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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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DOE OSTI2023

Electrocoagulation of high-salinity produced water: lessons learned from its early applications in unconventional reservoir plays

Ample research has demonstrated that electrocoagulation is a versatile technology capable of facilitating the removal of a wide range of physical, biological, organic, and inorganic constituents such as suspended solids, turbidity, bacteria, viruses, oil and grease, organic carbon (including chemical and biological oxygen demand), silica, fluoride, and so on. Despite its purported advantages and extensive lab-scale evaluations, it has not yet been widely adopted for water and wastewater treatment and reuse at commercial scale. This is symptomatic of a chasm between its technological capabilities on one hand and field performance and reputation on the other. Herein, we opine as to why this might be the case using our collective experiences with the oil and gas industry as an exemplar. In this study, we highlight scientific, technological, engineering, and business issues that need to be addressed to realize the full potential of this promising technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

DOE OSTI2025

Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring

Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. Significant effort has been devoted to enhancing Hydra’s user interface, to ensure that it provides clear, actionable insights for shift workers and other users. Gradient Weighted Class Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. Hydra has been containerized to enable the creation of portable demos and seamless integration with container-based technologies such as Kubernetes and Docker. With the user interface enhancements and containerization, Hydra can be rapidly deployed for new use cases and experiments. This talk will describe the Hydra framework, its user interface and experience, and the challenges inherent in its design and deployment.

Britton, Thomas [Thomas Jefferson National Acceler↗

DOE OSTI2024

ISO_Fortran_binding_m v0.1.0

The Fortran programming language standard defines a broad feature set supporting the interoperability of Fortran programs with program written according to the C programming language standard. Among Fortran's C-interoperability features is a a C header file "ISO_Fortran_binding.h" This header file defines the interface to various C data structures and functions that C programs may use to access Fortran data entities. The ISO_Fortran_binding_m software defines a native Fortran module that presents an interface to these same data structures and functions. ISO_Fortran_bind_m thus enables Fortran programs to access and manipulate Fortran entities in ways that precisely mirror what C programs can do using ISO_Fortran_binding.h. ISO_Fortran_binding_m facilitates writing portable standard-conforming Fortran programs that emulate non-interoperable features, e.g., dynamic polymorphism, in a standard-conforming interoperable way similar but broader than what is demonstrated in Berkeley Lab's Caffeine software [1]. ISO_Fortran_binding_m also enables a Fortran programmer to extend Fortran's capabilities to emulate certain C functionality such as memory address arithmetic or computing C's "sizeof" function. [1] https://github.com/BerkeleyLab/caffeine/blob/213e3df1c319f0663306354414f352acda42a24f/src/caffeine/collective_subroutines/co_reduce_s.f90#L88 [2] https://github.com/BerkeleyLab/ISO_Fortran_binding_m/blob/0c585362bb4f2c72cf9049c800a7115b529ec533/src/iso_fortran_binding_m.F90#L196

Rouson, Damian↗

DOE OSTI2024

AiiDA-INQ plugin

The AiiDA-INQ plugin will allow the INQ code developed at the lab to utilize the AiiDA workflow manager which enables high-throughput workflows. This includes provenance tracking, restart capabilities, job calculators, and other capabilities

Keilbart, NathanD↗

DOE OSTI2016

Future Directions of High Fidelity CFD for Aerothermal Turbomachinery Analysis and Design

Computational fluid dynamics is a powerful tool used on a daily basis by designers and researchers in the advancement of propulsion technology. As hardware and software technology continue to evolve, the impact on propulsion systems has the potential to be disruptive. with continued development of High Performance Computing, Large Eddy Simulation, High-Order unstructured grid algorithms, optimization and uncertainty quantification it is conceivable that a new frontier of simulation based research, analysis and design capability is in the foreseeable future. In order to accelerate this development collaboration between industry, academia and government labs is required.

