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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Metasurface holographic optical traps for ultracold atoms

We propose metasurface holograms as a novel platform to generate optical trap arrays for cold atoms with high quality, efficiency, and thermal stability. We developed design and fabrication methods to create dielectric, phase-only metasurface holograms based on titanium dioxide. We experimentally demonstrated optical trap arrays of various geometries, including periodic and aperiodic configurations with dimensions ranging from 1D to 3D and up to a few hundred trap sites. We characterized the performance of the holographic metasurfaces in terms of the positioning accuracy, size and intensity uniformity of the generated traps, and power handling capability of the dielectric metasurfaces. Our proposed platform has great potential for enabling fundamental studies of quantum many-body physics, and quantum simulation and computation tasks. The compact form factor, passive nature, good power handling capability, and scalability of generating high-quality, large-scale arrays also make the metasurface platform uniquely suitable for realizing field-deployable devices and systems based on cold atoms.

42 ENGINEERING↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2020

Distribution Feeder-Scale Fast Frequency Response via Optimal Coordination of Net-load Resources Part II: Large-Scale Demonstration

This work is the second of a two-part series in which we develop and experimentally demonstrate a hierarchical control solution for optimally coordinating thousands of deferrable loads and distributed energy resources (DERs) to provide fast frequency response (FFR) from an entire distribution feeder. In Part I, we developed and proved practical algorithms for fast, cost-based optimal dispatch and for determining the optimal amount of headroom to operate solar inverters with to support FFR dispatch while minimizing opportunity cost. Simulation results in Part I demonstrated the advantages of the hierarchical dispatch approach in being able to maintain fast solution times needed for FFR even when the problem size increases. In Part II, we implement the algorithms developed in Part I in a novel, large-scale power hardware-in-the-loop experiment including embedded controllers and more than 100 powered appliance loads and DER connected to a simulated real-world distribution system with more than 10,000 controlled devices. Experimental results from multiple scenarios confirm that the optimal FFR dispatch approach scales well and can optimally coordinate more than 10,000 net-load resources across a distribution network while achieving hardware response times within 500 ms, which is not possible using state-of-the-art optimal coordination approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1977

Tilt-proprotor perspective

The general tilt-proprotor concept is discussed, and a more detailed look at the XV-15 aircraft is taken. The special features of the two-engine system, engine control system, and flight control system are mentioned. The main objectives of the XV-15 program are to (1) verify rotor/pylon/wing/dynamic stability and aircraft performance over a representative operational envelope, (2) assess the handling qualities and establish a safe operating envelope, and (3) investigate gust sensitivity, effects of downwash, and hover operation. With regard to rotor/pylon stability, one challenge is to be able to predict a parameter's value and then build hardware to match. The analytical program has gained some respect through aeroelastic and full-scale XV-3 demonstrations. Special concern centers around the thrust and power management system when flying at high speed when very small changes in rotor collective pitch represent large changes in thrust and power. Demonstration of the system awaits wind-tunnel and flight testing.

Few, D. D.↗
DOE OSTI2020

A Modular Optimal Power Flow Method for Integrating New Technologies in Distribution Grids

This work proposes a modular concept to build optimal power flow (OPF) models for distribution networks containing various emerging technologies under diverse ownership structures, to efficiently deal with evolving technology capabilities and information sharing or privacy constraints. This concept will support any typical OPF application (e.g., optimal dispatch of a given asset without violating grid constraints) by coordinating between grid module and technology module without the need to recreate various modeling elements as technology capability changes due to innovation. Moreover, the modularity of the proposed concept enables achieving system level objectives without sharing detailed information on module level objectives and constraints among modules. To achieve this, the proposed work develops a gradient-descent algorithm which builds upon the literature on the state-of-the-art power flow approximation. The proposed concept is demonstrated with two case studies of i) controllable loads and ii) battery energy storage system (BESS) on an actual large-scale distribution grid.

