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DOE OSTI2020

Hydrogen Refueling Reference Station Lot Size Analysis for Urban Sites

Hydrogen Fueling Infrastructure Research and Station Technology (H2FIRST) is a project initiated by the DOE in 2015 and executed by Sandia National Laboratories and the National Renewable Energy Laboratory to address R&D barriers to the deployment of hydrogen fueling infrastructure. One key barrier to the deployment of fueling stations is the land area they require (i.e. "footprint"). Space is particularly a constraint in dense urban areas where hydrogen demand is high but space for fueling stations is limited. This work presents current fire code requirements that inform station footprint, then identifies and quantifies opportunities to reduce footprint without altering the safety profile of fueling stations. Opportunities analyzed include potential new methods of hydrogen delivery, as well as alternative placements of station technologies (i.e. rooftop/underground fuel storage). As interest in heavy-duty fueling stations and other markets for hydrogen grows, this study can inform techniques to reduce the footprint of heavy-duty stations as well. This work characterizes generic designs for stations with a capacity of 600 kg/day hydrogen dispensed and 4 dispenser hoses. Three base case designs (delivered gas, delivered liquid, and on-site electrolysis production) have been modified in 5 different ways to study the impacts of recently released fire code changes, colocation with gasoline refueling, alternate delivery assumptions, underground storage of hydrogen, and rooftop storage of hydrogen, resulting in a total of 32 different station designs. The footprints of the base case stations range from 13,000 to 21,000 ft 2 . A significant focus of this study is the NFPA 2 requirements, especially the prescribed setback distances for bulk gaseous or liquid hydrogen storage. While the prescribed distances are large in some cases, these setback distances are found to have a nuanced impact on station lot size; considerations of the delivery truck path, traffic flow, parking, and convenience store location are also important. Station designs that utilize underground and rooftop storage can reduce footprint but may not be practical or economical. For example, burying hydrogen storage tanks underground can reduce footprint, but the cost savings they enable depend on the cost of burial and the cost land. Siting and economic analysis of station lot sizes illustrate the benefit of smaller station footprints in the flexibility and cost savings they can provide. This study can be used as a reference that provides examples of the key design differences that fueling stations can incorporate, the approximate sizes of generic station lots, and considerations that might be unique to particular designs.

08 HYDROGEN↗

DOE OSTI2021

Scalable Approaches to Selecting Key Entities in Large Networked Infrastructure Systems

This work aims at bringing advances in discrete optimization algorithms to solving practical engineering problems at scale. Often times, in many engineering design problems, there is a need to select a small set of influential or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization provides for a formal way to solve such problems. Common examples with infrastructure systems involve sensor placement and identification of key entities with certain objectives. However, scaling these approaches to large infrastructure systems can be challenging because of the high computational complexity of the overall framework that include the optimization algorithms as well as high-complexity compute-oracles that provide the necessary objective function values. In this work, we explore a well-studied and widely-applicable paradigm, namely leader-selection in a multi-agent networked setting in the context of scalable methodologies. We demonstrate novel frameworks that utilize variations of accelerated submodular optimization algorithms along with linear-algebraic methods that can help accelerate the oracle computations. We further explore this combination in conjunction with graph partitioning paradigms to take advantage of the accelerated algorithms in a distributed setting. Finally we demonstrate the key findings on a practical problem in an operational setting. For this, we leverage an example road network with approximately 18k nodes and 27k edges in a traffic control application, where we seek a limited number of k=200 key intersections. This problem can be solved in a serial setting in just under 5 hours providing more than 2 orders of magnitude speed-up over methods that do not consider acceleration techniques.

Visweswara Sathanur, Arun↗

DOE OSTI2020

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

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DOE OSTI2023

Fuel-Based Nash Bargaining Approach for Adaptive Signal Control in an N -Player Cooperative Game

This paper presents a fuel-based game-theoretic approach for adaptive signal control. Our controller applies Nash bargaining (NB) in an n-player cooperative game to identify optimal phasing splits considering future traffic demands. The fuel-based NB controller utilizes an objective function that combines operational measures (delays and stops) with fuel consumption measures at intersections. The proposed controller was encoded in Python and then implemented and evaluated in a VISSIM microscopic traffic simulation model in an intersection with increasing volumes. The performance of the NB controller was compared to a traditional actuated control as the baseline. The results show that the NB controller was able to achieve superior environmental gains with a 17% saving in fuel consumption and a 17% reduction in CO emissions. In addition, the proposed controller was capable of maintaining acceptable operational conditions as it achieved a 20% reduction in delay, 8% reduction in the number of stops, and 8% reduction in queue lengths compared to the actuated controller. Compared to similar studies that applied NB for adaptive signal control, our fuel-based NB controller stands out as a promising approach to significantly improve fuel consumption at signalized intersections.

