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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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588 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Autonomous Sampling Platform Development: Radiological Contamination Mapping at SRS

From 1961 to 1964, radioactive elements were released from the Savannah River Site into local bodies of water via cooling water charges from the reactors on site. In 1983, the extent of the radioactive contamination was first studied, and elements such as 137 Cs, 90 Sr, 238 Pu, 241 Am, 244 Cm, and tritium were found to have seeped from local bodies of water into sediment and the surrounding flora and fauna. The current method of tracking and monitoring radioactive contamination at the SRS is to gather samples and conduct measurements in a laboratory. A cheaper, and safer, method to conduct such measurements would be to automate the process by using an autonomous boat that can travel to locations, conduct measurements, and return home all without human intervention. To introduce this idea, the construction of an autonomous boat prototype was completed to demonstrate the practicality and feasibility of such an idea. The prototype travels to a set of waypoints, stops at each waypoint, and returns when all waypoints have been reached. It does this by employing a simple battery-powered boat with an Arduino controller that steers the boat using a steering algorithm incorporated into a Proportional Integral Derivative (PID) function. A total of three tests were conducted at two different bodies of water; and after working out some hardware problems, the boat drone was able to successfully steer and reach all programmed waypoints. With the prototype complete, the next steps to realizing the final product of the boat drone will include adopting a processing unit with higher-bit architecture, using a bigger boat with a more powerful trolling motor, and incorporating a solar panel for continuous power and round-the-clock performance.

42 ENGINEERING↗
DOE OSTI2018

Advanced Vehicle Testing and Evaluation Final Technical Report Encompassing Project Activities From October 1, 2011 to April 30, 2018

The Advanced Vehicle Testing and Evaluation (AVTE) project incorporated the conduct of advanced technology vehicle and infrastructure testing in the U.S. Department of Energy (DOE) Advanced Vehicle Testing Activity (AVTA). It was the only activity tasked by DOE to conduct independent field evaluations of vehicle technologies that use advanced technology systems and subsystems in vehicles that reduce petroleum consumption. The results are available through reports published on the AVTA website at http://avt.inl.gov/. The AVTE project provided insight into advanced vehicle technology's long-term performance, durability, maintenance, and lifecycle costs that cannot be found elsewhere. The site is a resource for DOE stakeholders and the public to gain knowledge on advanced technology vehicle progress and innovation. Vehicles tested included light and heavy-duty vehicles with conventional, hybrid, plug-in hybrid, and all-electric propulsion systems. Vehicles were tested on closed tracks and chassis dynamometers, as well as operated on public roads, in fleet operations, and over prescribed routes. All testing was controlled by procedures developed specifically to support such testing. Testing and evaluations were conducted in the following phases: Development and refinement of test procedures, which established testing procedures; Baseline performance testing, which established a performance baseline; Accelerated reliability testing, which determined vehicle reliability; Fleet testing, used to evaluate vehicle economics in fleet operation, and End of test performance evaluation. Test results from all aspects of testing were posted by Idaho National Laboratory (INL) to their website. This final report documents work conducted for the entirety of contract DE-EE0005501 by Intertek Testing Services NA, Inc. (Intertek). The contract was performed from October 1, 2011 through April 30, 2018, and includes 18 amendments to the original award with the most recent Amendment 18 dated March 28, 2018. The final budget for the AVTE project totaled 20,220,873 dollars. As a cooperative agreement, Intertek was obligated to provide cost share of 4,044,175 dollars, leaving 16,176,698 dollars as the Federal share of the agreement. Actual total expenditure for the AVTE project was 20,286,850 dollars. The full 16,176,698 dollars Federal share was expended and 4,110,152 dollars of cost share was provided by Intertek. This resulted in an over-share of 65,977 dollars. There were 106 light-duty on-road vehicles vehicles tested in both fleet and accelerated reliability testing. One additional vehicle was rented to obtain performance testing only. Three heavy-duty truck and trailer combinations were tested as part of the semi-autonomous driving with heavy-duty vehicles in a platoon. Overall project scope and direction was determined between Intertek, DOE, and technical teams from Idaho National Laboratory (INL), Argonne National Laboratory (ANL), and National Renewable Energy Laboratory (NREL). The project was administered by the DOE’s National Energy Technology Laboratory (NETL).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

PV Cyber Security Research (Final Report)

