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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Engineering Innovations, Challenges, and Opportunities for Lignocellulosic Biorefineries: Leveraging Biobased Polymer Production

Alternative polymer feedstocks are highly desirable to address environmental, social, and security concerns associated with petrochemical-based materials. Lignocellulosic biomass (LCB) has emerged as one critical feedstock in this regard because it is an abundant and ubiquitous renewable resource. LCB can be deconstructed to generate valuable fuels, chemicals, and small molecules/oligomers that are amenable to modification and polymerization. However, the diversity of LCB complicates the evaluation of biorefinery concepts in areas including process scale-up, production outputs, plant economics, and life-cycle management. We discuss aspects of current LCB biorefinery research with a focus on the major process stages, including feedstock selection, fractionation/deconstruction, and characterization, along with product purification, functionalization, and polymerization to manufacture valuable macromolecular materials. We highlight opportunities to valorize underutilized and complex feedstocks, leverage advanced characterization techniques to predict and manage biorefinery outputs, and increase the fraction of biomass converted into valuable products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Programming Abstractions for Managing Workflows on Tiered Storage Systems

Scientific workflows in High Performance Computing (HPC) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS) that used a Virtual Data Space (VDS) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2023

Site 300 Roadway Improvements - 834 Complex Soil Sampling and Analysis Plan

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 834 Complex (project) (Figure 1). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.

supply chain risk management, data structuring, re↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (S300): S300 Roadway Improvements - 817 Complex Soil Sampling and Analysis Plan (May 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 817 Complex (project). The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was developed in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (Site 300): Site 300 Roadway Improvements - 836 Complex Soil Sampling and Analysis Plan (May 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 836 Complex (project). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coexistence Praxis: The Role of Resource Managers in Wolf-Livestock Interactions on Federal Lands

In resource management, new terms are frequently introduced, reflecting ongoing evolution in the theory and practice of ecology and governance. Yet understandings of what new concepts mean, for whom, and what they imply for management on the ground can vary widely. Coexistence—a prominent concept within the literature and practices around human-wildlife conflict and predator management—is one such term: widely invoked and yet poorly defined. While for some coexistence is the latest paradigm in improving human-wildlife relations, the concept remains debated and indeed even hotly contested by others—particularly on the multiple-use public lands of the American West, where gray wolf conservation, livestock production, and the claims of diverse stakeholders share space. The multiple meanings of coexistence present serious challenges for conservation practice, as what the concept implies or requires can be contested by those most central to its implementation. In this study we examine wolf-livestock management—a classic case of human-wildlife conflict—by focusing on the experiences and perspectives of U.S. Forest Service (USFS) managers. We reviewed coexistence's multivalence in the literature, complementing semi-structured interviews conducted with USFS employees on case study forests from across the western states. Through this, we highlight the complexity and multi-dimensionality of the concept, and the unique yet under-explored perspective that resource managers bring to these debates. This work draws on insights from political ecology to emphasize the situatedness of manager practice—taking place within a broader set of relations and contextual pressures—while extending political ecologists' traditional focus on the resource user to a concern with the resource manager as a key actor in environmental conflicts. Through our engagement with the experiences and perceptions of USFS managers, who must balance conservation aims with long-established land uses like livestock grazing, we hope to clarify the various dimensions of coexistence. Our hope is that this work thus increases the possibility for empathy and collaboration among managers and stakeholders engaged in this complex socio-ecological challenge.

Martin, Jeff Vance↗
DOE OSTI2024

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗
DOE OSTI2023

Genetic monitoring of steelhead in the Klickitat River to estimate productivity, straying, and migration timing