Hanif, Sarmad↗
DOE OSTI2022

Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. Traditional reactive power planning approaches do not consider intermittency, nor the possibility of coordinating the control of existing and future volt-ampere reactive resources. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool coordinates with an optimization-based volt-var operational tool for: a) modeling the coordination of all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment, and b)selecting a set of scenarios with voltage violations, derived from PV intermittency c) testing the final investment decision. The tool obtains an investment need for each intermittency scenario with a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. Two options are provided for final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

An Expanded Role for Python in Expediting System Simulation Development

The Python programming language has traditionally been used as a “scripting” language and is not generally recognized as a language for building system simulations, where C++, Java, and other compiled languages are typically used. While very powerful and gaining favor for a large set of programming tasks, Python is generally regarded as not having the speed to directly do the extensive numeric computations required in large-scale simulation. This paper aims to present an approach, supported by case studies, to re-think this assertion. Directly using Python in this new role has the potential to expedite an agile, spiral cycle of development of design-test-modify for systems simulation.

programming language↗
DOE OSTI2020

Studying impacts of communication system performance on dynamic stability of networked microgrid

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time‐critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter‐dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB‐D simulation environment, along with a representative multi‐traffic, multi‐channel, multi‐protocol communication system model, developed in the network simulator (ns‐3). Second, a hierarchical engine for large‐scale infrastructure co‐simulation framework is developed to co‐simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use‐cases. While the example use‐cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

System and component development for long-duration energy storage using particle thermal energy storage

Energy storage, at various scales, will be required to maintain reliable power supply from variable renewable resources, and improve grid resilience. Long-duration energy storage (10-100 h) can substitute baseload coal power generation and increase levels of renewable power supply. Thermal energy storage (TES) has siting flexibility and the ability to store a large capacity of energy, and thus it has the potential to meet the needs of long-duration energy storage. Here, a novel TES system was developed by using solid particles as storage media and charging/discharging electricity from renewable power connected via the electric grid. The particle TES uses low-cost silica sand at 30-40$/Ton that is stable at high temperatures of >1,000 degrees C. Thus, the particle TES system has an overall low storage cost and high thermal-power efficiency. Key components of the system were conceptually designed and modeled for their performance. Conversion of electricity to thermal energy using electric heating can achieve a >98% charging efficiency, and the conversion of thermal energy back to electricity uses an air-Brayton combined power cycle with >52% thermal-to-electricity efficiency at >1,170 degrees C to achieve a >50% roundtrip efficiency after subtracting estimated plant parasitic losses. Laboratory-scale prototypes were fabricated and tested to verify their design approaches and operations relevant to product-scale components.

25 ENERGY STORAGE↗
DOE OSTI2023

A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Low-cost crushed-rock heat storage with oil or salt heat transfer

The demand for variable electricity and heat is met by fossil-fuel power plants because the power plant capital costs and the cost of storing fossil fuels are low. A low-carbon economy requires replacement of the storage function of fossil fuels to provide variable heat and electricity as needed. The Crushed Rock Ultra-large Stored Heat (CRUSH) system is a new technology with the goal to provide heat storage at an incremental capital cost of $2–4/kWh at scales of 10 s to 100 s of GWhs that enables economic daily to multi-week storage. CRUSH can be coupled to nuclear power plants, concentrated solar power (CSP) plants and thermal energy batteries to provide variable electricity and heat on demand. Sensible heat is stored in crushed rock—the lowest-cost heat-storage material. Heat is transferred to and from the crushed rock using nitrate salt or heat-transfer oils but these fluids do not store heat. Heat from a nuclear reactor, CSP plant or conversion of low-price electricity into heat is used to heat the salt or oil. The hot fluid is poured over the crushed rock, trickles downward by gravity through the rock, heats the rock and is collected by the drain pans under the crushed rock. To recover heat cold salt or oil is poured over hot crushed rock, trickles down by gravity while being heated and is collected by the drain pans under the crushed rock. The system design and engineering trade-offs are described. The technology is in the early stages of development.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1972

Completely modular thermionic reactor ion propulsion system /trips/.

Description of a nuclear reactor powered ion propulsion system which is an advanced completely modularized system lending itself to development of prototype and/or flight type components without the need for complete system tests until late in the development program. This modularity is achieved in all of the subsystems and components of the electric propulsion system, including the thermionic fuel elements, the heat rejection subsystem (head pipes), the power conditioning modules, and the ion thrusters. The use of heat pipes for cooling the collectors and as a means of heat transport to the radiator allows early prototype or flight configuration testing of a small module of the heat rejection subsystem as opposed to full scale liquid metal pumps and radiators in a large vacuum chamber. The power conditioner (p/c) is arranged in modules with passive cooling which allows complete prototype testing. The ion engines are typically matched with one or more p/c modules and are the same size for any power level propulsion system of interest.

