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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

"Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic"

This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.

Wang, Han↗
DOE OSTI2025

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Improving Dynamic Wireless Charging System Performance For Electric Vehicles Through Variable Speed Limit Control Integration

Electric Vehicle (EV) charging has been a significant barrier to the widespread use of EVs. Traditional EV charging methods depend on cables, and there are concerns about safety, accessibility, convenience, and weather. A recent development, dynamic (or in-motion) wireless charging, enables EVs to charge wirelessly by incorporating charging infrastructure into roadways, allowing EVs to charge while moving. However, the energy transferred relies heavily on vehicle speed and time spent in the charging lane. This paper proposes an innovative solution that combines dynamic wire-less charging with Variable Speed Limit (VSL) control. This dynamic traffic control strategy adjusts speed limits based on real-time traffic, weather, and incidents. This integration of dynamic wireless charging and VSL has two potential benefits. First, it can motivate driver compliance with VSL through the incentive of charging. Second, it can promote smoother traffic flow and improve traffic safety by implementing lower speed limits at certain times. To verify these benefits, microscopic traffic simulations in SUMO were conducted under different EV penetration rates and VSL compliance rates. Simulation results reveal that the proposed approach can enhance dynamic wireless charging system performance while improving traffic flow and safety.

Xu, Guanhao [ORNL] (ORCID:0000000214326357)↗
DOE OSTI2020

Injury severity analysis of truck-involved crashes under different weather conditions

This paper investigates truck-involved crashes to determine the statistically significant factors that contribute to injury severity under different weather conditions. The analysis uses crash data from the state of Ohio between 2011 and 2015 available from the Highway Safety Information System. To determine if weather conditions should be considered separately for truck safety analyses, parameter transferability tests are conducted; the results suggest that weather conditions should be modeled separately with a high level of statistical confidence. To this end, three separate mixed logit models are estimated for three different weather conditions: normal, rain and snow. The estimated models identify a variety of statistically significant factors influencing the injury severity. Different weather conditions are found to have different contributing effects on injury severity in truck-involved crashes. Rural, rear-end and sideswipe crash parameters were found to have significantly different levels of impact on injury severity. Based on the findings of this study, several countermeasures are suggested: 1) safety and enforcement programs should focus on female truck drivers, 2) a variable speed limit sign should be used to lower speeds of trucks during rainy condition, and 3) trucks should be restricted or prohibited on non-interstates during rainy and snowy conditions. These countermeasures could reduce the number and severity of truck-involved crashes under different weather conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Impacts of Distributed Speed Harmonization and Optimal Maneuver Planning on Multi-Lane Roads

A commonly proposed method for improving traffic flow on freeways is speed harmonization. The effectiveness of current speed harmonization approaches, such as variable speed limits, is extremely reliant on human driver compliance. Connected and automated vehicles (CAVs) are expected to come to market within this decade, offering the opportunity to eliminate or reduce the reliance on human compliance. However, extending current roadside infrastructure-based approaches of assigning centrally computed harmonization speeds to individual vehicles is, costly. An alternative approach is to have individual vehicles estimate the traffic state on-board and make distributed decisions to achieve the harmonization goal autonomously. In this work, we present a distributed algorithm for estimating the current average speed of traffic. We couple this with a distributed 2D maneuver planning approach. Then, we study the impact on traffic efficiency in terms of energy consumption and travel time at varying CAV penetration rates.

Goulet, Nathan↗
DOE OSTI2024

CIRCLES: Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing

The energy efficiency of today’s vehicular mobility relies on the un-integrated combination of i) control via static assets (traffic lights, metering, variable speed limits, etc.); and ii) onboard vehicle automation (adaptive cruise control (ACC), ecodriving, etc.). These two families of control were not co-designed and are not engineered to work in coordination. Recent studies have shown i) limitations of controls, and even ii) negative impacts of ACC. This project focused on the technology development, implementation and prototyping, and validation of Mobile Traffic Control (MTC). MTC can be viewed as an extension of classical traffic control (in which static infrastructure actuates traffic flow). In the MTC paradigm, automated vehicles actuate the entire flow via their behavior, offering enhanced possibilities to optimize the energy footprint of traffic, if designed correctly. We set out to demonstrate for the first time that considerably reduced fuel consumption of all vehicles in traffic can be achieved via distributed control of a small proportion of CAVs. Compared to baseline vehicular technologies, our work offers a significant design departure: control algorithms for the CAVs consider the impact one vehicle can have on overall traffic, improving resulting overall fuel consumption. We focus on using a few vehicles (as traffic controllers via CAV technology) to improve the energy efficiency of traffic flow to further optimize energy efficiency. A live-traffic demonstration in November 2022 featured the deployment of 100 specially-equipped CAVs, constituting an approximate local penetration rate upwards of 2%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Bayesian modeling framework for crash severity effects of active traffic management systems

