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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Centroidal Voronoi tessellation based methods for optimal rain gauge location prediction

With more satellite and model precipitation data becoming available, new analytical methods are needed that can take advantage of emerging data patterns to make well informed predictions in many hydrological applications. Herein we propose a new strategy where we extract precipitation variability patterns and use correlation map to build the resulting density map that serves as an input to centroidal Voronoi tessellation construction that optimizes placement of precipitation gauges. We provide results of numerical experiments based on the data from the Alto-Adige region in Northern Italy and Oklahoma and compare them against actual gauge locations. This method provides an automated way for choosing new gauge locations and can be generalized to include physical constraints and to tackle other types of resource allocation problems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Intercomparison of flood inundation models across land use types and hydrological flood stages

Flood Inundation Mapping (FIM) model selection is a key operational decision because accurate, rapid mapping underpins early warning and resource allocation. FIM performance is context-dependent and can vary with hydrograph phase, land-use/land-cover (LULC), and the evaluation benchmark. Intercomparison studies typically assess a single near-peak snapshot against one reference dataset. Here, we provide a context-stratified intercomparison across (i) multiple hydrograph phases, (ii) LULC classes, and (iii) benchmark types, for five FIM approaches spanning a wide range of physical complexity and operational cost (TRITON, LISFLOOD-FP, HEC-RAS 2D, ARC-Curve2Flood, and OWP HAND-FIM). We use the Hurricane Matthew flood (2016) in the Neuse River Basin, North Carolina, USA, as a case study. Using high-resolution remote sensing-derived flood inundation maps, hand-labeled points, and building footprints, we assess model skill across two rising and two falling hydrograph limbs and across major LULC types. Results show that model rankings shift systematically across contexts: LISFLOOD-FP ranks highest in three of four flood phases, while TRITON leads during one rising limb phase; LISFLOOD-FP performs best in vegetated areas, whereas HEC-RAS improves relative performance in agricultural and urban areas; and benchmark choice influences conclusions, with LISFLOOD-FP performing best for flooded-building detection in the late falling limb, while TRITON ranks highest against hand-labeled points. We also report representative wall-clock runtimes for each workflow to provide use-case context for operational feasibility. Together, these results offer transferable guidance for model selection and for designing large-scale, benchmark-aware FIM intercomparison studies.

Nikrou, Parvaneh [University of Alabama]↗
DOE OSTI2020

How fast can one resize a distributed file system?

Efficient resource utilization becomes a major concern as large-scale distributed computing infrastructures keep growing in size. Malleability, the possibility for resource managers to dynamically increase or decrease the amount of resources allocated to a job, is a promising way to save energy and costs. However, state-of-the-art parallel and distributed storage systems have not been designed with malleability in mind. The reason is mainly the supposedly high cost of data transfers required by resizing operations. Nevertheless, as network and storage technologies evolve, old assumptions about potential bottlenecks can be revisited. In this study, we evaluate the viability of malleability as a design principle for a distributed storage system. We specifically model the minimal duration of the commission and decommission operations. To show how our models can be used in practice, we evaluate the performance of these operations in HDFS, a relevant state-of-the-art distributed file system. We show that the existing decommission mechanism of HDFS is good when the network is the bottleneck, but can be accelerated by up to a factor 3 when storage is the limiting factor. We also show that the commission in HDFS can be substantially accelerated. With the highlights provided by our model, we suggest improvements to speed both operations in HDFS. We discuss how the proposed models can be generalized for distributed file systems with different assumptions and what perspectives are open for the design of efficient malleable distributed file systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Towards elastic in situ analysis for high-performance computing simulations

In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Joint Design of Multimodal Transit Networks and Shared Autonomous Mobility Fleets

Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced, and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the regions existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.

bi-level programming↗
DOE OSTI2020

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis

The capability of cyanobacteria to produce sucrose from CO 2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. Here we used targeted metabolomics, 13 C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13 C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

09 BIOMASS FUELS↗
DOE OSTI2021

Regionalized Life Cycle Greenhouse Gas Emissions of Forest Biomass Use for Electricity Generation in the United States

This study presents a cradle-to-grave life cycle analysis (LCA) of the greenhouse gas (GHG) emissions of the electricity generated from forest biomass in different regions of the United States (U.S.), taking into consideration regional variations in biomass availabilities and logistics. The regional biomass supply for a 20 MW bioelectricity facility is estimated using the Land Use and Resource Allocation (LURA) model. Results from LURA and data on regional forest management, harvesting, and processing are incorporated into the GHGs, Regulated Emissions, and Energy Use in Technologies (GREET) model for LCA. The results suggest that GHG emissions of mill residues-based pathways can be 15-52% lower than those of pulpwood-based pathways, with logging residues falling in between. Nonetheless, our analysis suggests that screening bioenergy projects on specific feedstock types alone is not sufficient because GHG emissions of a pulpwood-based pathway in one state can be lower than those of a mill residue-based pathway in another state. Furthermore, the available biomass supply often consists of several woody feedstocks, and its composition is region-dependent. Forest biomass-derived electricity is associated with 86-93% lower life-cycle GHG emissions than the emissions of the average grid electricity in the U.S. Key factors driving bioelectricity GHG emissions include electricity generation efficiency, transportation distance, and energy use for biomass harvesting and processing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. Here, this work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

