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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ultrafast terahertz emission from emerging symmetry-broken materials

Abstract Nonlinear optical spectroscopies are powerful tools for investigating both static material properties and light-induced dynamics. Terahertz (THz) emission spectroscopy has emerged in the past several decades as a versatile method for directly tracking the ultrafast evolution of physical properties, quasiparticle distributions, and order parameters within bulk materials and nanoscale interfaces. Ultrafast optically-induced THz radiation is often analyzed mechanistically in terms of relative contributions from nonlinear polarization, magnetization, and various transient free charge currents. While this offers material-specific insights, more fundamental symmetry considerations enable the generalization of measured nonlinear tensors to much broader classes of systems. We thus frame the present discussion in terms of underlying broken symmetries, which enable THz emission by defining a system directionality in space and/or time, as well as more detailed point group symmetries that determine the nonlinear response tensors. Within this framework, we survey a selection of recent studies that utilize THz emission spectroscopy to uncover basic properties and complex behaviors of emerging materials, including strongly correlated, magnetic, multiferroic, and topological systems. We then turn to low-dimensional systems to explore the role of designer nanoscale structuring and corresponding symmetries that enable or enhance THz emission. This serves as a promising route for probing nanoscale physics and ultrafast light-matter interactions, as well as facilitating advances in integrated THz systems. Furthermore, the interplay between intrinsic and extrinsic material symmetries, in addition to hybrid structuring, may stimulate the discovery of exotic properties and phenomena beyond existing material paradigms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗
DOE OSTI2025

Electrolyte Design and Optimization for Alkali Metal‐Sulfur Batteries

Alkali metal-sulfur batteries, including lithium-sulfur (Li-S), sodium-sulfur (Na-S), and potassium-sulfur (K-S) systems, have garnered significant attention as promising electrochemical energy storage (EES) technologies. Among them, Li-S batteries stand out as strong contenders for next-generation energy storage, owing to their high energy density and the cost-effectiveness of sulfur-based cathodes. However, with the rapid technological advances and the escalating energy demand, lithium resources are becoming increasingly scarce, making it imperative to explore alternative metal anodes to replace lithium. Therefore, Na-S and K-S batteries, serving as counterparts to Li-S systems, are emerging as formidable contenders for next-generation energy storage technologies due to the abundant and cost-effective nature of sodium and potassium. Although Na-S and K-S batteries possess considerable potential in the energy sector, their development is still in its infancy, with performance constrained by the nascent state of electrolyte design and optimization. This review article provides a comprehensive overview of recent advancements and developments in liquid electrolytes for alkali metal-sulfur batteries. Additionally, it identifies key challenges and proposes future research directions aimed at enhancing electrolyte stability, optimizing interfacial compatibility, and improving the overall performance of alkali metal-sulfur batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

The Earth has humans, so why don’t our climate models?

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. In this work, we describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Fidelity Solar Irradiance Data: Simple Access to State-of-the-Art Information Accelerates Southeast Asia's Clean Energy Economic Transformation

High quality, robust, and reliable renewable energy resource data is foundational to climate-smart decision making, evidence-based policy planning, and clean energy investment mobilization. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to this critical resource data by providing free, high-fidelity solar resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights several ways the Southeast Asia solar resource data has been used for power system planning and project development in the region.

Advanced Energy Partnership for Asia↗
DOE OSTI2024

On the Feasibility of Deep Geothermal Wells Using Numerical Reservoir Simulation

This study examines the geothermal energy extraction potential from the basement rock within the Denver–Julesburg Basin, focusing on the flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-shaped, V-shaped, inclined V-shaped, and pipe-in-pipe configurations against enhanced geothermal system setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. The results suggest that while closed-loop systems offer moderate temperature increases, Enhanced geothermal system configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Immersive Analytics in Critical Spatial Domains: From Materials to Energy Systems

