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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measuring Flexibility through Reduction Potential

As electrical loads, electric vehicles (EVs) are distinguished in many cases by their substantial flexibility. Harnessing this flexibility, however, requires a precise characterization of its timing and degree. Current approaches to flexibility characterization tend to be limited either by the complexity of their representation or their inability to capture aggregate flexibility across multiple vehicles. In this paper, we present the reduction potential matrix, a novel metric capturing fleet-level EV load flexibility while being both straightforward to calculate and intuitive to interpret. We use the metric to quantify flexibility for two operationally distinct commercial groups: freight vehicles and transit buses. While we find that both vehicle groups have substantial flexibility, its timing, magnitude, and duration varies across the groups. We describe how this variability translates into variation in the each group's role as a grid resource, and conclude with a discussion of how system planners, fleet operators, and other stakeholders can use the novel metric to cultivate symbiosis between vehicles and the grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Triboelectric Nanogenerator Repeatability and Reproducibility Study

The development of triboelectric nanogenerators (TENGs) has largely focused on optimizing output performance, often at the expense of other critical research considerations such as the development of reliable technical procedures. In particular, the reliability of reported results—specifically repeatability and reproducibility—remains underexplored and is frequently limited to brief discussion within available literature. Without rigorous validation through repeatability and reproducibility studies, the credibility and broader applicability of reported findings remain uncertain. This study addresses this gap by systematically evaluating the repeatability and reproducibility of TENG performance data. Five polymer materials—Kapton, polyethylene (PE), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF)—were investigated across all pairwise combinations of 25 total combinations for the reproducibility study and three selected pairs of the 25 samples were selected for the repeatability study. For each TENG pairing, we analyzed the methodology, experimental procedures, and resulting performance data to quantify consistency and reliability. The objective of this work is to assess the validity of the collected dataset and determine whether the observed performance trends are consistent for use in future TENG design and optimization studies. Establishing reliable and reproducible data is essential for advancing the development of high-output TENG systems and ensuring their dependable implementation in practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dynamic Asset Allocation with Expected Shortfall via Quantum Annealing

Recent advances in quantum hardware offer new approaches to solve various optimization problems that can be computationally expensive when classical algorithms are employed. We propose a hybrid quantum-classical algorithm to solve a dynamic asset allocation problem where a target return and a target risk metric (expected shortfall) are specified. We propose an iterative algorithm that treats the target return as a constraint in a Markowitz portfolio optimization model, and dynamically adjusts the target return to satisfy the targeted expected shortfall. The Markowitz optimization is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The use of the expected shortfall risk metric enables the modeling of extreme market events. We compare the results from D-Wave’s 2000Q and Advantage quantum annealers using real-world financial data. Both quantum annealers are able to generate portfolios with more than 80% of the return of the classical optimal solutions, while satisfying the expected shortfall. We observe that experiments on assets with higher correlations tend to perform better, which may help to design practical quantum applications in the near term.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Advancing Commercialization of Algal Biofuels Through Increased Biomass Productivity and Technology Integration

Cellana is a leading developer of algae-based bioproducts, and its pre-commercial production of marine microalgae takes place at Cellana?s Kona Demonstration Facility (KDF) in Hawaii. KDF is housing more than 70 high-performing algal strains for different bioproducts, of which over 30 have been grown outside at scale. So far, Cellana has produced more than 10 metric tons of algal biomass for the development of biofuels, animal feed, and high-value nutraceuticals. Cellana?s ALDUO algal cultivation technology allows Cellana to grow non-extremophile algal strains at large scale with no contamination disruptions. Cellana?s research and production at KDF have addressed three major areas that are crucial for the commercialization of algal biofuels: yield improvement, cost reduction, and the overall economics. Commercially acceptable solutions have been developed and tested for major factors limiting areal productivity of algal biomass and lipids based on years of R&D work conducted at KDF. Improved biomass and lipid productivity were achieved through strain improvement, culture management strategies (e.g., alleviation of self-shading, de-oxygenation, and efficient CO2 delivery), and technical advancement in downstream harvesting technology. Cost reduction was achieved through optimized CO2 delivery system, flue gas utilization technology, and energy-efficient harvesting technology. Improved overall economics was achieved through a holistic approach by integration of high-value co-products in the process, in addition to yield improvements and cost reductions.

09 BIOMASS FUELS↗
DOE OSTI2017

Design Considerations for Proposed Fermilab Integrable RCS

Integrable optics is an innovation in particle accelerator design that provides strong nonlinear focusing while avoiding parametric resonances. One promising application of integrable optics is to overcome the traditional limits on accelerator intensity imposed by betatron tune-spread and collective instabilities. The efficacy of high-intensity integrable accelerators will be undergo comprehensive testing over the next several years at the Fermilab Integrable Optics Test Accelerator (IOTA) and the University of Maryland Electron Ring (UMER). We propose an integrable Rapid-Cycling Synchrotron (iRCS) as a replacement for the Fermilab Booster to achieve multi-MW beam power for the Fermilab high-energy neutrino program. We provide a overview of the machine parameters and discuss an approach to lattice optimization. Integrable optics requires arcs with integer-pi phase advance followed by drifts with matched beta functions. We provide an example integrable lattice with features of a modern RCS - long dispersion-free drifts, low momentum compaction, superperiodicity, chromaticity correction, separate-function magnets, and bounded beta functions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Application of Robotics in Decommissioning and Decontamination - 12536

