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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Additively manufactured multiplexed inertial coalescence filters

Multiphase flows often pose a significant challenge to the efficient and reliable design of thermofluidic systems. This paper describes multiplexed inertial coalescence filters composed of parallel helical pathways, designed to capture fine droplets (<40 µm) through inertial separation while maintaining a low pressure drop (<400 Pa). Filtration efficiencies for 7 µm and 30 µm droplets were characterized for varying flow conditions, with complete capture observed above a threshold flow rate. Models for filtration efficiency and pressure drop were developed and validated against experimental results to allow system design and optimization, enabled by the tunable additive manufacturing approach used to fabricate the filters. Filter quality factor was computed for varying droplet sizes, showcasing exceptional quality factor when compared to state-of-the-art filters documented in the literature. In conclusion, this multiplexed inertial coalescence filtration approach could find use in dehumidification systems, fog harvesting, chemical reactors, and microgravity droplet capture.

3D Printing↗
DOE OSTI2023

Automating STEM Aberration Correction via Bayesian Optimization

Multipole aberration correctors have given scanning transmission electron microscopes (STEM) the ability to produce high-quality, atomic-resolution images, enabling STEM to be a key tool in material sciences for characterizing the structure and composition of materials. However, the process of correcting these aberrations typically requires human input and is accomplished using scanned STEM or Ronchigram images at different focii and beam tilts. In this work, we demonstrate an automated on-sample aberration correction system using Bayesian Optimization. We have developed a Python-based server able to communicate with the CEOS DCOR aberration corrector and the Thermo Fischer microscope scripting interface. This server allows us to change aberrations, acquire images and perform basic image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Startup regime of high-efficiency tapering-enhanced FEL oscillator

In this paper, we present a design of a high-efficiency high-gain free-electron laser oscillator based on the use of a strongly tapered undulator for extracting energy from high-brightness electron beams. We provide an analytical model of the setup followed by numerical simulations for lasing at the wavelength of 13.5 nm. We discuss the optimization of the system in steady state and the conditions necessary for the pass-per-pass buildup of the power from shot noise level. We propose the use of fast phase shifters as a way to accelerate the buildup. Finally, we present time-dependent simulations of the oscillator and discuss the role of spectral filtering. The optimized working point yields a total energy conversion efficiency from the electron beam to output radiation above 1% at the wavelength of 13.5 nm.

Beam dynamics↗
DOE OSTI2022

Industrialized and Robotic Construction Advances in Terrestrial Construction and Opportunities in Space Construction

Advanced Building Construction (ABC) methods such as industrialized prefabrication and robotically controlled additive deposition are becoming promising technologies to support rapid construction of buildings at volume and scale. However, apart from the structural shell of the building, optimal integration of other systems – mechanical, electrical, plumbing, thermal, power generation, storage and distribution, is key to enabling functional buildings. Leveraging ABC methods to integrate such systems in the buildings requires the building design to be optimized for constructability constraints imposed by the industrialized and robotically controlled production setups entailing such ABC methods. The Industrialized Construction Innovation (ICI) team at the National Renewable Energy Laboratory (NREL) has been focusing on research and development of such ABC technologies and processes that can lead to production of buildings which are resilient to extreme climatic conditions at reduced cost and time. There is a potential synergy between such advancements in terrestrial construction, which can be leveraged for the benefit of space (orbital and extra-terrestrial) construction. Similarly, advances in space construction can lend itself to accelerated adoption of robotic construction methods in terrestrial construction. This presentation will give an overview of selective research efforts of the ICI team at NREL, along with a vision for potential collaborations with agencies like NASA.

