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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Status of the Top Plate and Anticryostat for High Field Cable Test Facility at Fermilab

Fermi National Accelerator Laboratory (Fermilab) is currently constructing a new High Field Vertical Magnet Test Facility (HFVMTF) designed for testing High Temperature Superconducting (HTS) cables under high magnetic fields. This facility is expected to offer capabilities similar to those of EDIPO at PSI and FRESCA2 at CERN. The background magnetic field of 15 T will be generated by a magnet supplied by Lawrence Berkeley National Laboratory. The primary function of HFVMTF will be to serve as a superconducting cable test facility, facilitating tests under high magnetic fields and a broad spectrum of cryogenic temperatures. Additionally, the facility will be utilized for testing high-field superconducting magnet models and demonstrators, including hybrid magnets, developed by the US Magnet Development Program (MDP). This paper provides a comprehensive description of the current status of two pivotal components of the facility: the Top/Lambda Plates Assembly and the Anticryostat for the Test Sample Holder. The latter will serve as a principal interface component connecting cable test samples with the facility's cryostat.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Anomalous transport in high-mobility superconducting SrTiO 3 thin films

The study of subtle effects on transport in semiconductors requires high-quality epitaxial structures with low defect density. Using hybrid molecular beam epitaxy (MBE), SrTiO 3 films with a low-temperature mobility exceeding 42,000 cm 2 V -1 s -1 at a low carrier density of 3 × 10 17 cm -3 were achieved. A sudden and sharp decrease in residual resistivity accompanied by an enhancement in the superconducting transition temperature were observed across the second Lifshitz transition where the third band becomes occupied, revealing dominant intraband scattering. These films further revealed an anomalous behavior in the Hall carrier density as a consequence of the antiferrodistortive (AFD) transition and the temperature dependence of the Hall scattering factor. Using hybrid MBE growth, phenomenological modeling, temperature-dependent transport measurements, and scanning superconducting quantum interference device imaging, we provide critical insights into the important role of inter- versus intraband scattering and of AFD domain walls on normal-state and superconducting properties of SrTiO 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Comprehensive Total Cost of Ownership Quantification for Vehicles with Different Size Classes and Powertrains

In order to accurately compare the costs of two vehicles, the total cost of ownership (TCO) should consist of all costs related to both purchasing and operating the vehicle. This TCO analysis builds on previous work to provide a comprehensive perspective of all relevant vehicle costs of ownership. In this report, we present what we believe to be the most comprehensive explicit financial analysis of the costs that will be incurred by a vehicle owner. This study considers vehicle cost and depreciation, financing, fuel costs, insurance costs, maintenance and repair costs, taxes and fees, and other operational costs to formulate a holistic total cost of ownership and operation of multiple different vehicles. For each of these cost parameters that together constitute a comprehensive TCO, extensive literature review and data analysis were performed to find representative values in order to build a holistic TCO for vehicles of all size classes. The light- and heavy-duty vehicles selected for analysis in this report are representative of those that are on the road today and expected to be available in the future. Important additive analyses in this study include systematic analysis of vehicle depreciation, in-depth examination of insurance premium costs, comprehensive maintenance and repair estimates, analysis of all relevant taxes and fees, and considerations of specific costs applicable to commercial vehicles. We find that cars depreciate faster than light trucks and that older plug-in electric vehicles have a greater depreciation rate than newer electric vehicles. Light-duty vehicle (LDV) insurance costs show comparable costs for different powertrains, and lower costs for larger size classes. Medium- and heavy-duty vehicle (MHDV) insurance costs vary significantly by vocation. Electric and electrified powertrains have lower maintenance and repair costs than internal combustion engine (ICE) powertrains for all vehicle sizes, relative to vehicle price. MHDV maintenance and repair costs depend heavily on vocation and duty cycle. LDV taxes and fees are comparable across powertrain types and size classes, though marginally higher registration fees exist for alternative fuel vehicles. MHDV fees depend on the vocation, weight rating, and state. Many electric tractor trailers would be affected by additional battery weight, reducing the available payload capacity, and this cost can be substantial. Electric vehicle charging for commercial vehicles can be time-consuming; labor rates can cause this cost to dominate TCO. With improved knowledge of each of the cost components, we calculate a lifetime TCO for comparison across vehicles of different types and attributes. For a simulated small sport utility vehicle in 2025, modeled using Autonomie, the hybrid electric vehicle (HEV) has the lowest cost, followed by the conventional ICE vehicle. For MHDV, TCO can be drastically different depending on the vocation. Long-haul vehicles typically have the lowest per-mile costs. Excluding labor costs, the class 4 delivery has a comparable TCO to the day cab. Vocational trucks, refuse trucks, and transit buses have a high per-mile cost of ownership due to maintenance and insurance. For all of these vehicles, the cost of operating the vehicle is heavily weighted by the labor of the driver, followed by the fuel costs. While the HEV begins as the lowest cost powertrain for passenger vehicles, fuel cells are forecast to reach cost parity by 2030 when hydrogen prices reach $\$ 5$/kg while battery electric vehicles (BEV) reach cost parity by 2035 at a battery cost of $\$ 98$ per usable kWh of capacity, with these two technologies being the lowest cost in 2050. For the class 8 day cab tractor, the HEV and ICE vehicle begin as the lowest cost powertrains, and the 250-mile BEV reduces in cost from the most expensive to the least expensive by 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Control system for multi-system coordination via a single reference governor

