. In addition, the combined use of the three methods allows a robust search for sustainable alternatives of currently marketed chemicals that have an unfavourable risk profile. - Highlights: •We recommend a framework of three approaches for a chemical safety assessment. •LCA evaluates the sustainability of chemicals in comparison to alternatives. •Human biomonitoring allows the assessment of total exposure to critical chemicals. •A case study on nanomaterials demonstrates the value of the combined approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗
DOE OSTI2022

Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey

The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°~31° N, 77°~98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. In conclusion, this modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of Al location on formation of silver clusters in mordenite

Formation of zeolite supported Ag 0 clusters depends on a combination of thermodynamically stable atomic configurations, charge balance considerations, and mobility of species on the surface and within pores. Periodic density functional theory (DFT) calculations were performed to evaluate how the location of Al in the mordenite (MOR) framework and humidity control Ag 0 nanocluster formation. Four Al framework sites were studied (T1-T4) and the Al positions in the framework were identified by the shifts in the differential Al…Al pair distribution function (PDF). Furthermore, structural information about the Ag 0 nanoclusters, such as dangling bonds, can be identified by Ag…Ag PDF data. For Ag 0 formation in vacuum MOR structures with a Si:Al ratio of 5:1 with Al in the T1 position resulted in the most framework flexibility and the lowest Ag 0 nanocluster charge, indicating the best result for formation of charge neutral nanoclusters. When water is present, Al in the T3 and T4 positions results in the formation of the smallest average Ag 0 nanoclusters plus greater expansion of the O-T-O bond angle than in vacuum, indicating easier diffusion of the Ag 0 nanoclusters to the surface. Here, the presence of Al in 4-membered rings and in pairs indicates favorable MOR structures for formation of single Ag atoms, despite the existence of synthesis challenges. Therefore, Al in the T2 position is the least favorable for Ag 0 nanocluster formation in both vacuum and in the presence of water. Al in the T1, T3, and T4 positions provides beneficial effects through framework flexibility and changes in nanocluster size or charge that can be leveraged for design of zeolites for formation of metallic nanoclusters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗
DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Gray two-moment neutrino transport: Comprehensive tests and improvements for supernova simulations

Aims. In this work we extended an energy-integrated neutrino transport method to facilitate efficient, yet precise, modeling of compact astrophysical objects. We particularly focus on core-collapse supernovae. Methods. We implemented a gray neutrino-transport framework from the literature into FLASH and performed a detailed evaluation of its accuracy in core-collapse supernova simulations. Based on comparisons with results from simulations using energy-dependent neutrino transport, we incorporated several improvements to the original scheme. Results. Our analysis shows that our gray neutrino transport method successfully reproduces key aspects from more complex energy-dependent transport across a variety of progenitors and equations of state. We find both qualitative and reasonable quantitative agreement with multi-group M1 transport simulations. However, the gray scheme tends to slightly favor shock revival. In terms of gravitational wave and neutrino signals, there is a good alignment with the energy-dependent transport, although we find 15–30% discrepancies in the average energy and luminosity of heavy-lepton neutrinos. Simulations using the gray transport are around four times faster than those using energy-dependent transport.

Astronomy & Astrophysics↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2012

Dark Matter searches: the nightmare scenario

The unfortunate case where the Large Hadron Collider (LHC) fails to discover physics Beyond the Standard Model (BSM) is sometimes referred to as the ''Nightmare scenario'' of particle physics. We study the consequences of this hypothetical scenario for Dark Matter (DM), in the framework of the constrained Minimal Supersymmetric Standard Model (cMSSM). We evaluate the surviving regions of the cMSSM parameter space after null searches at the LHC, using several different LHC configurations, and study the consequences for DM searches with ton-scale direct detectors and the IceCube neutrino telescope. We demonstrate that ton-scale direct detection experiments will be able to conclusively probe the cMSSM parameter space that would survive null searches at the LHC with 100 fb{sup −1} of integrated luminosity at 14 TeV. We also demonstrate that IceCube (80 strings plus DeepCore) will be able to probe as much as ≅ 17% of the currently favoured parameter space after 5 years of observation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Developing IEEE Std 2800-Compliant Algorithms for Transmission-Connected Inverter-Based Resources

This study addresses the compliance of Inverter-based Resources (IBRs) with IEEE Standard 2800, a leading standard that defines interconnection and interoperability requirements for IBRs integrated into transmission systems. Focusing on abnormal grid scenarios, the research evaluates the specific demands on IBRs, proposing a controller development framework for abnormal grid conditions. This framework caters to maintaining ride-through operation in line with IEEE Std 2800, alongside managing currents during voltage ride-through scenarios. The effectiveness of this proposed controller frame-work is rigorously validated through case studies, employing a MATLAB/Simulink model of an IBR to test its performance under diverse grid fault conditions, ensuring the IBRs' alignment with standard requirements and their robust performance in enhancing grid reliability.

IEEE Std 2800↗
DOE OSTI2011

Quantitative PET Imaging Using A Comprehensive Monte Carlo System Model

We present the complete image generation methodology developed for the RatCAP PET scanner, which can be extended to other PET systems for which a Monte Carlo-based system model is feasible. The miniature RatCAP presents a unique set of advantages as well as challenges for image processing, and a combination of conventional methods and novel ideas developed specifically for this tomograph have been implemented. The crux of our approach is a low-noise Monte Carlo-generated probability matrix with integrated corrections for all physical effects that impact PET image quality. The generation and optimization of this matrix are discussed in detail, along with the estimation of correction factors and their incorporation into the reconstruction framework. Phantom studies and Monte Carlo simulations are used to evaluate the reconstruction as well as individual corrections for random coincidences, photon scatter, attenuation, and detector efficiency variations in terms of bias and noise. Finally, a realistic rat brain phantom study reconstructed using this methodology is shown to recover >; 90% of the contrast for hot as well as cold regions. The goal has been to realize the potential of quantitative neuroreceptor imaging with the RatCAP.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Reduce Operations: Send Volume Balancing While Minimizing Latency

Communication hypergraph model was introduced in a two-phase setting for encapsulating multiple communication cost metrics (bandwidth and latency), which are proven to be important in parallelizing irregular applications. In the first phase, computational-task-to-processor assignment is performed with the objective of minimizing total volume while maintaining computational load balance. In the second phase, communication-task-to-processor assignment is performed with the objective of minimizing total number of messages while maintaining communication-volume balance. The reduce-communication hypergraph model suffers from failing to correctly encapsulate send-volume balancing. We propose a novel vertex weighting scheme that enables part weights to correctly encode send-volume loads of processors for send-volume balancing. The model also suffers from increasing the total communication volume during partitioning. To decrease this increase, we propose a method that utilizes the recursive bipartitioning framework and refines each bipartition by vertex swaps. For performance evaluation, we consider column-parallel SpMV, which is one of the most widely known applications in which the reduce-task assignment problem arises. Extensive experiments on 313 matrices show that, compared to the existing model, the proposed models achieve considerable improvements in all communication cost metrics. Furthermore, these improvements lead to an average decrease of 30% in parallel SpMV time on 512 processors for 70 matrices with high irregularity.

97 MATHEMATICS AND COMPUTING↗