SEARCH · Search NASA

Results for “next generation energy systems”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Energy Storage Technologies and U.S. Department of War Requirements

This report offers an overarching primer the energy storage market and assessment of each technology’s suitability for U.S. Department of War applications. We find that battery energy storage is the most promising technology for energy storage applications in terms of energy density and cost. While commercial and advanced Li-ion can satisfy certain electric mobility (e.g., cars, midsized vehicles) and electric flight application (e.g. drones) needs of DoW, next generation technologies like Li metal solid-state batteries, and conversion chemistries (Li-air, Li-CFx) are needed for heavy duty applications like armored vehicles, tanks, airplanes, jets. Batteries should also be supplemented with technologies such as supercapacitors and next-generation flywheels to provide short bursts of high energy for high power needs. Thermal energy storage solutions (sensible and latent heat) and fuel cells (PEMFC and SOFC) are good alternatives for supporting energy storage applications in terms of energy density and cost. Gravity-based energy storage promises the least energy density with high cost and should only be used for niche DoW applications. Of the different gravity energy storage technologies reviewed, pumped-hydro, flywheel, and compressed air energy storage are promising, while solid gravity energy storage appears the least attractive. Military facilities requiring 14 days of energy storage can benefit from flow batteries, especially commercial VFRBs and next generation iron-air systems. While VFRBs are expensive, iron-air batteries have the coupled benefits of low cost and good energy density promising 100+ hours of storage.

25 ENERGY STORAGE↗

DOE OSTI2022

Foundational Science to Accelerate Nuclear Energy Innovation [Brochure]

The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in-situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous BES workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future. The PROs are as follows: (1) Master complex electronic structures to tailor thermochemical reactivity, transport, and microstructural evolution; (2) Interrogate and direct the physics and chemistry underpinning next-generation coolants and solvents; (3) Elucidate and control the underlying physics and chemistry of interfaces in complex nuclear environments; (4) Bridge multi-fidelity multi-resolution experiments, computational modeling, and data science to control dynamic behavior; and (5) Harness artificial intelligence to design inherently resilient condensed phases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

A Hardware and Software Co-design Framework for Energy Efficient Neuromorphic Systems

Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Visualizing energy transfer between redox-active colloids

Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficient D CT of the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.

Qu, Alan Subing [Univ. of Illinois at Urbana-Champ↗

DOE OSTI2026

Radiation Effects in Next Generation Used Nuclear Fuel Reprocessing Strategies

With the global community committed to significantly expanding nuclear energy capacity, the development of efficient used nuclear fuel (UNF) management strategies has become more critical than ever. These strategies are vital to fostering the widespread adoption of closed fuel cycles, which are essential for sustainable nuclear energy production and security. Achieving this ambitious goal necessitates a comprehensive understanding of radiation effects on next-generation technologies, as radiolysis can often limit the longevity and performance of these systems. This seminar will provide an overview of next-generation UNF reprocessing strategies, highlighting the latest advancements and innovative approaches in the field. Particular attention will be given to two key areas of recent research: 1. Radiation robustness and performance of advanced sulfur chloride-based chlorination technologies. We will explore the efficacy of sulfur chloride-based chlorination processes in the presence of surrogate cladding materials, specifically aluminum. These processes have shown promise in the dissolution, decontamination, and recovery of cladding materials for reuse. Detailed findings on how the composition and performance of these sulfur chloride solvents respond to radiation exposure will be discussed. 2. Impacts of metal ion complexation and direct dissolution conditions on monoamide-based reprocessing strategies. We will delve into the time-resolved and dose accumulation effects of irradiation on the direct dissolution of voloxidized uranium and rhenium using N,N-di-(2-ethylhexyl) butyramide (DEHBA) or N,N-di-(2-ethylhexyl)isobutyramide (DEHiBA) in pre-equilibrated n-dodecane solvent. The implications of these interactions on dissolution efficiency, radiolytic stability, and overall process performance will be examined. These studies aim to underscore the importance of understanding radiation effects in the development of next-generation UNF reprocessing technologies and the global transition towards more sustainable and efficient nuclear energy systems.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

DOE OSTI2020

Internal Insulation and Corrosion Control of Molten Chloride Storage Tanks

The National Renewable Energy Laboratory (NREL) is leading a multi-national team to validate the potential of a chloride-based molten-salt system that uses a ternary blend of MgCl2/KCl/NaCl to provide higher temperature thermal energy storage capability, thereby allowing for higher efficiency in the power block of next-generation concentrating solar power systems. While higher thermal stability, greater operating temperature, and a more efficient power cycle are possible, the molten chlorides present several unique challenges, including the design of internal liners to prevent the corrosion and thermal stress of alloy tank shells. This work discusses issues and potential solutions related to containment of molten chloride salt, specifically the design of the molten salt storage tanks.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

A novel lung-inspired 3D-printed desiccant-coated heat exchanger for high-performance humidity management in buildings