This report describes the improvements to the Feasible Actuator Range Modifier (FARM) component of the RAVEN-based HYBRID framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON plug-in that solves the power dispatch problem. The solution involves economically optimal dispatches that satisfy the limits on production variables and corresponding rates of variation (explicit constraints) as well as the limits on the process variables tied to the service life of equipment (implicit constraints). The problem can be addressed as a two-stage process, i.e., HERON power dispatcher estimates a solution that meets explicit constraints (low-resolution physics), whereas FARM uses the simulation outcomes of HYBRID high-fidelity model to capture the system dynamic response and enforce implicit constraints (high-resolution physics). The initial version of the code (FARM-Alpha) was released by Argonne National Laboratory in January 2021 followed by FARM-Beta (January 2022) and FARM-Gamma (April 2022).

42 ENGINEERING↗
DOE OSTI2023

Status of the Top Plate and Anticryostat for High Field Cable Test Facility at Fermilab

Fermi National Accelerator Laboratory (FNAL) and Lawrence Berkeley National Laboratory (LBNL) are building a new High Field Vertical Magnet Test Facility (HFVMTF) for testing superconducting cables in high magnetic field. This facility will be located at Fermilab and will have a capability similar to EDIPO at PSI and FRESCA2 at CERN. The background magnetic field of 15T in HFVMTF will be produced by a magnet pro-vided by LBNL. The HFVMTF is jointly funded by the US DOE Offices of Science, High Energy Physics, and Fusion Energy Sciences. As a primary use it will be superconducting cable test facility, in high magnetic fields and wide range of temperatures. Complementary, this facility will be used to test high-field super-conducting magnet models and demonstrators, including hybrid magnets, produced by the US Magnet Development Program (MDP). The paper describes the status of Top Plates Assembly and Sample Insert Anticryostat, which will be a main interface component between cable test mode and magnet test mode at this facility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Current Status of the High Field Cable Test Facility at Fermilab

Fermi National Accelerator Laboratory (FNAL) and Lawrence Berkeley National Laboratory (LBNL) are collaborating to construct a new High Field Vertical Magnet Test Facility (HFVMTF) designed for testing superconducting cables in high magnetic fields. This state-of-the-art facility will be situated at Fermilab and will provide capabilities comparable to EDIPO at PSI and FRESCA2 at CERN. The HFVMTF’s background magnetic field, reaching 15 T, will be generated by a magnet supplied by LBNL. The HFVMTF is a collaborative effort supported by the US DOE Offices of Science, High Energy Physics, and Fusion Energy Sciences. It will serve as a vital testbed for superconducting HTS cables, subjecting them to high magnetic fields and a wide range of temperatures, benefiting both scientific communities. Additionally, this facility will play a key role in testing high-field superconducting magnet models and demonstrators, including hybrid magnets, developed by the US Magnet Development Pro gram (MDP). These hybrid magnets, utilizing both LTS and HTS superconductors, are significant advancements toward achieving 18+ T dipoles for future hadron-hadron colliders. The presentation outlines the current status of the facility, covering aspects such as construction progress, cryostat designs, top and lambda plates, and systems for powering, quench protection, and monitoring.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

FARM User Guidance and Instructions

The FARM (Feasible Actuator Range Modifier) module is a component of the RAVEN-based FORCE framework for the analysis of Integrated Energy Systems (IES). FARM aids HERON in the solution of the power dispatch problem by evaluating feasible set-point signals to be issued to the control systems of the different IES unit components. Set-points need to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To enforce all these limits, a two-stage approach is adopted. First, the power dispatcher algorithm in HERON module estimates set-points that meet the constraints on the production variables, e.g., power levels and power ramp rate limits. These constraints are called explicit constraints. Then, if necessary, FARM adjusts these set-points to ensure the respect of the limits on the process variables of interest, given the knowledge of the system dynamics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). In this report, the general workflow and the software requirements of FARM module are summarized, and the detailed instructions for installing FARM software, running built-in example cases, deriving Linear Parameter-Varying (LPV) state-space models, and using FARM for user-defined power dispatch problems are provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FARM supervisory capabilities for thermal energy storage

