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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

Chen, Xiaoliang (ORCID:0000000278056237)↗
DOE OSTI2023

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies.

Jacquelin, Frederic (ORCID:0000000183154344)↗
DOE OSTI2020

Lessons Learned from Hanford's Purex Tunnel 2 Structural Stabilization Project - 20509

In 2019, U.S. Department of Energy (DOE) contractor CH2M Hill Plateau Remediation Company (CHPRC) completed structurally stabilizing the Plutonium Uranium Extraction Plant (Purex) waste storage Tunnel 2 with engineered grout. This action significantly reduced risk to Hanford workers, the surrounding community and environment by preventing future collapse of the tunnel and possible release of radioactive materials. Decades of plutonium production at the Purex facility required construction of two large underground tunnels for storage of highly contaminated plutonium processing equipment that had failed during the operation of Purex and other facilities across the Hanford Site. Following the partial collapse of Purex Tunnel 1 on May 9, 2017, DOE and CHPRC took immediate action to safely stabilize the collapsed section of the tunnel. The hole in the tunnel was filled the next day as a temporary stabilization measure, and no contamination was detected as a result of the partial collapse of the tunnel. Workers then stabilized Tunnel 1 in its entirety by filling with engineered grout, which also encapsulated the radioactive equipment and railcars within the tunnel. Grouting eliminated the risk of further collapse and stabilized the tunnel contents, all while not precluding future remedial actions or final closure decisions. While conducting an integrity analysis of Tunnel 1 by using construction drawings, photos and other historical documents, project teams also analyzed the much larger Purex Tunnel 2. The analysis found Tunnel 2 did not meet current codes for structural integrity, and it may not be able to bear the weight of the soil above the tunnel. Therefore, it was determined that Tunnel 2 was also at risk of collapse. DoE's independent panel of experts determined grouting to be the best choice for stabilizing Tunnel 2 because it provides the highest level of stability and protection, and does not preclude future remedial actions. DOE authorized CHPRC to commence work and the Purex Tunnel 2 Structural Stabilization Project was launched in December of 2017. The paper will discuss the process of recognizing the following critical aspects and risks associated with stabilizing Tunnel 2, and the actions taken by the project team to mitigate these risks, including: - Tunnel investigation and data collection to assist with future remediation; - On-site batching and mock-ups to provide the large quantity of grout required for stabilizing the tunnel; - Workforce involvement and innovative actions that mitigated the project's most significant potential hazards, including traffic control; - Project team high level of execution translated into successful project execution. Sharing lessons learned from this critical risk-reduction effort can enhance the safety and efficiency of other complex and hazardous projects across the DOE complex. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗
DOE OSTI2025

Network Anomaly Detection in Distributed Edge Computing Infrastructure

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Marfo, William [University of Texas at El Paso,Dep↗
DOE OSTI2023

Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors

Understanding how individual animals respond to stressors behaviourally and physiologically is a critical step towards quantifying long-term population consequences and informing management efforts. Glucocorticoid (GC) metabolite accumulation in various matrices provides an integrated measure of adrenal activation in baleen whales and could thus be used to investigate physiological changes following exposure to stressors. In this study, we measured GC concentrations in faecal samples of Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) collected over seven consecutive years to assess the association between GC content and metrics of exposure to sound levels and vessel traffic at different temporal scales, while controlling for contextual variables such as sex, reproductive status, age, body condition, year, time of year and location. We develop a Bayesian Generalized Additive Modelling approach that accommodates the many complexities of these data, including non-linear variation in hormone concentrations, missing covariate values, repeated samples, sampling variability and some hormone concentrations below the limit of detection. Estimated relationships showed large variability, but emerging patterns indicate a strong context-dependency of physiological variation, depending on sex, body condition and proximity to a port. Our results highlight the need to control for baseline hormone variation related to context, which otherwise can obscure the functional relationship between faecal GCs and stressor exposure. Therefore, extensive data collection to determine sources of baseline variation in well-studied populations, such as PCFG gray whales, could shed light on cetacean stress physiology and be used to extend applicability to less-well-studied taxa. GC analyses may offer greatest utility when employed as part of a suite of markers that, in aggregate, provide a multivariate measure of physiological status, better informing estimates of individuals’ health and ultimately the consequences of anthropogenic stressors on populations.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2024

Impact of Color Space and Color Resolution on Vehicle Recognition Models

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings can be designed in principle to highlight certain vehicle characteristics or compensate for lighting differences when assessing potential matches to previously encountered objects. The dataset used in this work includes imagery gathered under diverse environmental conditions, including daytime and nighttime lighting. Experimental results inform expectations for possible improvements with automatic color space selection through feature learning. Moreover, we find there is only a gradual decrease in model performance with degraded color resolution, which suggests the need for simplified data collection and processing. By focusing on the most critical features, we could see improved model generalization and robustness, as the model becomes less prone to overfitting to noise or irrelevant details in the data. Such a reduction in resolution will lower computational complexity, leading to quicker training and inference times.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