Immersive analytics (IA) leverages virtual reality, augmented reality, and mixed reality to transform how users interact with complex datasets across domains such as science, industry, and education. These immersive technologies offer spatial and multimodal environments that foster intuitive exploration, but they also introduce challenges related to cognitive load, interface design, and system performance. Here, this article presents a comprehensive review of visualization techniques, interaction models, and multimodal inputs utilized in IA. Drawing on case studies in scientific visualization, industrial training, and educational communication, we examine both the potential and limitations of current systems. Finally, we propose future research directions, focusing on real‐time collaboration, adaptive user interfaces, and scalable data exploration strategies to advance the field.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Engineering Redox Flow Battery Electrodes with Spatially Varying Porosity Using Non‐Solvent‐Induced Phase Separation

Redox flow batteries (RFBs) are a promising electrochemical platform for efficiently and reliably delivering electricity to the grid. Within the RFB, porous carbonaceous electrodes facilitate electrochemical reactions and distribute the flowing electrolyte. Tailoring electrode microstructure and surface area can improve RFB performance, lowering costs. Electrodes with spatially varying porosity may increase electrode utilization and provide surface area in reaction‐limited zones; however, the efficacy of such designs remains an open area of research. Herein, a non‐solvent‐induced phase‐separation (NIPS) technique that enables the reproducible synthesis of macrovoid‐free electrodes with well‐defined across‐thickness porosity gradients is described. The monotonically varying porosity profile is quantified and the physical properties and surface chemistries of porosity‐gradient electrodes are compared with macrovoid‐containing electrode, also synthesized by NIPS. Then, the electrochemical and fluid dynamic performance of the porosity‐gradient electrodes is evaluated, exploring the effect of changing the direction of the porosity gradient and benchmarking against the macrovoid‐containing electrode. Lastly, the performance is examined in a vanadium RFB, finding that the porosity‐gradient electrode outperforms the macrovoid electrode, is independent of gradient direction, and performs favorably compared to advanced electrodes in the contemporary literature. It is anticipated that the approach motivates further exploration of microstructurally tailored electrodes in electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Cooperative effects in thin dielectric layers: Long-range Dicke superradiance

The realization and control of collective quantum effects so far have predominantly focused on cold atomic ensembles. Quantum photonic platforms, with their engineered Green's functions and integration capability of advanced solid-state quantum emitters, provide opportunities to explore regimes of light-matter interaction beyond the scope of atomic systems. In this work, we demonstrate that embedding quantum emitters within a thin dielectric layer fundamentally alters their collective radiative behavior. The optical modes in the dielectric layer mediate long-range dipole-dipole interactions between emitters, enabling both total and directional superradiance between emitters separated by several wavelengths. Crucially, this mechanism supports Dicke superradiance even in parameter regimes where standard settings fail to support an interaction, unveiling a dimensionality-driven enhancement of cooperative effects. By bridging many-body quantum optics and photonic engineering, our work reveals a distinct interplay between surrounding dimensionality and collective quantum dynamics. Experimental realization of these predictions, readily achievable in solid-state quantum optics platforms, paves the way for scalable, directional quantum light sources and frontiers in many-body quantum optics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗
DOE OSTI2023

Moiré photonics and optoelectronics

Moiré superlattices, the artificial quantum materials, have provided a wide range of possibilities for the exploration of completely new physics and device architectures. In this Review, we focus on the recent progress on emerging moiré photonics and optoelectronics, including but not limited to moiré excitons, trions, and polaritons; resonantly hybridized excitons; reconstructed collective excitations; strong mid- and far-infrared photoresponses; terahertz single-photon detection; and symmetry-breaking optoelectronics. We also discuss the future opportunities and research directions in this field, such as developing advanced techniques to probe the emergent photonics and optoelectronics in an individual moiré supercell; exploring new ferroelectric, magnetic, and multiferroic moiré systems; and using external degrees of freedom to engineer moiré properties for exciting physics and potential technological innovations.