Abstract Objective Salmonids with a complex life history variation present challenges for conservation management, but genetic approaches alongside fisheries monitoring can address questions regarding the viability of the natural populations. Methods We genotyped adult (n = 3108) and juvenile (n = 2624) samples of steelhead Oncorhynchus mykiss that were collected in the Klickitat River, Washington, USA at traps in the lower drainage to examine tributary level productivity, straying from outside sources, and variation in adult migration timing. Result Genetic assignment of steelhead from this system indicated that the majority were produced within or near tributaries of the middle Klickitat River (juvenile mean = 72.8%; adult mean = 87.3%). Analyses with parentage-based tagging identified that most hatchery-origin adults were assigned to the Skamania Hatchery (80.8%), as expected, since this has been the release stock for decades within the Klickitat River drainage. Hatchery-origin adults were also identified from programs operating outside the Klickitat River, which were primarily strays from Snake River hatcheries. Most natural-origin steelhead were assigned to the Klickitat River, but there were also natural-origin fish identified as strays from other regions of the Columbia River (22.3% of natural returns). We also examined genes known to be associated with migration timing in adult steelhead observed at the trap and observed a strong relationship between migration date and alleles for early and late migration, but individual outliers were detected across seasons. Conclusion Our results indicate that genetic variation of steelhead in the Klickitat River has been influenced by hatchery programs as well as natural-origin straying from other subbasins, but genetic diversity remains high throughout the subbasin, and both early and late migration alleles are maintained. The genetic diversity present in Klickitat River steelhead may enable this Endangered Species Act listed (threatened) species to better adapt to stochastic environmental conditions compared to less diverse populations.

Collins, Erin E. (ORCID:0000000260978479)↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Chemical durability and corrosion-induced microstructure evolution of compositionally complex titanate pyrochlore waste forms with uranium incorporation

Chemical durability of compositionally-complex rare-earth titanate pyrochlore solid solutions with or without uranium incorporation was investigated by semi-dynamic leaching testing. A Ti-enriched amorphous passivation film formed on the surface due to the surface alteration-reorganization mechanism with preferential release of the weakly oxygen-bonded rare earth elements. Elemental release was found to be strongly correlated with chemical disorder, with a negligible correlation with entropy. The release rate of the uranium gradually decreased with increasing numbers of components, implying a possibility of designing and optimizing waste form performance with enhanced chemical durability by controlling composition complexity and chemical disorder for effective nuclear waste management.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2026

Engineering Layer For System Analysis

ELSA offers various utility classes and methods to streamline the definition of regions, materials, and geometries in nuclear simulations. Key features include generating OpenMC regions, managing material properties, and providing convenient abstractions for complex geometrical and physical configurations. Additionally, ELSA supports the creation of submodels, enabling users to build modular and reusable components for their simulations. The codebase also includes robust extrusion and revolution capabilities, facilitating the efficient creation of 3D parametric geometries from 2D profiles through linear and rotational transformations.

Ferney, Paul [Idaho National Laboratory (INL), Ida↗
DOE OSTI2021

Manatee County Clear Sky Assessment Process: Prioritizing Solar + Storage for Resilient Facilities & Communities

The Clear Sky Tampa Bay project was a 15-month collaborative research effort to support solar + storage deployment for community resilience in Florida. The Clear Sky Decision Support Toolkit is a collection of resources designed to support users in conducting solar + storage prioritization and feasibility screening assessments at critical facilities. The Tampa Bay Regional Planning Council worked with four local governments in the region to test and apply the Toolkit. This case study series describes how each partner government used the Toolkit and highlights key insights and lessons learned that other users could follow to replicate the process. Manatee County applied the Clear Sky Decision Support Toolkit to two of the county's critical facilities: Nolan Middle School, which serves as a special needs emergency shelter, and its Public Safety Complex (PSC), which houses critical services including emergency management, communications, and medical services, traffic control, and more. The PSC serves as the Emergency Operations Center during activation events. The Clear Sky assessment process improved interagency communication and coordination and informed siting decisions for a potential solar + storage system for the Manatee County PSC.

14 SOLAR ENERGY↗
DOE OSTI2025

MRCI Task 2: Addressing Key Technical Challenges - Executive Summary Report

This report provides a high-level overview of Task 2 products that were developed through defining carbon storage systems, Precambrian basement structure and stress, developing regional technical collaboration, modeling and risk assessment. Subtask 2.1: Defining Sub-Regional CS/CCUS Systems Subtask 2.2: Defining Precambrian basement faulting/stress Subtask 2.3: Developing industrial partnerships and regional technical collaborations Subtask 2.4: Conducting regional/subregional analysis Subtask 2.5: Assessing and managing risk for potential commercial-scale storage complexes

MRCI,Technical Challenges↗