High-Fidelity Modeling of Curbside Driving Behavior in SUMO: Preprint

Recent advances in mobility technologies have considerably increased the need for curbside use and congestion at curb-lanes. At airports and in dense urban centers, the growing adoption of Transportation Network Companies (TNCs), such as Uber and Lyft, has led to an increase in curbside traffic due to the reduction in the use public transit and other shared modes. In dense city areas, the curbside demand and congestion were further augmented by the growing use of e-commerce, which requires home-delivery of goods. To model vehicles' curbside interactions realistically, there is a need for high-fidelity simulation models that can represent the complex interplay of vehicles as they complete pick-up or drop-off trips. Vehicles' curbside behaviors are usually not modeled with high-fidelity in microscopic simulators such Simulation of Urban of Mobility (SUMO). This paper presents such a high-resolution model for vehicle curbside behavior in SUMO, using SUMO's Traci interface. Simulations results demonstrate that the improved curbside behavior captures vehicles' curbside maneuvering more accurately than the default SUMO curbside model, and increases the curbside utilization and capacity.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Pricing Strategy of Electric Vehicle Aggregators Based on Locational Marginal Price to Minimize Photovoltaic (PV) Curtailment

The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Complex Parsing for In-Network Acceleration of High-Energy Physics Experiments

This paper describes a novel application and evaluation of programmable networking in High-Energy Physics (HEP): a complete parser for the custom packet format used by Fermilab’s DUNE experiment. Notably, this parser is implemented on a Tofino programmable network switch and evaluated on the FABRIC testbed by using network traffic generated by the ICEBERG DUNE prototype. The parsed network traffic consists of Jumbo Ethernet frames that contain digitizations of sensor readings from ICEBERG’s detector.This work is an early investigation into providing in-network processing support for HEP experiments. The paper describes DUNE’s custom packet format, the challenges encountered when implementing a parser for that format, and an exploration of the techniques that are needed to overcome those challenges. We identify performance bottlenecks and discuss directions for future research.

Sagstad, Bjoern [IIT, Chicago] (ORCID:000900033610↗
DOE OSTI2021

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A dynamic interaction between CD19 and the tetraspanin CD81 controls B cell co-receptor trafficking

CD81 and its binding partner CD19 are core subunits of the B cell co-receptor complex. While CD19 belongs to the extensively studied Ig superfamily, CD81 belongs to a poorly understood family of four-pass transmembrane proteins called tetraspanins. Tetraspanins play important physiological roles by controlling protein trafficking and other processes. Here, we show that CD81 relies on its ectodomain to traffic CD19 to the cell surface. Moreover, the anti-CD81 antibody 5A6, which binds selectively to activated B cells, recognizes a conformational epitope on CD81 that is masked when CD81 is bound to CD19. Mutations of CD81 in this interface suppress its CD19 export activity. These data indicate that the CD81 - CD19 interaction is dynamically regulated upon B cell activation and this dynamism can be exploited to regulate B cell function. These results are not only valuable for understanding B cell biology, but also have important implications for understanding tetraspanin function generally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Blue Keanu: A Scientific Visualization Tool For Network Data

This software allows the user to visualize complex PCAP-ng files captured from network capture software such as Wireshark. The visualization runs in a GUI window that can be zoomed or moved to areas of interest in a waterfall type display. The user then can see an area of interest that looks different than the typical traffic visually, such as a human interaction or non-repetitive area of data. The program will tell the user the packet number and byte offset of interest for fast analysis of discrete atomic or non-random events. This is particularly useful for visualization of unknown binary format data, such as in PLC or SCADA protocols that may have human or other non-repetitive activity for further analysis, reverse engineering, or fast forensic analysis.

Durller, MichaelGeorge↗
DOE OSTI2020

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Performance of concrete under accelerated physical salt attack and carbonation

Conventional testing of physical salt attack (PSA) on concrete does not consider concomitant factors that may exist in service and alter the mechanisms and kinetics of PSA. This study adopted a combined testing approach where concrete was concurrently investigated under accelerated PSA and carbonation, simulating elements serving in heavy traffic and industrial zones, while implementing ambient conditions similar to that in geographic locations with previous cases of PSA. Based on the tested mixture design parameters [water-to-binder ratio, cement type, and supplementary cementitious materials], potential performance improvement and risks were identified. Thermal, mineralogical, and microscopy analyses elucidated the co-occurrence of complex degradation processes in concrete subjected to accelerated PSA and carbonation, which were distinctive from that induced by the single-factor PSA exposure. The synoptic results from this study may informatively improve guidance on mixture design of concrete when dual exposure to PSA and carbonation is expected in the field.

36 MATERIALS SCIENCE↗