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

We investigate the impact of three land surface models (LSMs) on hub-height wind speed under three different soil regimes (dry, wet, and frozen) to understand and improve the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that, the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by land surface due to weaker surface fluxes and more dominated by large-scale synoptic disturbances. Over the frozen soil, the land surface model seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first are the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second are the inaccurate initial soil states such as soil temperature and soil moisture.

Xia, Geng↗
DOE OSTI2022

Novel cis-regulatory elements as synthetic promoters to drive recombinant protein expression from the Chlamydomonas reinhardtii nuclear genome

Eukaryotic green microalgae represent a sustainable, photosynthetic biotechnology platform for generating high-value products. The model green alga Chlamydomonas reinhardtii has already been used to generate high value bioproducts such as recombinant proteins and terpenoids. However, low, unstable, and variable nuclear transgene expression has limited the ease and speed of metabolic engineering and recombinant protein expression in this system. Here, novel genetic devices for transgene expression in C. reinhardtii have been developed by identifying cis-regulatory DNA elements capable of driving high transgene expression in C. reinhardtii promoters using de novo motif discovery informatics approaches. Thirteen putative motifs were synthesized as concatemers, linked to a common minimal basal promoter, and assayed for their activity to drive expression of a yellow fluorescent protein reporter gene. Following transformation of the vectors into C. reinhardtii by electroporation, in vivo measurements of yellow fluorescent protein expression by flow cytometry revealed that five of the DNA motifs analyzed displayed significantly higher reporter expression compared to the basal promoter control. Two of the concatemerized motifs, despite being much smaller minimal cis-regulatory elements, drove reporter expression at levels approaching that of the conventionally-used AR1 promoter. This analysis provides insight into C. reinhardtii promoter structure and gene regulation, and provides a new toolbox of cis-regulatory elements that can be used to drive transgene expression at a variety of expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗
DOE OSTI2024

Modifications to the JET Shattered Pellet Injector to Optimize Disruption Mitigation Experiments for Supporting ITER’s DMS Design

Shattered pellet injection (SPI) experiments on Joint European Torus (JET) are an important element in determining the physics basis for mitigating disruptions in ITER. Here, the initial design of the JET SPI system included three barrels to produce pellets with diameters of 4.5, 8.1, and 12.5 mm. The variability of the pellet speed by operating with and without a mechanical punch was limited and led to poor pellet integrity, so the mechanical punch was removed. Fragment size distribution is a function of pellet speed and the desire to change the resulting fragment size distribution was not originally a requirement. After the first set of SPI experiments on JET, different pellet sizes were considered to enable dual injection experiments with identical pellet diameters. It was also determined that speed control is necessary to improve experimental repeatability and to determine how the fragment size distribution impacts mitigation performance. Two barrels were fabricated to form pellets of 10 mm diameter and a third to form an 8.1 mm diameter pellet. New propellant valves were also fabricated and characterized to improve repeatability and overall performance. To have fine control of pellet speed, inserts to reduce the breech volume were fabricated and installed. Laboratory testing was conducted to ensure pellet release and provide a comparison of propellant gas delivered versus pellet speeds for a range of pellet types and mixtures. This article will also discuss how lessons learned from the JET SPI modifications can be applied to other SPI systems, such as the ITER SPI system, as controlling pellet release with the least amount of propellant gas is essential for optimal SPI effectiveness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. Although the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “intelligent driver model” (IDM) as our driver behavior model and use real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations, and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2020

A High-Efficient Solution for Electro-Hydraulic Actuators with Energy Regeneration Capability

