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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transient Optimization of the Cryogenic Moderator System Controller at the Spallation Neutron Source for Improved Performance

The high-energy neutron beam generated at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory is moderated to use cold (slow) neutrons for scientific discoveries. The Cryogenic Moderator System (CMS) removes heat from the neutron beam using cryogenic hydrogen (H 2 ) moderators connected via heat exchangers to a helium (He) refrigeration loop that dissipates heat using a compressor-brake system. However, the CMS is affected by sporadic losses in beam power, referred to as "beam trips," as these events generate significant disturbances in cooling requirements. To accommodate the heat load transients during beam trips, the CMS uses a decentralized control strategy consisting of four flow valves and one electric heater adjusted by independent proportional-integral (PI) controllers. During the CMS’s initial commissioning, the PI gains were calibrated based only on tracking performance, overlooking their effectiveness in disturbance rejection. A data-driven, control-oriented closed-loop model was developed to recalibrate the PI gains and minimize the transient disturbances caused by beam trips. The model consists of three main components: (1) a physics-based model of the He refrigeration loop, (2) a machine-learning model of the cryogenic H 2 cooling trains, and (3) the control logic used for feedback set-point tracking. Experimental results showed that the recalibrated gains obtained in this study improved the CMS’s transient response during beam trips.

Maldonado Puente, Bryan↗
DOE OSTI2026

2002 VCMPO Model Calibration and Home-Based Travel Survey

The 2001-2002 Model Calibration and Home-Based Travel Survey was a study conducted in Volusia County, Florida. One of the major purposes of the survey project was to collect information to allow for a better understanding of the travel habits and patterns of residents within Volusia County. The project was jointly funded by the Florida Department of Transportation, Volusia County, and the Volusia County MPO. Demographic and trip data were collected for 2,727 households from February 20, 2002, to February 28, 2002. Respondents were asked to record their travel and activities for a 48-hour period. Detailed information regarding their trips, including mode of transportation, trip purpose, departure and arrival times, and number of passengers, were gathered from 13,408 trips.

1Hz data↗
DOE OSTI2026

2000 Bay Area Travel Survey

In 2000, the San Fransisco Bay Area Metropolitan Transportation Commission launched its fifth travel survey effort in the nine-county Bay Area. More than 15,000 households participated. The survey was an activity-based travel survey that collected information on all in-home and out-of-home activities over a two-day period, including weekday and weekend pursuits. Like the 1990 survey, the 2000 Bay Area Travel Survey also included an additional sample of 3,000 households using Bay Area Rapid Transit. Respondents were asked to report detailed information regarding their travel, including trip locations, mode of transportation, trip start and end times, trip purpose, and trip activities. Demographic variables include gender, age, employment status, occupation, income, education level, ethnicity, as well as whether a respondent held a valid driver's license, was a student, and had a disability.

1Hz data↗
DOE OSTI2026

2005 Tahoe Regional Household Travel Survey

The 2005 Tahoe Regional Household Travel Survey is a comprehensive survey of the travel patterns of households in the Tahoe Basin region of California and Nevada. The survey was conducted for the Tahoe Regional Planning Agency over a four-month period, from June through September 2005. This collection contains two sets of travel data: 1) a survey of 1,220 permanent households within the Tahoe Basin region and 2) a subset survey of 125 seasonal households within the same area. The permanent resident dataset contains information about 2,732 household members, 2,471 vehicles, and 11,048 trips made during a 24-hour period. The seasonal resident subset features 726 trip observations and information about 229 household members and 185 vehicles. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles and 2) document all travel for each household member, regardless of age, over an assigned 24-hour period. During the recruitment phase, respondents were asked to provide detailed household information, including permanent or seasonal resident status, number of household occupants, type of dwelling unit, number of vehicles available to household members, as well as the year, body type, and fuel type of each vehicle. Survey participants received mailed travel logs to aid in documenting travel information for the 24-hour assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of passengers, departure and arrival times, activities at each trip destination, ride fare, and parking costs. Demographic information for this study includes age, gender, income, education, and employment status.