Laskowski, Gregory M.↗

DOE OSTI2024

Rural Hybrid Generation & Microgrids: National Labs are Here to Help

Hear about the tools and technical assistance National Labs can offer to co-ops and other rural utilities seeking to build or enhance hybrid generation projects combining multiple technologies including wind, solar, battery storage, and others resources. These hybrids can deliver additional value by combining complimentary resources and by increasing reliability and resilience, including as part of a microgrid to serve a local load or community. Experienced cooperative and lab experts will share their perspectives on the opportunities there are to make use of their advanced modeling capabilities and expertise to scope, design, and implement these emerging hybrid solutions. Key takeaways for participants will include better understanding of the resilience benefits distributed hybrid systems provide, awareness of National Laboratory assistance programs and resources, tips for successful federal funding applications for these types of projects, and peer-to-peer learning through shared experiences and discussion.

14 SOLAR ENERGY↗

DOE OSTI2023

Demonstrating Load-Shaping Capabilities of Cost Minimizing Heat Pump Water Heater Controls with Varying Price Profiles

Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Looking back at 50 years of cutting-edge capabilities: LANSCE ‘underpins Los Alamos as a world-class scientific institution’

What started with an idea and a memo has since grown into a one-of-a-kind, world-renowned facility that contributes to the nation’s advancement of science and technology. First known as LAMPF (Los Alamos Meson Physics Facility) until the 1990s when it became LANSCE (Los Alamos Neutron Science Center), the facility is celebrating the 50th anniversary of its first full-capacity proton beam on June 9, 1972. Since then, LANSCE has contributed to a range of national security goals, such as supporting the Lab’s stockpile stewardship program, helping to advance the science of medical radioisotopes, improving the understanding of hydrodynamics, and more. “LANSCE has served the nation through its near-unique capabilities,” said Alan Carr, Senior Historian at the National Security Research Center, which houses the facility’s evolution through decades of documents, photos and other materials. “Since 1972, LANSCE has been the Lab’s major experimental science facility and underpins Los Alamos as a world-class scientific institution. This was true 50 years ago and is still true today.” In commemoration of this anniversary, we’re taking a look back at LANSCE’s historic five decades, including its first director, earliest milestones and important contributions to national security.

43 PARTICLE ACCELERATORS↗

DOE OSTI2013

Demand Response Performance of GE Hybrid Heat Pump Water Heater

This report describes a project to evaluate and document the DR performance of HPWH as compared to ERWH for two primary types of DR events: peak curtailments and balancing reserves. The experiments were conducted with GE second-generation “Brillion”-enabled GeoSpring hybrid water heaters in the PNNL Lab Homes, with one GE GeoSpring water heater operating in “Standard” electric resistance mode to represent the baseline and one GE GeoSpring water heater operating in “Heat Pump” mode to provide the comparison to heat pump-only demand response. It is expected that “Hybrid” DR performance, which would engage both the heat pump and electric elements, could be interpolated from these two experimental extremes. Signals were sent simultaneously to the two water heaters in the side-by-side PNNL Lab Homes under highly controlled, simulated occupancy conditions. This report presents the results of the evaluation, which documents the demand-response capability of the GE GeoSpring HPWH for peak load reduction and regulation services. The sections describe the experimental protocol and test apparatus used to collect data, present the baselining procedure, discuss the results of the simulated DR events for the HPWH and ERWH, and synthesize key conclusions based on the collected data.

Demand Response↗

DOE OSTI2025

Development of Thin Gap GEM-µRWELL Hybrid Detectors at Jefferson Lab

Over the past few decades, Micro Pattern Gaseous Detector (MPGD) technologies have been increasingly adopted as tracking detector options in High Energy and Nuclear Physics experiments thanks to their good spatial resolution, high-rate capability, stability and more importantly their ability for large area coverage at a relatively low cost compared to the alternative. The thin gap GEM-µRWELL hybrid detector is the latest addition to the MPGD family, that was introduced to vastly improve the spatial resolution capability of gaseous trackers when deployed in the barrel region to cover large angular acceptance of the central tracker in a collider experiment. In this talk, I will re-introduce the concept and motivation for the development of thin gap GEM-µRWELL hybrid technology with an emphasis on the initial studies that establish the proof-of-concept of the technology. I will then discuss the more recent results from latest beam test campaign at Jefferson Lab in May 2025 to study detector efficiency performance with various gas mixtures. I will also briefly present the ongoing activities to develop large area thin gap GEM-µRWELL tracking detectors for the ePIC experiment of the future Electron Ion Collider as well as the exploration of the technology to provide large area tracking options to the muon system of experiments at a future Higgs Factory Collider such as the FCC-ee for example. Finally, I will conclude with some perspectives on new ideas under exploration to develop the next generation of thin gap MPGD technologies with enhanced timing and spatial resolution capabilities