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cyber security attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid support functions which can drastically influence power quality, voltage, and bulk system frequency. This project was split into two phases. The first provided a survey and roadmap of the cybersecurity for the solar industry. The second investigated multiple PV cybersecurity research and development (R&D) concepts identified in the first phase. In the first year, the team created a roadmap for improving cybersecurity for distributed solar energy resources. This roadmap was intended to provide direction for the nation over the next five years and focused on the intersection of industry and government and recommends activities in four related areas: stakeholder engagement, cyber security research and development, standards development, and industry best practices. At the same time, the team produced a primer for DER vendors, aggregators, and grid operators to establish a common taxonomy and describe basic principles of cyber security, encryption, communication protocols, DER cyber security recommendations and requirements, and device-, aggregator-, and utility-level security best practices to ensure data confidentiality, integrity, and availability. This material was motivated by the need to assist the broader PV industry with cybersecurity resilience and describe the state-of-the-art for securing DER communications. Lastly, an adversary-based assessment of multiple PV devices was completed at the Distributed Energy Technologies Laboratory at Sandia National Laboratories to determine the status of industry cybersecurity practices. The team found multiple deficiencies in the security features of the assessed devices. In the second year, a set of recommendations was created for DER communication protocols— especially with respect to the state-of-the-art requirements in IEEE 2030.5. Additionally, several cybersecurity R&D technologies related to communications-enabled photovoltaic systems were studied to harden DER communication networks. Specifically, the team investigated (a) using software defined networking to create a moving target defense system for DER communications, and (b) engineering controls that prevent misprogramming or adversary action on DER devices/networks by disallowing setpoints that will generate unstable power system operations.

14 SOLAR ENERGY↗
DOE OSTI2020

Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

Connecting automated vehicles to traffic lights can lead to significant energy savings by enabling them to pass through intersections in an energy-efficient way without unnecessary stops. A cellular-based communication system connecting multiple traffic lights can help realize the full potential of energy-efficient driving at intersections. Thus, we propose a hierarchical speed planner that can leverage information from multiple connected traffic lights. The proposed speed planner consists of two modules: a green window selector and a reference trajectory generator. The green window selector, based on Dijkstra’s algorithm, finds a series of "green windows" for connected traffic lights that builds an energy-optimal path for vehicles to follow. The reference trajectory generator finds optimal entering times, based on the selected green window at each intersection, and then computes reference trajectories. Deriving and using analytical optimal entering speeds as a function of entering times allows us to guarantee the computational simplicity suitable for real-time implementation. We also demonstrate how to balance energy and traffic flow perspectives in the reference trajectory generator. Lastly, a high-fidelity simulation framework is used to evaluate the proposed speed planner and quantify the extent to which it can save energy in various real-world urban route scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.

2D materials↗
DOE OSTI2019

Off-Road Vehicles Research Workshop (Summary Report)

This report summarizes the off-road vehicles stakeholder workshop that was focused on research, technical challenges, and future direction related to off-road vehicles and held at Argonne National Laboratory in October 2018. The views expressed in this report are those of the workshop participants. Topics discussed and included in this report are engine efficiency and emissions; efficient fluid power; and system integration, control, and new technologies. The purposes of this workshop were to: 1) Discuss and exchange information on the current state of technology for off-road vehicles; 2) Work collectively to address the technical challenges; and 3) Help shape the future direction for off-road vehicles research and development (R&D). The report highlights some key points, takeaways, and technical challenges and ideas to inform DOE R&D programs for the off-road sector. The workshop objectives were to: • Identify the barriers to cost-effective, high-efficiency, off-road vehicles; • Identify pre-competitive research topics that could have the biggest impact in overcoming barriers; and • Identify metrics that should be applied to program progress and ambitious but realistic goals. Customer acceptance, reliability, and system durability are some of the market barriers to new technology that were discussed during the workshop. Participants agreed that a comprehensive strategy is required to explore ways to improve engine efficiency while lowering emissions (i.e., NO x , GHG, etc.) and at the same time, meet customer needs and expectations. A full system optimization was recommended for off-road vehicles. It was noted that off-road vehicles are very diverse with a wide range of engine size and vehicle applications. A need for R&D for transient operations and other operating conditions was identified, as off-road vehicles are currently optimized for steady state. Fuel type for these vehicles has an important role. Fuel cost and fuel delivery to the work-site are challenging for any new fuel. Increased vehicle electrification is one of the new technologies discussed during the workshop. Electric propulsion for off-road vehicles has limited applications in the near term, but hybridization seems more promising. Furthermore, electrification requires better batteries and, possibly, the use of higher-voltage systems. Developing more efficient component technologies, including pumps, motors, and valves, is important for more efficient fluid power systems. New work-circuit architectures that can decrease throttling losses and better integrate the work circuit and engine can offer significant efficiency gains. Autonomous vehicles for off-road sectors were also discussed. Autonomous vehicles for off-road space are very appealing for some markets due to shortage in skilled labor and its significant impact on productivity, vehicle weight, and therefore, energy use and emissions. Autonomous driving for mining and farming applications is more feasible because the routes are predefined, but for other applications, such as forestry and construction vehicles, autonomous driving would be very challenging.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Understanding the Charging Flexibility of Shared Automated Electric Vehicle Fleets

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL. In each of these four cities, SAEV simulations are performed using local projected electricity prices from the Regional Energy Deployment System (ReEDS) for a handful of supply side scenarios. A coordinated charging model is introduced that seeks to reduce fleet charging costs in response to time-varying electricity prices and increasing shares of personal trip demand served (between 1% and 25% of all metro trips served by the SAEV fleet). Simulation results are presented across various scenarios assuming fleetwide coordination to minimize charging energy costs while constrained by offering adequate mobility service to fleet customers. The results indicate that the SAEV charging load is highly flexible; energy costs were shown to reduce between 13% and 46% across a wide range of simulated scenarios. In addition, these savings were realized without detrimentally impacting the fleet’s ability to service trips.