Engineering↗

NASA NTRS2013

Speech Recognition Interfaces Improve Flight Safety

"Alpha, Golf, November, Echo, Zulu." "Sierra, Alpha, Golf, Echo, Sierra." "Lima, Hotel, Yankee." It looks like some strange word game, but the combinations of words above actually communicate the first three points of a flight plan from Albany, New York to Florence, South Carolina. Spoken by air traffic controllers and pilots, the aviation industry s standard International Civil Aviation Organization phonetic alphabet uses words to represent letters. The first letter of each word in the series is combined to spell waypoints, or reference points, used in flight navigation. The first waypoint above is AGNEZ (alpha for A, golf for G, etc.). The second is SAGES, and the third is LHY. For pilots of general aviation aircraft, the traditional method of entering the letters of each waypoint into a GPS device is a time-consuming process. For each of the 16 waypoints required for the complete flight plan from Albany to Florence, the pilot uses a knob to scroll through each letter of the alphabet. It takes approximately 5 minutes of the pilot s focused attention to complete this particular plan. Entering such a long flight plan into a GPS can pose a safety hazard because it can take the pilot s attention from other critical tasks like scanning gauges or avoiding other aircraft. For more than five decades, NASA has supported research and development in aviation safety, including through its Vehicle Systems Safety Technology (VSST) program, which works to advance safer and more capable flight decks (cockpits) in aircraft. Randy Bailey, a lead aerospace engineer in the VSST program at Langley Research Center, says the technology in cockpits is directly related to flight safety. For example, "GPS navigation systems are wonderful as far as improving a pilot s ability to navigate, but if you can find ways to reduce the draw of the pilot s attention into the cockpit while using the GPS, it could potentially improve safety," he says.

Source record↗

DOE OSTI2021

Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach

With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. Based applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.

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DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗

DOE OSTI2023

Micromovements and discomfort associated with flight mission with helmet operation tasks with different levels of cognitive workload

When performing stationary tasks under elevated cognitive workload, individuals must perform continual muscle contractions to maintain stability of the body, resulting in fatigue of the postural muscles. When the muscles perform these contractions in a prolonged manner, the body potentially responds through small changes in body movements—micromovements that may lead to discomfort. The study purpose was to evaluate impact of cognitive load on micromovements. The micromovements were measured during three different cognitive workloads; low, medium, and high. The NASA-TLX score was used to evaluate the perceived mental workload and discomfort was assessed by visual analog scale. In total, 60 subjects (30 males and 30 females) were recruited and performed cognitive tasks that simulated flight operations such as changing the radio frequency based on air traffic control messages, balancing the fuel levels in simulated fuel tanks, and aiming a reticle in a designated moving target using the cyclic control. Cognitive load was defined by the frequency of events. Micromovements were defined by changes in the center of pressure (COP) of the seat pan and COP standard deviation. It was found that the high cognitive workloads had the highest NASA-TLX scores including mental demands, temporal demands, and effort. The neck area had the highest overall levels of discomfort followed by upper back. The highest standard deviation for COP shift and number of micromovements occurred for medium cognitive workloads. In conclusion, while there were some interesting trends, few trends reached a statistical significance due to high variability among subjects for the outcome variables.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Model for Collaboration among Carriers to Reduce Empty Container Truck Trips

In recent years, intermodal transport has become an increasingly attractive alternative to freight shippers. However, the current intermodal freight transport is not as efficient as it could be. Oftentimes an empty container needs to be transported from the empty container depot to the shipper, and conversely, an empty container needs to be transported from the receiver to the empty container depot. These empty container movements decrease the freight carrier’s profit, as well as increase traffic congestion, decrease roadway safety, and add unnecessary emissions to the environment. To this end, our study evaluates a potential collaboration strategy to be used by carriers for domestic intermodal freight transport based on an optimization approach to reduce the number of empty container trips. A binary integer-linear programming model is developed to determine each freight carrier’s optimal schedule while minimizing its operating cost. The model ensures that the cost for each carrier with collaboration is less than or equal to its cost without collaboration. It also ensures that average savings from the collaboration are shared equally among all participating carriers. Additionally, two stochastic models are provided to account for uncertainty in truck travel times. The proposed collaboration strategy is tested using empirical data and is demonstrated to be effective in meeting all of the shipment constraints.