The FARM (Feasible Actuator Range Modifier) module is a component of the RAVEN-based FORCE framework for the analysis of Integrated Energy Systems (IES). FARM aids HERON in the solution of the power dispatch problem by evaluating feasible set-point signals to be issued to the control systems of the different IES unit components. Set-points need to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To enforce all these limits, a two-stage approach is adopted. First, the power dispatcher algorithm in HERON module estimates set-points that meet the constraints on the production variables, e.g., power levels and power ramp rate limits. These constraints are called explicit constraints. Then, if necessary, FARM adjusts these set-points to ensure the respect of the limits on the process variables of interest, given the knowledge of the system dynamics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). In this report, an overview of the major capabilities of the latest release of FARM is provided, along with a summary of the tool demonstration campaign conducted at the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility. These results assess the performance of the control system architecture embedding FARM both as a Validator of the HERON power dispatcher and as a real time Supervisory control scheme. Additionally, the report outlines the areas that FARM might benefit from, along with proposed solutions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FullWave — A Full Wave Parallel Code for Modeling RF Fields in Hot Tokamak Plasma

FullWave is a computer code that simulates how radio-frequency (RF) waves travel and deposit energy in the hot plasma inside a fusion reactor. RF waves are used to heat the plasma and drive electrical current, which is essential for sustaining fusion reactions. The code uses a new algorithm that can handle much finer spatial detail than previous codes — more than 100 times finer — while running efficiently on national supercomputers. It incorporates a detailed physics model that captures subtle kinetic effects important for accurate prediction of wave behavior. Under this project, FullWave was extended to cover multiple RF frequency ranges relevant to present and future tokamaks, and validated against experimental parameters from the DIII-D tokamak at General Atomics. Results were published in peer-reviewed journal articles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗
DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗
DOE OSTI2024

Full-Scale Development and Piloting of a Hybrid Digital Twin for Wastewater Operations Optimization

This full-scale pilot of a machine learning based nutrient controller/digital twin is being done as part of The Water Research Foundation (WRF) project 5121: Development of Innovative Predictive Control Strategies for Nutrient Removal. The overall goal of this project is to develop and full-scale test a hybrid (machine learning + mechanistic model) nutrient management controller at four different water resource recovery facilities (WRRFs). The controller demonstrates both short-term optimization functions and predictive capabilities. This project includes the design of the controller, its performance at the WRRFs, and a description of the future improvements.

Johnson, Bruce↗
DOE OSTI2024

Testing hadronic-model predictions of depth of maximum of air-shower profiles and ground-particle signals using hybrid data of the Pierre Auger Observatory

We test the predictions of hadronic interaction models regarding the depth of maximum of air-shower profiles, X max , and ground-particle signals in water-Cherenkov detectors at 1000 m from the shower core, S ( 1000 ) , using the data from the fluorescence and surface detectors of the Pierre Auger Observatory. The test consists of fitting the measured two-dimensional ( S ( 1000 ) , X max ) distributions using templates for simulated air showers produced with hadronic interaction models pos-, et--04, 2.3d and leaving the scales of predicted X max and the signals from hadronic component at ground as free-fit parameters. The method relies on the assumption that the mass composition remains the same at all zenith angles, while the longitudinal shower development and attenuation of ground signal depend on the mass composition in a correlated way. The analysis was applied to 2239 events detected by both the fluorescence and surface detectors of the Pierre Auger Observatory with energies between 10 18.5 eV to 10 19.0 eV and zenith angles below 60°. We found, that within the assumptions of the method, the best description of the data is achieved if the predictions of the hadronic interaction models are shifted to deeper X max values and larger hadronic signals at all zenith angles. Given the magnitude of the shifts and the data sample size, the statistical significance of the improvement of data description using the modifications considered in the paper is larger than 5 σ even for any linear combination of experimental systematic uncertainties. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Recent and Planned Improvements to the System Advisor Model (SAM)

This talk will focus on recent and planned modeling improvements to the NREL System Advisor ModelTM (SAM), including PV hourly clipping correction, expanded geographic scope of PVWatts including Ukraine, additional options for hybrids, and enhanced PySAM and GUI interoperability. Additional topics will also include: bifacial modeling features including irradiance on the ground for agrivoltaics, PV uncertainty simulation features, and integrations with other NREL and industry tools.

battery↗