Offering an independent humidity management method for buildings, desiccant-coated heat exchangers (DCHXs) are deemed a promising approach to improve the overall energy efficiency of air conditioning (AC) systems. State-of-the-art DCHXs, however, are bound with conventional HX topologies either providing limited desiccant-air interfacial areas or introducing excessive pressure drop penalties. In this paper a novel 3D-printed DCHX concept inspired by the bronchi arrangement of a human lung is introduced to address the shortcomings inherent in existing DCHX designs. The proposed lung-inspired DCHX utilizes two intertwined bicontinuous flow networks enabling highly efficient heat and mass transfer characteristics for augmented adsorption and regeneration processes at low pressure drop penalties. While the first network evenly distributes an incoming air stream through the entire volume of the lung-inspired DCHX, the second network volumetrically splits a cooling water stream within and through the first network. Effects of various parameters including air flow rate, outdoor air humidity ratio, and regeneration temperature on dehumidification performance and energy efficiency of the proposed lung-inspired DCHX were investigated. Experimental results indicated the proposed lung-inspired 3D-printed DCHX outperforms existing DCHX systems by demonstrating an excellent balance between a high volumetric adsorption rate and a low pressure drop penalty. The volumetric adsorption rate of the proposed lung-inspired DCHX technology is 54.8 g/m 3 -s, a more than two-fold improvement compared with state-of-the-art DCHX systems. Additionally, the lung-inspired DCHX offers high thermal energy efficiency of 56% at a low regeneration temperature of 40 °C. Therefore, the proposed lung-inspired 3D-printed DCHX offers a new solid-desiccant-based air dehumidification pathway for next-generation high-performance AC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

From Molecules to Modules: Advanced Characterization of Membrane Systems

Membrane technologies can enhance the efficiency and selectivity of chemical separations in energy-water systems. Advanced characterization tools are critical for discerning separation mechanisms, revealing degradation processes, and designing novel materials and material systems for new and emerging challenges. The pursuit of next-generation membranes for water and energy applications requires understanding phenomena at the molecular scale, mesoscale, and macroscale. This perspective highlights advanced characterization techniques for elucidating and enhancing membrane performance, while addressing fundamental trade-offs involved in characterizing membranes under realistic conditions.

Zhu, Yaguang [Princeton University, NJ (United Sta↗

DOE OSTI2021

Electronic, Ionic, and Mixed Conduction in Polymeric Systems

Polymers that simultaneously transport electrons and ions are paramount to drive the technological advances necessary for next-generation electrochemical devices, including energy storage devices and bioelectronics. However, efforts to describe the motion of ions or electrons separately within polymeric systems become inaccurate when both species are present. In this paper, we highlight the basic transport equations necessary to rationalize mixed transport and the multiscale materials properties that influence their transport coefficients. Potential figures of merit that enable a suitable performance benchmark in mixed conducting systems independent of end application are discussed. Practical design and implementation of mixed conducting polymers require an understanding of the evolving nature of structure and transport with ionic and electronic carrier density to capture the dynamic disorder inherent in polymeric materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Distributed Energy Resource Management Systems: Preserving Customer Privacy through K-Anonymity

The smart grid represents the next generation of electricity distribution systems that utilizes recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. One of the many concerns in smart grid digital communication discussions is the possibility of violating customers’ privacy. Violating customers’ privacy imposes a significant barrier as smart grid desirable attributes are tightly tied to customers’ participation. Employing privacy models can address concerns regarding information privacy in smart grid digital communication. In this work, we provide an approach to utilizing K-anonymity to ensure data within the system excludes Personally Identifiable Information. Results suggest that a dynamically generated generalization hierarchy minimizes information loss incurred by the anonymization process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗

DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗

DOE OSTI2020

Subsurface Biogeochemical Research: Watershed System Science for Energy

The Subsurface Biogeochemical Research (SBR) program within the U.S. Department of Energy’s (DOE) Office of Biological and Environmental Research is strategically aligned with DOE’s mission to ensure U.S. security and prosperity through watershed system science. Water resources critical for energy production are under pressure from growing water demand, contamination, drought, flooding, and saltwater intrusion. Sustainable management of watershed systems and their coupling with the built environment rely on understanding the hydrological and biogeochemical processes that control watershed system dynamics and water availability and quality. Next-generation science-based models of watershed systems are needed to address many U.S. energy and environmental challenges, including contaminant cleanup, clean water availability, safe storage of energy and nuclear byproducts in the subsurface, nutrient availability for sustainable biofuel crops, and recovery of subsurface energy resources.

09 BIOMASS FUELS↗

DOE OSTI2025

Leveraging dendritic complexity for neuromorphic computing

Abstract Beyond-von Neumann computing approaches are necessary to sustain the growth of microelectronics and the increasing appetite for artificial intelligence/machine learning algorithms. Neuromorphic computing is an emerging paradigm that takes inspiration from the brain to provide a path forward to improve the computational efficiency and computational density of next-generation computing architectures. In nature, we observe brains performing complex computations with a much smaller energy footprint than conventional computing approaches. Current neuromorphic systems are focused primarily on scalability, namely, increasing the number of computational units (neurons) and connections between units (synapses). However, for brain-like cognition and efficiency in next-generation computing hardware, we need increased complexity in function, as well as improved connection density for scalability. Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. To conclude, we discuss how we can utilize emerging hardware devices in these systems and design next-generation neuromorphic architectures with dendrites.