42 ENGINEERING↗

NASA NTRSDate not supplied

Bypass Duct Acoustic Liner Design With and Without Bifurcation Effects

The growth in air traffic and the commitment to sustainable aviation continue to provide new challenges to reducing aircraft noise levels. Acoustic liner design methodologies must therefore provide the capability to efficiently predict the acoustic benefits of novel liner configurations within complex aircraft nacelle geometries. With these observations in mind, a broadband acoustic liner optimization process has been developed and assessed through a series of design and experimental studies at increasing technology readiness levels. This work applies the design process to the aft-fan noise component and explores the effects of bypass duct bifurcations (e.g., the pylon and lower bifurcation). In addition to this new application, the design study is expanded to include the use of a commercially available duct propagation code. Despite the different general workflow for the two propagation codes, consistent optimized impedance spectra and in-duct attenuation predictions were obtained for several acoustic treatment options. The preliminary results are promising, and this work increases confidence in the enhanced broadband liner design methodology and lays the groundwork for complimentary use of the codes in future studies. The potential benefits of acoustic treatment on the upper and lower bifurcations are also demonstrated. The knowledge gained through this preliminary stage of the liner design process will be used to guide the identification of candidate liner designs for a proposed static engine test within the next year.

Acoustic Liner Design↗

NASA NTRSDate not supplied

Bypass Duct Acoustic Liner Design with and without Bifurcation Effects

Acoustic Liner Design↗

DOE OSTI2020

Periodic Porous 3D Electrodes Mitigate Gas Bubble Traffic during Alkaline Water Electrolysis at High Current Densities

We report alkaline water electrolysis at high current densities is plagued by gas bubble generation and trapping in stochastic porous electrodes (e.g., Ni foams), which causes a significant reduction in the number of electrolyte accessible catalyst active sites. Here, 3D printed Ni (3DPNi) electrodes with highly controlled, periodic structures are reported that suppress gas bubble coalescence, jamming, and trapping and, hence, result in rapid bubble release. The 3DPNi electrodes decorated with carbon-doped NiO achieve a high current density of 1000 mA cm -2 in 1.0 m KOH electrolyte at hydrogen evolution reaction and oxygen evolution reaction overpotentials of 245 and 425 mV, respectively. This work demonstrates a new approach to the deterministic design of 3D electrodes to facilitate rapid bubble transport and release to enhance the total electrode catalytic activity at commercially relevant current densities.

08 HYDROGEN↗

NASA NTRSDate not supplied

Quasi-Wireless Capacitive Power Transfer with Secure Data Acquisition for Robotic Systems in Space Infrastructure

Space exploration is dependent on robotic systems that utilize end-effectors to collect samples, probe surfaces, and manipulate objects. These systems can rarely be designed to do all three, forcing engineers to make tradeoffs based on the mission parameters - i.e. should the robotic appendage have a claw, drill, or shovel, and which would be best suited for the mission? Additionally, as more industrial and government entities partake in space exploration, data protection is needed in transit and at rest. To address these challenges, we present a first-of-its- kind robotic linkage that has no wiring between the joints. Instead, quasi-wireless capacitive (QWiC) power transfer is used to send energy over the robot’s chassis without a return wire. This enables the system to be completely modular through the use of single-contact permanent magnet connections, allowing rapid alterations in joint kinematics and/or the changing of end-effectors. For collecting sensor data from the robotic arm and to send remote commands to it, we use a Supervisory Control and Data Acquisition (SCADA) system. Data transmission relies on MQTT and OPC UA communication protocols with encryption. The SCADA server logs and archives sensor data and provides the functionality for authorized users to send remote commands from SCADA client(s) to motors. A SCADA client can be any of the web browsers that connects to a server via a secure communication channel using SSL protocol. Furthermore, as an extra data protection mechanism, we inject noise to the sensor data traffic, which obfuscates the timing of sensor data packets and adds confusion about which data packet represents which motor.

wireless sensor networks↗

DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗

NASA NTRSDate not supplied

INSPiRE – An Approach to Mission Quality Management using Network Slicing for Space Applications