Abstract The recent wildfires in the western United States during 2018 and 2020 caused record‐breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS‐SR) as a proof of concept to fill that void. The CFS‐SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning (DL) technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018–2019 fire season, the CFS‐SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county‐level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Informed Investments in Clean Energy Technologies

Governments and companies face consequential decisions about allocating resources to the research, development, demonstration and deployment of energy technologies to meet environmental, economic and social goals. Here we discuss how research insights can inform and potentially improve these decisions to make effective use of limited resources and time in shaping the next-generation energy infrastructure. We outline three key research steps: forecasting technological change, relating investments to economic, social and environmental outcomes and informing decision-making processes. We recommend advances to address uncertainty as well as to make methods and results more practicable, emphasizing the importance of model validation, streamlining and interactivity. Progress has been made, yet further work is needed-for example, in the development of reduced-order, testable models and more comprehensive data collection. Overall, this research is beginning to inform decisions but could be adopted more widely by governments and the private sector to help support technological progress for energy affordability, equitable climate change mitigation, health benefits and other objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Grid Strength Analysis for Integrating 30 GW of Offshore Wind Generation by 2030 in the U.S. Eastern Interconnection

Offshore wind is a key player in the transition to a decarbonized electric gird, and the United States has set ambitious goals of integrating 30 GW of offshore wind capacity by 2030 and 110 GW by 2050. To facilitate this integration, the National Renewable Energy Laboratory and the Pacific Northwest National Laboratory are conducting the Atlantic Offshore Wind Transmission Study to assess transmission solutions. To achieve the 110-GW target by 2050, meticulous planning for network expansion and resource allocation is essential; however, meeting the 2030 goals requires integrating offshore wind power with minimal system upgrades, thus necessitating a careful study of grid strength and stability. The study team developed the Automated System-wide Strength Evaluation Tool (ASSET) to assess system strength under various operating conditions and contingencies, focusing on the proposed integration of 30 GW of offshore wind power by 2030. In this paper, we provide a summary of key features of the ASSET software and results of the grid strength analysis for integrating 30 GW of offshore wind generation by 2030 in the U.S. Eastern Interconnection.

Automated System-wide Strength Evaluation Tool (AS↗
DOE OSTI2021

Installation Quality Framework: Investment Return Approach for Energy Savings on Building Product Installation

A case study was conducted on 44 residential homes using both traditional house wrap and ZIP System systems to measure the overall airtightness and compare estimated energy usages. The labor, material, and overhead and profit (O&P) costs were analyzed and used to determine the optimal choice for long-term benefits in terms of cost and performance. The impact of insulation installation is considered a key factor in improving the strategy of reducing energy consumption. Improved installation practices can affect the airtightness of common wall assemblies to reduce the building energy performance gaps and provide insight on how to allocate resources better. A framework was developed to analyze operational costs and building energy performance to address how installation quality is a factor in the return of investment in building construction for heating and cooling systems within the thermal envelope. With this methodology, aggressive energy performance goals will be met while balancing the tradeoff between installation techniques and building systems efficiency based on the introduced probabilistic investment return (PIR) metric.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗
DOE OSTI2025

Popnet : computer vision based deep learning model for forecasting gridded population

Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.

computer vision↗
DOE OSTI2024

Leveraging BERT and Network-Based Attention Analysis for Identifying Treatment Milestones in EHRs

This study introduces a sophisticated data-driven framework for analyzing Electronic Health Records (EHRs) using transformer-based models to identify and disentangle overlapping treatment contexts. The framework leverages a preprocessing pipeline that transforms structured procedural codes into semantically enriched descriptive text, enabling the use of attention mechanisms to cluster medical events into treatment milestones—cohesive and distinct components of care processes. The methodology is rigorously validated using synthetic datasets derived from the MIMIC-III database, designed to simulate the heterogeneity and overlapping procedural contexts characteristic of real-world EHR scenarios. Quantitative evaluation highlights the framework’s robustness in disentangling concurrent care pathways, with attention metrics and unsupervised clustering approaches demonstrating the ability to preserve intra-context relationships while distinguishing inter-context dependencies. By addressing challenges inherent in data heterogeneity, this approach provides a foundation for uncovering complex treatment patterns, advancing clinical decision-making, and optimizing resource allocation in diverse healthcare environments.

Kim, Minsu [ORNL] (ORCID:0000000224185535)↗