1Hz data↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Analyzing users’ preferences between personal and pooled rideshare services using a mixed logit modeling approach

Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in the transportation system in comparison to personal rideshare. Despite the growth in trip volume, ridesharing usage is still low compared to other popular transportation methods in the U.S., particularly traveling in one’s own personal vehicle. Furthermore, pooled rideshare usage is lower than personal rideshare. To understand riders’ preferences, a national survey (N = 2884) was conducted in the U.S. to investigate users’ choice behaviors in rideshare services examining personal versus pooled rideshare. Each survey respondent completed 20 stated-preference scenarios where participants choose between a personal or pooled rideshare option. Based on the responses, a mixed logit model was developed to capture the choice behavior preferences of the participants. The model unveiled the impact of demographic and trip attribute variables on users’ rideshare preferences. The discussion encompassed insights into demographic backgrounds and trip attributes, accompanied by a set of policy recommendations aimed at enhancing future pooled rideshare utilization.

Choice model↗
DOE OSTI2024

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

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

Energy consumption↗
DOE OSTI2026

Hydrogen release from aqueous based LOHCs: The role of water in the potassium formate/bicarbonate cycle

Here, in this study, we evaluate the chemical and physical properties governing the potential round-trip efficiency of an aqueous formate/bicarbonate cycle for hydrogen storage, with a particular focus on potassium formate as a potential liquid hydrogen carrier. Using thermodynamic parameters, we predict the conversion of formate to bicarbonate across varying temperatures and pressures, revealing that hydrogen release is relatively insensitive to temperature but highly dependent on pressure. Our findings indicate that hydrogen uptake is highly efficient, whereas hydrogen release poses a greater challenge, necessitating detailed optimization to enhance round-trip efficiency. We calculate the solubility limits of bicarbonate salts influenced by the common ion effect to enable the prediction of target conversion ranges that prevent the precipitation of potassium bicarbonate in a reactor. Furthermore, the energy efficiency of hydrogen release was assessed based on the heating requirements of the aqueous solutions. This analysis maximizes the round-trip efficiency by balancing the solubility limits of bicarbonate, the heat capacity of aqueous formate solutions and conversion based on thermodynamic equilibria. Taking these factors into consideration we suggest reaction conditions that could be utilized in a systems analysis to calculate the levelized cost of storage using the bicarbonate/formate cycle at commercial scales.

common ion effect↗
DOE OSTI2025

Analysis of pumped thermal energy storage using particle media integrated with concentrating solar power

Pumped Thermal Energy Storage (PTES) is an electricity storage system that converts electricity into thermal energy which is stored and later transformed back into electricity. Previous work has illustrated that particles have low capital costs and can be operated over a wide range of temperatures. PTES with particle storage achieves higher round-trip efficiency and specific power output than when molten salt thermal energy storage is used. This article explores hybrid systems that combine PTES with Concentrating Solar Power (CSP). Hybrid systems share the majority of components thereby reducing costs compared to two stand-alone devices. In addition, hybrid systems can provide multiple services (such as renewable power generation and electricity storage services). Using particle thermal storage in these hybrid concepts provides freedom in choosing the design conditions, since a wide range of operating temperatures is allowable, therefore making it possible to identify hybrid system designs that have good performance. In this article, two concepts for hybrid PTES-CSP are introduced. Thermodynamic models are developed and these are used to evaluate the performance of two hybrid systems. These models account for turbomachinery efficiency, and approach temperature and pressure loss in heat exchangers, as well as other sources of inefficiency, such as motor-generator losses, and air fan power. The “Solar Top-Up” Concept uses CSP to increase the temperature delivered by the charging heat pump. The discharging system uses a topping gas cycle and a bottoming steam cycle to fully exploit the available energy. Using solar heat to increase the maximum temperature from 750 K to 1100 K increases the round-trip efficiency from 41 % to 62 % and the specific work output from 88 kJ/kg to 301 kJ/kg. The second concept is a “Dual-Mode” device which provides both electricity storage and solar electricity generation with the same set of components. The PTES-mode and CSP-mode performance are optimized at different design values but careful parameter selection leads to good performance of both modes: one design produces PTES round-trip efficiency > 60 %, CSP heat engine efficiency > 40 %, and specific work outputs > 150 kJ/kg (for both cycles), when the particle receiver temperature is > 1200 K and maximum heat pump temperature is 1100 K.

