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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

Mechanism of Protein Transport on the Twin Arginine Translocation Pathway

This project was awarded in September 2018 and ran for one year. The overarching goals of this multi-award project was to understand the mechanism and energetics of the Tat protein transport system responsible to the transport of proteins into the chloroplast thylakoid lumen. This pathway is responsible for the delivery of a number of proteins required for chloroplast biogenesis and homeostasis, including subunits of the photosynthetic oxygen-evolving complex and of the three large protein complexes of the photosynthetic electron transport chain. The manner through which proteins are transported on this pathway is highly unusual, and through the research supported by his set of DOE grants are leading to a mechanism of action that is previously unknown for protein trafficking. During the course of this short project we have continued to uncover evidence that the Tat pathway allows proteins to cross membranes through lipid-lined toroidal pores. The experiments undertaken during this project have been continued during the subsequent on-going DOE award period.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area

This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Radioisotope Power System Cask Trailer Chiller Low Heat Load Study

This report documents the study of the Radioisotope Thermoelectric Generator Transport System (RTGTS) chillers capability to transport a Radioisotope Power System (RPS) with low heat loads in the cask. Tests of the RTGTS chillers included setting the glycol/water (coolant) temperature to a control set point of 40° and 60°F while applying heat loads ranging from 2500 to 0 W heat load. The tests performed showed that the chiller systems could handle the entire range of heat loads. However, a noise/vibration started to occur when the set point was 60°F with low heat loads. Three recommended changes to the RTGTS chillers are recommended with varying implementation windows. The first is an operational change that can be implemented immediately. The RTGTS chillers should have a set point of 40°F when transporting an RPS less than 1500 W. The second is a short-term change (within the next two years) augmenting RPS heat by utilizing one RTGTS onboard heater (2500 W) while transporting an RPS with less than 1500 W which will also require a change to the Instrument and Data Acquisition System (IDAS) to allow monitoring and possible control of power to the onboard heaters. However, if the heater does fail during transport, no emergency actions or immediate repairs will be required due to the chillers’ ability to handle low heat loads. Running a 2500 W heater when transporting low heat loads aids in prolonging the life of the chillers. The remaining recommended long-term change is to replace the current chillers with a new portable or permanently installed chiller system in the next five to ten years. This time frame takes advantage of the proven reliability and durability of the current chillers while the new chillers are phased in and proven to the RPS program.

42 ENGINEERING↗
DOE OSTI2021

Applications of Digital Twin System in a Smart City System with Multi-Energy

With the increasing number of electric vehicles (EV), the smart transportation system is becoming more closely related to the smart energy system in a smart city. However, because of the high complexity, dynamics, and large-scale of these two systems, it is challenging to study their operation problems systematically, especially in severe conditions, e.g., the 2021 winter Texas power traffic crisis. With artificial intelligence, federated learning, edge computing, and automatic control, a digital twin based smart city is proposed in this paper and it focuses on two applications: smart transportation and smart energy grid. Both systems are presented in a structured design manner with several components in the digital twin system, which also contains many real-implementations of different scenarios. The proposed digital twin of a smart city provides a potential clue for the problems mentioned above.

artificial intelligence↗
DOE OSTI2025

Impacts and emerging research opportunities in Vehicle-Grid Integration for transportation: A review

This review provides a comprehensive examination of Vehicle-Grid Integration (VGI) technologies and their impacts on transportation systems, with a particular emphasis on the transportation-energy nexus. It systematically explores how VGI affects key transportation applications such as charging infrastructure planning, electric vehicle (EV) routing, smart charging coordination, shared mobility, and dynamic pricing. By synthesizing recent literature from both transportation and energy systems perspectives, this study highlights how advanced methodologies, such as reinforcement learning, game theory, and optimization techniques, are used to model the complex interactions between EVs, mobility patterns, and distributed energy systems. Furthermore, the review also identifies critical challenges, including behavioral factors, data limitations, and system scalability. Drawing on these insights, the paper outlines emerging research opportunities to support the design of integrated, resilient, and user-centric VGI solutions that advance sustainable mobility and energy system efficiency.

Charging coordination↗
DOE OSTI2023

Validation and Verification of TEDS Facility HYBRID Modeling

The HYBRID modeling repository is an in-house developed library of models for selected integrated energy systems (IES) modelling. HYBRID models have been developed since 2015 to describe the physical operation of tightly coupled thermal systems including power generators, thermal transport systems, thermal storage, thermal-to-electric conversion systems, and other thermal applications. Here, validation and verification (V&V) capabilities are demonstrated using the Thermal Energy Distribution System (TEDS) at INL. Building upon prior work, the TEDS model has been updated and verified so that it better represents the installed system configuration and the operating control system. The model control system was changed to allow replication of actual experimental procedures. Experimental operations focusing primarily on thermocline tank performance were devised and performed. Several anomalies were found in the operation data of the experiment facility. V&V activities calibrating a selected input parameter are demonstrated on a single component as well as with a single parameter within the thermocline. Calibrating is then demonstrated on multiple components and a multi-parameter metric for the entire system. The validation methodology is successfully applied to validate the model with experimental data. It is also used to confirm a hypothesis behind one of the anomalies in experimental performance.