Cardwell, Suma G. (ORCID:0000000226575545)↗

DOE OSTI2025

RaDIATE Collaboration Thermal shock studies

As next-generation accelerator target facilities (High Energy Physics, Spallation Sources, ... ) become increasingly more powerful and intense, high power target systems face key technical challenges, such as radiation damage and thermal shock. Those ultimately degrade the performance and lifetime of targets and have been identified as the leading cross-cutting challenges of high-power target facilities. In order to operate reliable beam-intercepting devices in the framework of energy and intensity increase for next generation accelerators, the RaDIATE Collaboration (Radiation Damage In Accelerator Target Environment), established in 2012 and managed by Fermilab, brings together existing expertise in nuclear material and accelerator targets from 20 international institutions, including CERN, to execute a coordinated strategy for high power targetry R&D. In this context, several thermal shock studies were performed at CERN's HiRadMat (High-Radiation to Materials) facility, that took a key step towards improving our knowledge on target damage tolerance. The first experiment HRMT-24, supported by EURCARD2 and completed in 2015, successfully validated the Johnson-Cook strength model developed at SwRI on beryllium S200FH (used for beam window material), providing a better confidence in simulating the thermal shock response of current and future S200FH beryllium components. HRMT-43, supported by ARIES and completed in 2018, tested various materials (Be, C, SiC, Si, Ti and ceramic nanofiber). It was a first and unique test which included pre-irradiated specimens from high energy proton beam irradiation to identify thermal shock response differences between non-irradiated and previously irradiated materials. Real-time measurement of dynamic thermomechanical response of graphite helped to benchmark numerical simulations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Progress and opportunities in corrosion mitigation in heat transfer fluids for next-generation concentrating solar power

Concentrating solar power (CSP) systems have gained considerable eminence in converting solar thermal energy into electrical power in recent years. According to the U.S. Department of Energy's Gen3 roadmap, the CSP system should operate > 700 °C, as its efficiency depends directly on the operating temperature of the heat transfer fluids (HTFs). A significant challenge, however, is the corrosion of containment materials by the HTFs that is exacerbated at the higher temperatures. A comprehensive review of the high temperature stable HTFs, their properties, corrosion mechanisms on different alloys, and corrosion mitigation measures is of much importance for directing a concerted research and development in the field, which forms the motivation for this compendium. First, molten salt HTFs and their thermophysical properties, along with liquid metals, are introduced. Corrosion of structural materials in different HTFs including molten salts, liquid metals, and supercritical carbon-di-oxide at various temperatures and the corrosion mechanisms are comprehensively reviewed. In addition, several corrosion mitigation methods are discussed. Finally, future directions for HTFs and corrosion mitigation methods in molten salts are proposed. As a result, the holistic review presented here will serve as the foundation for further research addressing relevant challenges and enabling the promise of achieving cost-competitive CSP.

14 SOLAR ENERGY↗

DOE OSTI2024

High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system

Abstract Developing novel lead‐free ferroelectric materials is crucial for next‐generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time‐consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high‐throughput combinatorial synthesis approach to fabricate lead‐free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High‐resolution x‐ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well‐controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high‐throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth. image

36 MATERIALS SCIENCE↗

DOE OSTI2025

Modified Andronov-Hopf Oscillator-Based Grid-Forming Converter with Emulated Virtual Cable for Enhanced Power Sharing Performance

Nonlinear oscillator-based grid-forming converters offer superior dynamic and steady-state performance, making them an attractive solution for interconnecting renewable resources. This paper proposes a novel modified Andronov-Hopf oscillator to enhance the operating spectrum and facilitate the integration of renewable energy sources. An inner loop controller based on the Lyapunov energy function is implemented to achieve robust stability and performance, while a virtual cable emulation strategy enables seamless parallel operation. Comprehensive modeling and simulation studies validate the effectiveness of the proposed system, demonstrating its capabilities in addressing diverse operating scenarios, including grid faults, renewable energy fluctuations, and parallel operation. The proposed solution exhibits fast transient response, robust stability, and flexible operation, making it a valuable contribution to the field of renewable energy integration. The results of this study can be used to inform the design and implementation of next-generation grid-forming converters, enabling a more sustainable and reliable energy future. Additionally, the proposed system's ability to operate in both grid-connected and islanded modes makes it an ideal candidate for remote and off-grid renewable energy applications. The proposed solution's scalability and modularity also make it suitable for large-scale renewable energy integration. The proposed system is verified through MATLAB/Simulink and PLECS simulations, demonstrating its effectiveness in ensuring robust and efficient operation.

Andronov-Hopf Oscillator (AHO)↗