14 SOLAR ENERGY↗
DOE OSTI2025

Electrifying education: Exploring the electrification potential of U.S. School bus fleets

We analyze the operations of 270 diesel school buses across the United States to assess their electrification potential and evaluate the impact of various charging strategies on electricity demand. We find that school buses typically follow a two-route schedule on weekdays, featuring extended dwell times between morning and evening trips. Weekday trip distances average 25 miles, while weekend trips average 42 miles. Charging simulations indicate over 90% of the U.S. school bus fleet could be electrified using current technologies (300-mile range at 1.21 kWh/mile with 19.2-kW depot charging) without modifying existing operating patterns. Depot charging is a key enabler of school bus electrification, however, the strategic placement of charging stations at other locations (e.g., schools) can further increase electrification potential. Additionally, we find electric school bus charging to be highly flexible, with charge management capable of reducing peak charging loads at depots by up to 77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

First-of-a-Kind Risk-Informed Digital Twin for Operational Decision Making

A digital twin (DT) is a digital model or a collection of models of a physical entity. DTs in the nuclear arena can be used from plant design through decommissioning. Decisions are typically a priori or made offline. Risk-informed decision making is identifying what can go wrong, its frequency, and the consequences of its failure. Ideally risk-informed decision making reflects the current state of the plant and provides a decision in real time. Traditionally, probabilistic risk assessments (PRAs) evaluate the failures of safety systems, the risk of core damage, and the offsite dose as the consequence. However, this DT evaluates the decisions on the control side rather than the protection side. It uses the same risk methods to probabilistically inform the decision-making process but in a different way. Rather than evaluating the risk of core damage, this DT evaluates the likelihood of avoiding a trip set point while maintaining plant safety. Performance-based assessments are identified via its probabilistic evaluation of operational alternatives based on system status. Because the purpose of the control system is to maintain system variables within prescribed operating ranges, upsets or challenges that can exceed a trip set point resulting in a plant transient and a challenge to plant mitigating systems based on actual plant conditions, are evaluated to safely maintain the plant within the operating ranges. The probabilistic portion of the model is autonomously and automatically adjusted, and the metric of interest (i.e. likelihood of avoiding a trip set point) is recalculated. The digital representation of the physical system (i.e. the DT) performs a deterministic performance–based assessment of the probabilistically identified alternatives identified to validate the probabilistic assessment. A decision-making algorithm selects the appropriate option based on the probabilistic and deterministic assessments and transmits a control signal to a component(s) to initiate a corrective action or informs an operator of its decision.

digital twin↗
DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

WholeTraveler Anonymized Data Phase 2

Phase 2 of the WholeTraveler study consisted of a global positioning system (GPS) data collection. Phase 2 started immediately after the completion of the Phase 1 survey for any respondent who opted into Phase 2. The raw locational data collected have been processed into identified "trips" and some of those trips into identified "trip chains." Data from Phase 1 and Phase 2 are linked by a unique respondent identifier. Anonymized versions of the Phase 1 and Phase 2 data are both available on Livewire.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

1991 Boston Household Travel Survey

The Central Transportation Planning Staff conducted a household travel survey in 1991, featuring demographic, socioeconomic, and travel features of 2,347 households, generating 39,372 weekday trips. The survey took the form of an activity-based travel diary that each subject household filled out for one weekday. The data was used to estimate new models for trip generation, automobile ownership, trip distribution, and mode choice.

1Hz data↗
DOE OSTI2026

1990 CATS Household Travel Survey

The Chicago Area Transportation Study (CATS) 1990 Household Travel Survey was a self-administered mail-out mail-back survey. The purpose of the study was to update existing data on travel patterns and characteristics of Chicago residents. CATS surveyed the region on a county-by-county basis with the Chicago Central Business District in collaboration with the Urban Transportation Center of the University of Illinois. The study gathered demographic and travel behavior data from residents of northeastern Illinois who were 14 years old or older. 19,314 households participated in the survey. The study focused on non-work travel by motorized vehicle and by foot, and residents recorded their trips on a single travel day. Transportation-related variables include trip origin and destination, trip purpose, travel time, mode of travel used, vehicle occupancy, and walking distance (if that transit mode was involved).

1Hz data↗
DOE OSTI2026

2000 Delaware Valley Regional Planning Commission Household Travel Survey

This survey was conducted from March through December 2000 under the auspices of the Delaware Valley Regional Planning Commission and the Southern Jersey Transportation Planning Organization and funded through the Pennsylvania and New Jersey departments of transportation. Cambridge Systematics provided quality assurance under a subcontract to NuStats. The purpose of the survey was to obtain information about work and non-work trip generation, trip distribution, modal choice, and traffic assignment, as well as to obtain data on average vehicle occupancy. The dataset contains demographic and travel data on 5,677 households in the 14-county study area. Of those households, 4,217 were from the 10-county Delaware Valley region of Pennsylvania and New Jersey. The 4,217 Delaware Valley households, when weighted, represent 9,358 members and 6,069 vehicles, and reported a total of 31,631 trips.

1Hz data↗