autonomous vehicle↗
DOE OSTI2018

Data Analyst - A Device for Continuous Repeating Real-Time Assay of HPGe, CZT, or Scintillation Gamma Spectra - 18574

There are many situations where a continuous repeating series of quantitative gamma spectral assays is desirable. These include measurements of fluid going through a pipe, of items on a conveyor passing under a detector, a detector aimed at the ground on a moving platform, or a detector in free air as a plume of radioactivity drifts by. While Canberra and many others have built and deployed many of these devices, they have all been custom built for that particular application, and rather expensive. The Data Analyst [DA] is a general purpose device that is designed to fulfill many of these applications without modification, but can be fairly-easily modified if needed. The DA is a small [12 x 18 x 5 cm], low-power [3 W] box that accepts the input from external MCAs, controls the acquisition cycle, analyzes the results, and transmits them to the outside world when needed. The detector-MCAs supported are HPGe with Lynx MCA, NaI or CeBr or LaBr with Osprey MCA, and CZT or CsI with internal MCAs. The DA internal software used for analysis is the full standard Genie analysis software, which is very widely used and well accepted in the industry. This allows exported spectral files to be reviewed and confirmed by Experts, and reanalyzed if needed. The DA hardware is totally autonomous. As soon as power is applied, it starts the pre-programmed sequences of acquisition and analysis. Multiple analysis sequences [called Workflows] can be in operation simultaneously, e.g. with different count times and different nuclide libraries. Only the detector with MCA, and the DA are required in the field. A PC can be connected to the DA locally or remotely via WiFi, USB, or Ethernet. The PC is needed to setup the DA, and to readout the results. The DA also supports [with application-specific programming] external Inputs/Outputs either for recording with the analysis results or for use in the computation of the result, e.g. temperature, flow rate, pressure, ... ]. The DA also has an internal GPS receiver with external antenna. Applications of the DA that are discussed include stack gas effluent activity where a portion of the effluent stream was extracted to flow through a shield with a HPGe detector and Lynx MCA, and measurement of reactor coolant activity where a shielded CZT detector and MCA were aimed at various sections of reactor primary coolant during a maintenance outage. Potential other applications are also described. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Analysis of hydrogen as a Transportation Fuel FY17 Report

This report summarizes the results of literature reviews, surveys and analyses performed to evaluate the potential of hydrogen-fueled vehicles to be an economically viable transportation alternative. Five existing and important drivers of expanding hydrogen-fueled transportation adoption are multi-billion dollar sales reservations of Nikola Class 8 trucks, CALSTART viability analysis of hybrid-hydrogen drayage trucks in the shipyard cargo application, analysis showing economic advantages of Fuel Cell Electric Vehicles (FCEV)s over Battery Electric Vehicles (BEV)s beginning at 150-mile ranges, the announcement of a commercial 5kg electrolyzer, and commercial plans or vehicle availability by nine vehicle manufacturers of FCEV passenger vehicles. But hydrogen infrastructure availability needed to support broad adoption of hydrogen-fueled vehicles is limited to less than 50 publicly-available refueling stations, primarily in California. The demand side (consumer) economics associated with FCEV adoption showed strong economic sensitivity to the original vehicle’s fuel economy (mpg), distance traveled, and hydrogen (H2) generation costs. Seven use cases were used to evaluate the broad range of potential FCEV purchasers, including autonomous vehicle applications. Each consumer use case analysis resulted in a different hydrogen fuel cost that would be equivalent to the current fuel cost being paid by the consumer. The H2 generation costs (supply side) were sensitive to the volume of H2 supplied and H2 production costs needed to repay H2 supply facility capital costs and produce competitively-priced energy. H2FAST was used to more accurately incorporate capital, maintenance and production costs into a viable H2 supply cost to the consumer. When the H2 generation and consumer economics were combined, several applications with positive economics became clear. The availability of low-cost hydrogen pipeline connections, and therefore low-cost hydrogen, greatly benefits the California drayage truck application with hybrid-hydrogen retrofits being repaid within seven years. Class 8 trucks could also take advantage of these low-cost, but regional hydrogen supplies. In addition, the IVYS electrolyzer-based hydrogen generation product showed the potential to deliver hydrogen economically in an urban or freeway off-ramp setting to a limited number of passenger vehicles in areas with low-cost electricity. These positive, manually developed results show the need to develop more advanced tools to provide an expanded evaluation of the economics of hydrogen-based fuel applications. The use cases evaluated showed significant potential for hydrogen-fueled vehicles to have a sustainable impact as a transportation fuel. The positive impact is not limited to transportation fuels, but also grid resilience and flexibility through the use of controllable and variable electrolyzer output to rapidly adjust to changing grid conditions and enable greater integration of solar and wind generated power. This capability would directly enable alternative fuel vehicles to impact energy consumption, GHG emissions, and the economy at the regional and national levels.

08 HYDROGEN↗