Peelgren, M. L.↗
DOE OSTI2025

Advanced Microstructured BaTiO 3 -Embedded PVDF–HFP/PEO Film for Enhanced Triboelectric Interface in Self-Sufficient Energy Generation and Sensing

The global reliance on fossil fuels and natural gas has largely dominated the energy production field, but due to finite resource depletion and escalating greenhouse gas emissions, the immediate exploration of sustainable energy alternatives to mitigate climate change and ensure resource security has been a major concern. There has been extensive research into other more renewable methods of energy production, such as wind and hydropower. Of these current energy generation types, there are many areas of untapped potential from the mechanical movements generated ambiently not only in the large scale of power generation but also on a smaller scale. The piezoelectric and triboelectric effects are phenomena where these ambient mechanical movements can generate electrical energy. Developing a hybrid system that leverages both mechanical stress and surface charges presents an ideal opportunity to exploit these untapped energy sources. Producing a hybrid PVDF–HFP/PEO film with perovskite BaTiO 3 (BTO) enables ambient power harvesting from both mechanical movement and surface charge. The optimized cell produced a potential of up to 15 V and a current of 200 nA with a 68 kΩ resistor, a substantial increase from a base system with an average of 2.1 V and 40 nA. These hybrid TENGs offer significant potential for energy harvesting in small-scale applications, such as health monitoring devices and indicators in electric circuits.

Ybarra, Rigobert [University of Texas Rio Grande V↗
DOE OSTI2021

Scaling of Turbulent Viscosity and Resistivity: Extracting a Scale-dependent Turbulent Magnetic Prandtl Number

Turbulent viscosity νt and resistivity ηt are perhaps the simplest models for turbulent transport of angular momentum and magnetic fields, respectively. The associated turbulent magnetic Prandtl number Pr t ≡ ν t /η t has been well recognized to determine the final magnetic configuration of accretion disks. Here, we present an approach to determining these "effective transport" coefficients acting at different length scales using coarse-graining and recent results on decoupled kinetic and magnetic energy cascades. By analyzing the kinetic and magnetic energy cascades from a suite of high-resolution simulations, we show that our definitions of ν t , η t , and Pr t have power-law scalings in the "decoupled range." We observe that Pr t ≈ 1–2 at the smallest inertial-inductive scales, increasing to ≈5 at the largest scales. Furthermore, based on physical considerations, our analysis suggests that Pr t has to become scale independent and of order unity in the decoupled range at sufficiently high Reynolds numbers (or grid resolution) and that the power-law scaling exponents of velocity and magnetic spectra become equal. In addition to implications for astrophysical systems, the scale-dependent turbulent transport coefficients offer a guide for large-eddy simulation modeling.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Measurements of large scale turbulence in the solar system

Direct observations of the solar wind between 1965 and 1972 indicate the existence of significant large scale (L is greater than 10 to the 6th km) turbulence in the solar system at heliocentric distances from 1 to 3 AU. These results are based on power spectra of the solar wind proton data obtained by plasma spectrometers on Pioneer 6 and Pioneer 10. The power spectra indicate that there is a power-law frequency dependence between 10 to the -4 Hz and 10 to the -3 Hz of the power spectra of the solar wind proton number density and streaming speed between 1 and 3 AU. The slopes and the amplitudes of the power spectra provide qualitative information concerning the cosmic ray diffusion tensor between 1965 and 1972 and at heliocentric distances between 1 AU and 3 AU.

Intriligator, D. S.↗
DOE OSTI2023

Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units

We present a solution of sparse ACOPF analysis on GPU. In particular, we discuss the performance bottlenecks and detail our efforts to accelerate the linear solver, a core component of ACOPF that dominates the computational time. ACOPF solutions of two large-scale systems, synthetic Northeast (25,000 buses) and Eastern (70,000 buses) \cite{birchfield2017tamu-cases} on GPU show promising speed-up compared to CPU based solution using a state-of-the-art solver. To our knowledge, this is the first result demonstrating acceleration of sparse ACOPF on GPUs.

Power grid analysis, GPU↗