25 ENERGY STORAGE↗
DOE OSTI2023

HYBRID Modeling Validation and Verification Status Matrix

The HYBRID modeling repository is a premier resource for integrated energy systems modeling. HYBRID models have been developed since 2015 to describe the physical operation of tightly coupled thermal systems including power generators, thermal transport systems, thermal storage, thermal-to-electric conversion systems, and other thermal applications. Due to the increased size of the repository, a concise summary matrix of available models is desired. This matrix will consolidate not only the list of available models but also indicate original information sources, publications that have model examples, and level of validation and verification that exists for the models. Validation and verification (V&V) levels begin from simplified algebraic relationship and advance to dynamic data validation. Moving forward, as new models are contributed to HYBRID, their V&V level will be included, updating this matrix.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Blueprinting Electrified Transit System Implementation

To achieve a more affordable and reliable transportation system, we need to smartly upgrade our power systems and install a large number of charging stations, but conventional planning methods are not up to the task. By applying advanced simulation and optimization tools, we can design a smarter, more cost-effective electric transportation network. The initial focus was on public transit systems, demonstrating how this approach can deliver broader economic, reliability, and air quality benefits nationwide.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

Cao, Yang↗
DOE OSTI2026

From Vehicles to Systems: Understanding Freight Transportation as a Connected Energy, Infrastructure, and Operations System

The U.S. freight system may need to handle 50% more cargo by 2050. Upgrading our freight system requires modernizing capital-intensive, long-lived assets including freight trains, ports, and terminal infrastructure. NLR is advancing freight system solutions spanning ALTRIOS, the first digital twin for the full freight rail system; ALTRIOS-LIFTS, which can create digital twins of freight terminals; INFORMES, the first national model of the intermodal freight system; MARINESim, used to simulate and optimize ocean-going vessel operations; and more. These modeling and simulation tools enable data-driven decision-making across freight modes and systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗
DOE OSTI2024

A real-time energy and cost efficient vehicle route assignment neural recommender system

Here, this paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m (m ≤ n) trips. This new assignment system has been deployed and integrated into the POLARIS. Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Energy consumption↗
DOE OSTI2023

A Comparison of As-loaded Dose Calculations to Measured Dose Rates

Demonstrating that the radiation dose fields surrounding spent nuclear fuel (SNF) storage and transportation systems meet the applicable limits set forth in 10 CFR 72 for storage and 10 CFR 71 for transportation is essential for the safe handling of radioactive material. The Used Nuclear Fuel-Storage, Transportation, and Disposal Analysis Resource and Data System (UNF-ST&DARDS) [2] is used to provide realistic estimates of SNF-related safety margins. The UNF-ST&DARDS dose rate analysis approach differs from that used in typical licensing approaches, which use design-basis assemblies with bounding source term characteristics to demonstrate the packaging design complies with the regulations. These bounding licensing approaches can increase the time needed to qualify fuel for loading into dry storage and the time a loaded system must wait prior to transportation. UNF-ST&DARDS dose rate assessments allow quantification of realistic, uncredited safety margins associated with actual fuel loading compared with the regulatory limits. While realistic estimation of the dose field surrounding SNF systems may allow for additional flexibility in operations, it is essential to understand how these predictions compare to measured doses. The U.S. Department of Energy Office of Integrated Waste Management and the Prairie Island Indian Community conducted a transportation dose assessment to estimate the site-specific incident-free radiation doses from shipping SNF by rail from the Prairie Island Nuclear Generating Plant (PINGP) through the Prairie Island Indian Community Reservation and Trust Land [5,6]. For that effort, the dose rates were obtained for 50 TN-40 and TN-40HT systems in storage configurations. This work compares the predicted dose rates from UNF-ST&DARDS as-loaded calculations with dose rates measured from 50 SNF storage systems at PINGP. The remainder of this paper discusses the data obtained for the evaluation, the modeling methods, and the results of the calculations.

spent nuclear fuel (SNF), UNF-ST&DARDS, Validation↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2020

LIGHT DUTY VEHICLE CHOICE MODELS EXAMINING ALTERNATIVE FUEL TECHNOLOGY PREFERENCES AMONG COMMERCIAL FLEET OWNERS

Agent-based transportation models are critical tool for evaluating energy use, emissions and mobility impacts of transportation. In this context, vehicle ownership models are critical for linking agent activities to their impacts on the transportation system. However, no agent-based freight transportation models to date has been developed that predicts the fuel technology that is preferred by establishments when purchasing a Light Duty Vehicle (LDV) for business purposes. This research addresses this gap by using recently collected survey data to estimate a discrete choice model that can be applied in the agent-based context.

commercial↗