3D printing↗
DOE OSTI2024

Component-to-Optimization Workflow Demonstration

This report aims to demonstrate workflow-generating algorithms for optimizing dispatch across a broad range of Integrated Energy System applications using the Framework for Optimization of Resources and Economics (FORCE) tool suite. The optimization is performed at two different time scales. In the coarse time scale, the optimization focuses on a class of energy sources and consumers and aims to find the optimal combinations and flows of energy based on real-time price data information. In the fine time scale, the optimization focuses on a specific thermal energy delivery system and aims to find the optimal setpoints of components in order to meet the energy demands from coarse-time-scale optimizations. In this demonstration, the coarse-scale optimization is implemented using the newly developed Dispatch Optimization Variable Engine (DOVE), while the fine-scale optimization used Optimization of Real-Time Capacity Allocation (ORCA).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
DOE OSTI2024

Probabilistic Hydropower Flexibility Valuation: Case Studies for Boating Flow Regime

The optimal scheduling of hydropower generation holds significant importance to power system operation. The unique requirements of environmental constraints and the power system, depending on their respective objectives, demand distinct flow patterns. While power system stakeholders strive to optimize revenue in electricity markets, stakeholders from boating recreation seeks to identify flow ranges that optimize the boating experience. In pursuit of a win-win solution, this study aims to reconcile the interests of various stakeholders in hydropower scheduling. The maximum revenue from day-ahead electricity market is explored through an optimization process considering both plant operation constraints, boating flow constraints, water availability, and market prices. Results of real world case studies at a river in California show that the proposed approach can achieve dual objectives: maximizing market revenue while addressing boating recreation necessities. In addition, as the accuracy of electricity price forecasting and flow forecasting increase, the optimal revenue becomes increasingly advantageous to hydropower plant operators.

13 HYDRO ENERGY↗
DOE OSTI2025

AutoFocus: AI/ML-driven real-time wavefront diagnostics to autonomously align and optimize X-ray optics

We present an integrated system that combines advanced wavefront diagnostics with artificial intelligence (AI) to automate and optimize X-ray optics at synchrotron beamlines. This system couples real-time wavefront sensing with AI-driven control algorithms to achieve precise beam alignment, stabilization, and performance optimization. A key feature is the use of multi-fidelity transfer learning, which enables knowledge gained from both real-world beamline optimizations and ultra-realistic digital twin simulations to be effectively applied to in situ optimization. By leveraging multi-objective bayesian optimization, the system continuously refines its performance, reducing optimization time and minimizing the need for manual adjustments. Designed for seamless deployment, it operates with existing beamline hardware and provides an intuitive graphical interface. Initial deployments at the advanced photon source beamlines have demonstrated its ability to enhance beam stability, improve reproducibility, and significantly streamline alignment procedures. This AI-enhanced control framework represents a significant step toward fully autonomous beamline operation in next-generation synchrotron facilities.

Rebuffi, Luca [Argonne National Laboratory (ANL), ↗
DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗
DOE OSTI2020

Methods and algorithms for computer synthesis of holographic elements to obtain a complex impulse response of optical information processing systems based on modern spatial light modulators

The possibilities of designing optical devices for data processing and imaging based on the manipulation of coherent light beams by means of spatial light modulators (SLMs) are investigated. A review of commercially available SLMs is presented and the limitations of their complex modulation characteristics are analysed. The main problem of using present-day SLMs is the lack of the ability to modulate directly all states within a unit circle in the complex plane. In this regard, the characteristics of current methods for the synthesis of holographic elements are described that implement a given complex impulse response of the optical system and are optimal for using SLMs with purely amplitude, purely phase, and hybrid amplitude – phase modulation. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Technoeconomic Analysis of Infrastructure Buildout Scenarios