Science & Technology - Other Topics↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A methodology for domain overlapping coupling of thermal-hydraulic systems

Multi-scale coupling has increasingly drawn attention as a promising approach for modeling thermal systems. Traditional system codes provide fast and robust predictions at the plant scale, while high-fidelity computational fluid dynamics (CFD)-based tools resolve localized flow and heat transfer phenomena with greater accuracy. By combining these complementary strengths, co-simulations enable multi-scale analysis that would otherwise be computationally prohibitive for a standalone CFD code. Here, this work introduces a robust and problem-agnostic domain overlapping (DO) coupling between the system thermal-hydraulic (STH) code System Analysis Module (SAM) and the coarse-mesh CFD code Pronghorn. Both applications belong to the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite, a code suite in active development at the Idaho National Laboratory (INL), tailored for multi-physics analysis of advanced reactors. Unlike previous approaches, BlueCRAB supports an agnostic interface between codes based on different fidelity, while its coupling formulation can address arbitrary flow geometries with multiple inlets and outlets in coupled components. The implemented method leads to consistent pressure drops, enthalpies, and scalar concentrations between coupled SAM and Pronghorn simulations. The methodology is demonstrated through two verification tests, which ensure the numerical consistency and conservation across the codes, and through one validation test against experimental data. The proposed problems explore different physical aspects inherent to thermal systems, with particular attention given to nuclear reactor analysis. These include buoyancy-driven flows, complex flow patterns, and setups with multiple inlets and outlets, representing challenges in advanced reactor applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Navigating the Use of Direct Current in Residential Settings: Merits and Obstacles

This review paper provides an in-depth analysis of the role of Direct Current (DC) power systems in residential settings, focusing on their safety, efficiency, and environmental advantages. It recognizes the significant contribution of the residential sector to global carbon dioxide emissions and explores how DC power systems can help mitigate these effects. The paper traces the historical evolution of DC power, its resurgence with renewable energy technologies, and the advancements in power electronics that facilitate its integration. Emphasizing the efficiency and safety benefits, especially in low-voltage applications, the paper highlights the seamless integration of DC systems with inherently DC-generating renewable sources like solar PV and wind. The review discusses the critical role of converter technologies in the transition to DC power and examines the compatibility of DC systems with energy storage solutions, underscoring their potential for enhanced energy management. It also addresses the environmental impacts of adopting DC power, aligning with global carbon reduction efforts. The paper analyzes regional case studies, exploring practical applications and outcomes, and addresses challenges such as the need for standardization. It concludes with recommendations for stakeholders and future research directions, emphasizing the economic, technological, and environmental aspects of DC power systems. Overall, the paper presents DC power as a viable and sustainable option for residential energy conservation, contingent upon ongoing technological progress, supportive policies, and standardization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Two-dimensional spin systems in PECVD-grown diamond with tunable density and long coherence for enhanced quantum sensing and simulation

Systems of spins engineered with tunable density and reduced dimensionality enable a number of advancements in quantum sensing and simulation. Defects in diamond, such as nitrogen-vacancy (NV) centers and substitutional nitrogen (P1 centers), are particularly promising solid-state platforms to explore. However, the ability to controllably create coherent, two-dimensional spin systems and characterize their properties, such as density, depth confinement, and coherence, is an outstanding materials challenge. We present a refined approach to engineer dense (≳1 ppm ∙ nm), 2D nitrogen, and NV layers in diamond using delta-doping during plasma-enhanced chemical vapor deposition epitaxial growth. We employ both traditional materials techniques, e.g., secondary ion mass spectrometry, alongside NV spin decoherence-based measurements to characterize the density and dimensionality of the P1 and NV layers. We find P1 densities of 5–10 ppm ∙ nm, NV densities between 1 and 3.5 ppm ∙ nm tuned via electron irradiation dosage, and depth confinement of the spin layer down to 1.6 nm. We also observe high (up to 0.74) ratios of NV to P1 centers and reproducibly long NV coherence times, dominated by dipolar interactions with the engineered P1 and NV spin baths.

36 MATERIALS SCIENCE↗