Managing traffic between the Earth-Moon and Earth-Mars is a complex process requiring significant investment in resources and expertise at NASA. INSPiRE improves the performance of space networks by enabling a dynamic re-configuration process that works for any mixed topology over a heterogeneous and multi-vendor network. To achieve the desired functionality, INSPiRE incorporates a set of algorithms, machine learning processes, and policy inference to handle unpredictable, disruptive events. INSPiRE draws parallels from the current notion of the 3GPP (5G and beyond) Network Slicing approach, where the same physical network divides into several virtual networks, and for each of these virtual networks, there is a guaranteed Quality of Service for the missions that they serve.

cognitive communications↗

NASA NTRS2011

Full-Scale Turbofan-Engine Turbine-Transfer Function Determination Using Three Internal Sensors

Existing NASA/Honeywell EVNERT full-scale static engine test data is analyzed by using source-separation techniques in order to determine the turbine transfer of the currently sub-dominant combustor noise. The results are used to assess the combustor-noise prediction capability of the Aircraft Noise Prediction Program (ANOPP). Time-series data from three sensors internal to the Honeywell TECH977 research engine is used in the analysis. The true combustor-noise turbine-transfer function is educed by utilizing a new three-signal approach. The resulting narrowband gain factors are compared with the corresponding constant values obtained from two empirical acoustic-turbine-loss formulas. It is found that a simplified Pratt & Whitney formula agrees better with the experimental results for frequencies of practical importance. The 130 deg downstream-direction far-field 1/3-octave sound-pressure levels (SPL) results of Hultgren & Miles are reexamined using a post-correction of their ANOPP predictions for both the total noise signature and the combustion-noise component. It is found that replacing the standard ANOPP turbine-attenuation function for combustion noise with the simplified Pratt & Whitney formula clearly improves the predictions. It is recommended that the GECOR combustion-noise module in ANOPP be updated to allow for a user-selectable switch between the current transmission-loss model and the simplified Pratt & Whitney formula. The NASA Fundamental Aeronautics Program has the principal objective of overcoming today's national challenges in air transportation. The Subsonic Fixed Wing Project's Reduce-Perceived-Noise Technical Challenge aims to develop concepts and technologies to dramatically reduce the perceived aircraft noise outside of airport boundaries. The reduction of aircraft noise is critical to enabling the anticipated large increase in future air traffic.

Hultgren, Lennart S.↗

DOE OSTI2021

43-0434 Bridge Inspection Report 2021

The documentation includes the following: 1. 2021 NMDOT Bridge Inspection Report including Element Level Data Collection (in digital formats prepared by NMSU)--conforms to the National Bridge Inspection Standards and AASHTO Manual for Bridge Element Inspection; 2. 2021 Supplemental Report (in digital formats prepared by NMSU)--provides detailed information related to current condition of major bridge components; 3. 2021 Inspection Pictures (in digital format prepared by NMSU); 4. 2021 Delamination Map (in digital format prepared by LANL). During the 2021 inspection, three critical findings were reported to our LANL contact, Mr. Jonathan Stein, by text and/or email on June 26, 2021. 1. The south joint of the bridge had a modular section of the joint that could potentially come loose during the passing of vehicles. This was reported as a critical finding for safety. If the modular section becomes dislodged or deformed it could pose a serious hazard that could result in a punctured tire to vehicles, motorcyclists and/or bicyclist causing drivers/riders to lose control; 2. The bracket plate located at the north end of the bridge on the pedestrian walkway was corroded through and provides little protection to pedestrians and bicyclists. This was reported as a critical finding for safety to prevent injuries to people; 3. The north approach rail has three missing posts. These posts help ensure that traffic is redirected and the energy is absorbed by the rail. This was reported as a critical finding for safety. Additionally, the approach rail is on a curve with a nearby drop off. Immediate repair is recommended. Based on the 2021 inspection, the bridge deck is rated in "fair" condition. The chain drag performed on the deck identified several areas with delamination that are concentrated near the expansion joints, in the closure joint of the deck near the bridge centerline, and at the south end of the northbound lanes. The chain drag performed during the 2021 inspection revealed 215,333 sq. in. (1495 sq. ft) of delaminations and patched areas (not including the sidewalk). This is approximately a 600% increase from 2019. It is recommended that the delaminations and spalls with exposed rebar be repaired. The "delaminated area" map for the 2021 inspection is provided in the supplemental report.

42 ENGINEERING↗