Gnanvo, Kondo [Thomas Jefferson National Accelerat↗

DOE OSTI2024

EVs@Scale High-Power Charging (HPC) Pillar Deep-Dive Technical Meeting

EVs@Scale Lab Consortium is addressing challenges, developing solutions, and enabling technologies for transportation electrification ecosystem. The consortium has five research pillars one of which focuses on high power charging (HPC). HPC pillar brings together hardware and software expertise, capabilities, and facilities related to high power EV charging, charge management, and grid integration. Deep-dive technical meetings are organized twice a year and provides an opportunity for more industry engagement and technical feedback for the national labs throughout the project lifecycle. High-Power Charging pillar has two active projects: (i) Next-Gen Profiles (NGP) and (ii) High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP). This presentation summarizes the progress made in both projects during the past six months focusing on specific topics. There will be two deep-dive technical meetings twice a year. Every deep-dive meeting will focus on different aspects of the project progress.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Nov. 2024 EVs@Scale High-Power Charging Deep Dive Technical Meeting

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗

DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Cath Lab Robotics: Paradigm Change in Interventional Cardiology?

Purpose of Review: To review the contemporary evidence for robotic-assisted percutaneous coronary and vascular interventions, discussing its current capabilities, limitations, and potential future applications. Recent Findings: Robotic-assisted cardiovascular interventions significantly reduce radiation exposure and orthopedic strains for interventionalists, while maintaining high rates of device and clinical success. The PRECISE and CORA-PCI studies demonstrated the safety and efficacy of robotic-assisted percutaneous coronary intervention (PCI) in increasingly complex coronary lesions. The RAPID study demonstrated similar findings in peripheral vascular interventions (PVI). Subsequent studies have demonstrated the safety and efficacy of second-generation devices, with automations mimicking manual PCI techniques. While innovations such as telestenting continue to bring excitement to the field, major limitations remain—particularly the lack of randomized trials comparing robotic-assisted PCI with manual PCI. Summary: Robotic technology has successfully been applied to multiple cardiovascular procedures. There are limited data to evaluate outcomes with robotic-assisted PCI and other robotic-assisted cardiovascular procedures, but existing data show some promise of improving the precision of PCI while decreasing occupational hazards associated with radiation exposure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Lab Collaboration Project (LCP) for Marine Energy: Nonlinear Ocean Waves and PTO Control Strategy (Task 11)

The objectives for this task was to advance analysis and simulation capabilities for wave-WEC interactions and PTO analysis in nonlinear ocean waves. The improvements involve advancements in the generation of nonlinear wave time series and in nonlinear control strategies resulting in a detailed examination of WEC-wave interaction under scarcely-studied nonlinear conditions.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Solving challenges in QCD 3D partonic distributions

The superior luminosity and high polarization of CEBAF, combined with the high resolution and excellent particle identification capabilities of its detectors, as well as the ability for multidimensional and multiparticle detection using polarized targets, make Jefferson Lab uniquely positioned to disentangle the genuine intrinsic transverse structure of hadrons encoded in 3D partonic distributions—particularly in the kinematic regime dominated by valence quarks. The inclusion of Jefferson Lab data on semi-inclusive and hard exclusive hadron production has the potential to dramatically advance our understanding of non-perturbative QCD dynamics. Although a wealth of data from various Jefferson Lab experiments is already available, its incorporation into phenomenological studies has been slow, and a significant portion of data from other leptoproduction experiments is still missing from global fits. In this contribution, we discuss the existing challenges and outline a path forward for improving the analysis of low center-of-mass electroproduction experiments in general, and Jefferson Lab data in particular.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