This paper proposes a cost-effective, robustly practical solution for a high-efficient electro-hydraulic actuator (EHA) for linear drives. The solution addresses the challenges of high costs and limited efficiency that limit the application of EHA for the electrification of many off-road vehicles. Based on a variable-speed electric motor and a low-cost technology for a fixed-displacement pump, the proposed solution is suitable for a compact implementation and it is capable of energy regeneration under overrunning load conditions. Two different architectures—an open circuit and a closed circuit one—are considered to determine the most practical and efficient configuration as well as the control features. Both architectures minimize throttling losses associated with the conventional regulation of hydraulic actuators. Actuator velocities below the minimum and beyond the maximum allowable pump flow rate are achieved in both architectures using a proportional valve that bypasses the pump flow. The paper describes the design methodology as well as the simulation and experimental activities performed to verify the control strategies as well as energy efficiency. Considering an EHA system of about 20 kW, a complete efficiency map of the system is determined for all the possible four quadrants of operation thus including both resistive and overrunning loads, where energy recuperation is implemented. Simulation and experimental results show that the energy efficiency of the hydraulic systems can reach 84.7% in resistive phases. In assistive phases, the hydraulic system can recover up to 81.8% of the actuator energy. Here, the comparison between open-circuit and closed-circuit structures shows the advantages of the former in terms of energy efficiency. The proposed solution is suitable for electrified hydraulic actuation systems, in both industrial and mobile applications. With the advantages of the low cost of the hydraulic unit and high energy efficiency, it is particularly suitable for mobile applications such as construction machines, that are recently experiencing a significant electrification trend.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Limits on the evolutionary rates of biological traits

Abstract This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait’s average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher’s fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species’ weights to the lengths of DNA strands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Limits on the Evolutionary Rates of Biological Traits

This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait’s average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher’s fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-of relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species’ weights to the lengths of DNA strands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Vertical-Axis Wind Turbine Steady and Unsteady Aerodynamics for Curved Deforming Blades

Vertical-axis wind turbines’ simpler design and low center of gravity make them ideal for floating wind applications. However, efficient design optimization of floating systems requires fast and accurate models. Low-fidelity vertical-axis turbine aerodynamic models, including double multiple streamtube and actuator cylinder theory, were created during the 1980s. Commercial development of vertical-axis turbines all but ceased in the 1990s until around 2010 when interest resurged for floating applications. Despite the age of these models, the original assumptions (2-D, rigid, steady, straight bladed) have not been revisited in full. When the current low-fidelity formulations are applied to modern turbines in the unsteady domain, aerodynamic load errors nearing 50% are found, consistent with prior literature. However, a set of steady and unsteady modifications that remove the majority of error is identified, limiting it near 5%. This paper shows how to reformulate the steady models to allow for unsteady inputs including turbulence, deforming blades, and variable rotational speed. A new unsteady approximation that increases numerical speed by 5–10× is also presented. Combined, these modifications enable full-turbine unsteady simulations with accuracy comparable to higher-fidelity vortex methods, but over 5000× faster.

17 WIND ENERGY↗
DOE OSTI2023

Grid Forming Fast Frequency Response for PMSG-Based Wind Turbines

Electric power systems are undergoing a rapid transition from fuel-based generation using synchronous generators to renewable generation interfaced by power electronics. In this context, a key challenge is using renewable generation with limited controllability to contribute to power system stability. In this work, we investigate grid-forming control of permanent magnet synchronous generator (PMSG) wind turbines. The proposed control and curtailment strategy supports the entire spectrum of standard functions of grid-following (e.g. maximum power point tracking (MPPT) and grid-forming control (e.g., primary frequency control) without explicit mode switching. A detailed case study is used to compare the performance of the proposed control at operating points corresponding to MPPT and frequency control with standard grid-forming and grid-following controls. Further, the results demonstrate that (i) the proposed energy balancing grid forming control is self-synchronizing in MPPT mode, (ii) the (limited) energy storage and controllability of wind turbines can be adequately utilized to provide grid support, and (ii) the proposed control exhibits good performance under variable wind speeds and does not result in a significant increase of wind turbine fatigue loads.

17 WIND ENERGY↗