The success of CCUS deployment in the southeast will depend on the optimized pipeline network from CO 2 point sources to geologic sinks for CO 2 storage. An optimal pipeline framework will reduce both environmental impacts and pipeline building cost. However, finding an optimal pipeline/transport infrastructure design is a non-trivial task and requires simultaneous usage of large volumes of data, computational resources, and a state-of-the-art simulator. Such a transport infrastructure model must consider point sources for CO 2 capture and associated volumes, sinks for CO 2 storage, and transportation from source to sink via pipeline networks. These considerations must be addressed simultaneously and systemically in an optimization process, in which a defined objective function (i.e., capital, variable CO 2 capture, transport, and storage cost function) is required to be minimized with the consideration of practical constraints (e.g., CO 2 flow through pipelines is less than the maximum capacity). Although optimization has been widely used in subsurface resources production and CO 2 storage, its application in large scale CCUS infrastructure design is rarely reported in the literature. SimCCS, developed by Los Alamos National Laboratory, is an open-source CCUS pipeline infrastructure design toolset that facilitates the optimization of pipeline infrastructure networks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Comparative study between experimental measurements and model predictions of bubble rise velocity in molten LiCl-KCl

This research investigated the shape and rise velocities of gas bubbles (helium, nitrogen, argon, and krypton) in molten LiCl-KCl at 500 °C using a specialized optical visualization apparatus. The apparatus features a custom quartz rectangular prism cell housed in a furnace, enabling the application of the shadowgraph measurement technique. Bubbles with diameters between 0.45 and 3.2 mm were produced via a capillary, and their equivalent diameters and rise velocities were analyzed using high-resolution imaging and centroid tracking algorithms. Important non-dimensional parameters were calculated to characterize behavior. Bubbles with 0.03 < Eo < 1.32, 17 < Re < 718, and Mo order of 10 -11 were generated in the molten LiCl-KCl, revealing that bubbles with diameters less than 1.46 mm remain spherical, whereas those with larger diameters are ellipsoidal. The transition and oscillation onset for bubble shape occurred at larger diameters than those previously observed in air–water systems. Comparative analysis of the gas-molten salt data against existing air–water bubble rise velocity correlations highlighted inaccuracies in predictions, particularly across the different bubble regimes. An existing correlation was calibrated to the molten salt data to improve the bubble velocity predictions. In conclusion, this study contributes essential experimental data for the design and safety evaluation of molten salt reactors and offers insights for the optimization of sparge systems for fission product removal.

Bubble rise velocity↗
DOE OSTI2020

The Future of Renewable Energy Transmission: An Autonomous Energy Grid

The drastic price reduction in variable renewable energy, such as wind and solar, coupled with the ease of use of smart technologies at the consumer level, is driving dramatic changes to the power system that will significantly transform how power is made, delivered, and used. Distributed energy resources (DERs)—which can include solar photovoltaic (PV), fuel cells, microturbines, gensets, distributed energy storage (e.g., batteries, ice storage), and new loads (e.g., electric vehicles (EVs), light-emitting diode (LED) lighting, smart appliances, and electric heat pumps)—are being added to electric grids and causing bidirectional power flows and voltage fluctuations that can impact optimal control and system operation. Residential solar installations, customer battery systems, and EVs are all seeing rapid increases in deployments. With DER seeing such increased use, it is not unreasonable to imagine a residential electricity customer having at least five controllable DERs. In future electric grids, as more DERs are integrated, the number of active control points will be too much for current control approaches to effectively manage.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Cost-optimized energy storage operation for a grid-connected solar PV system at community and individual scales

This study provides a comparative analysis of grid-connected PV-integrated battery storage at individual and community scales. The paper addresses the challenge of managing energy demand-generation mismatch by using a battery energy storage optimization algorithm, which minimizes operational costs while accounting for battery degradation. Also, this work introduces a broader evaluation basis that includes seasonal variability, grid exchange smoothness, and scalability across different battery capacities. Results show that community-scale storage more effectively dampens grid exchange power fluctuations and reduces system costs, particularly with moderate price differences between electricity buying and selling prices and low battery capacities. The paper also analyzes the impacts of static control versus cost-optimized battery system management. Here, it is shown that the gap in system costs between the cost-optimized and static control scenarios widens as the price difference increases.

25 ENERGY STORAGE↗
DOE OSTI2023

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