Decommissioning and dismantling of nuclear facilities is a significant challenge worldwide and one which is growing in size as more plants reach the end of their operational lives. The strategy chosen for individual projects varies from the hands-on approach with significant manual intervention using traditional demolition equipment at one extreme to bespoke highly engineered robotic solutions at the other. The degree of manual intervention is limited by the hazards and risks involved, and in some plants are unacceptable. Robotic remote engineering is often viewed as more expensive and less reliable than manual approaches, with significant lead times and capital expenditure. However, advances in robotics and automation in other industries offer potential benefits for future decommissioning activities, with the high probability of reducing worker exposure and other safety risks as well as reducing the schedule and costs required to complete these activities. Some nuclear decommissioning tasks and facility environments are so hazardous that they can only be accomplished by exclusive use of robotic and remote intervention. Less hazardous tasks can be accomplished by manual intervention and the use of PPE. However, PPE greatly decreases worker productivity and still exposes the worker to both risk and dose making remote operation preferable to achieve ALARP. Before remote operations can be widely accepted and deployed, there are some economic and technological challenges that must be addressed. These challenges will require long term investment commitments in order for technology to be: - Specifically developed for nuclear applications; - At a sufficient TRL for practical deployment; - Readily available as a COTS. Tremendous opportunities exist to reduce cost and schedule and improve safety in D and D activities through the use of robotic and/or tele-operated systems. - Increasing the level of remote intervention reduces the risk and dose to an operator. Better environmental information identifies hazards, which can be assessed, managed and mitigated. - Tele-autonomous control in a congested unstructured environment is more reliable compared to a human operator. Advances in Human Machine Interfaces contribute to reliability and task optimization. Use of standardized dexterous manipulators and COTS, including standardized communication protocols reduces project time scales. - The technologies identified, if developed to a sufficient TRL would all contribute to cost reductions. Additionally, optimizing a project's position on a Remote Intervention Scale, a Bespoke Equipment Scale and a Tele-autonomy Scale would provide cost reductions from the start of a project. Of the technologies identified, tele-autonomy is arguably the most significant, because this would provide a fundamental positive change for robotic control in the nuclear industry. The challenge for technology developers is to develop versatile robotic technology that can be economically deployed to a wide range of future D and D projects and industrial sectors. The challenge for facility owners and project managers is to partner with the developers to provide accurate systems requirements and an open and receptive environment for testing and deployment. To facilitate this development and deployment effort, the NNL and DOE have initiated discussions to explore a collaborative R and D program that would accelerate development and support the optimum utilization of resources. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Laser-Induced Spectrochemical Assay for Uranium Enrichment (LISA-UE)

Uranium hexafluoride (UF6) is the uranium compound typically involved in uranium enrichment process. As the first line of defense against nuclear proliferation, accurate determinations of the uranium enrichment ratio in UF6 are critical for materials verification, accounting and safeguards. Shipping gaseous UF6 samples off-site for analysis with mass spectrometry is cumbersome and costly, and results are not available for some time (months). In-field UF6 enrichment assay has the potential to substantially reduce the time, logistics and expense of sample handling. At present, COMPUCEA is the only accepted method for UF6 enrichment assay in the field. Laser-Induced Spectrochemical Assay for Uranium Enrichment (LISA-UE) is an all-optical (based on laser induced plasma emission) analytical technique intended for fieldable, accurate, precise and rapid UF6 enrichment assay. In its operation, laser induced plasma is created directly in the gaseous UF6 sample. Because different U isotopes emit at slightly different wavelengths, the isotopic information of the UF6 sample is inherently encoded in the atomic emission from the plasma. Isotopic emissions from 235U and 238U are measured simultaneously, which eliminate correlated noise from the laser induced plasma. Isotopic information of the UF6 sample can be extracted from the acquired spectrum with theoretical multi-variable non-linear spectral fitting. To date, advances made by the LISA-UE research team include optimization of the spectral window for direct gaseous UF6 enrichment assay with laser induced plasma, development of data reduction algorithms, and demonstrations of the LISA-UE technique with gaseous UF6 samples. In this presentation, the technical aspect of LISA-UE will be overviewed, the data reduction algorithm will be described, and performance of the technique will be discussed.

Chan, George↗
DOE OSTI2024

AI/ML Expo Boosting Job Performance with AI: Innovative Approaches and Success Stories

Our technology leverages artificial intelligence (AI) to enhance the user experience in High Performance Computing (HPC) environments. By analyzing user behavior and providing personalized recommendations, our AI system helps HPC users optimize their workflows and improve productivity. Additionally, we offer an advanced image similarity search feature, which utilizes AI algorithms to identify and retrieve visually similar images, saving users valuable time and effort in their research and analysis.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimal Control for Battery Storage Using Nonlinear Models

Battery storage system (BSS) becomes increasingly popular for grid applications due to the advance in battery and power electronics technologies as well as the growing need of flexibility and reserve from power systems with rapidly developed renewable generation. Successful assessment and deployment of BSS requires optimizing its operation and therefore maximizing the potential benefits. Although there exist studies that develop optimal control for evaluation or operational scheduling of BSS, modeling of charging/discharging operation and the corresponding impacts on state-of-charge (SOC) is over simplified in these methods. This paper develops an optimal control for BSS evaluation and operational scheduling using a general nonlinear model that expresses the change of SOC as a function of charging/discharging power and SOC level. The proposed method is compared with a typical existing method through a real-world energy storage evaluation project to show the significance of the proposed method.

battery, energy storage system, dynamic programmin↗
DOE OSTI2026

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗
DOE OSTI2025

Nanoindentation’s imprint on an advanced society: Toward application conditions at the extremes

Abstract As society strives for a more sustainable future, key technologies depend on advanced materials development. Nanoindentation enables highly localized nanoscale measurement of mechanical properties, crucial for optimizing the performance of functional materials. Coupled with chemical charging, variable temperatures, or high deformation speeds, it offers insights into pressing materials science challenges necessary for sustainable energy transitions. This article explores the role of nanoindentation in advancing vital next-generation technologies. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Cationic lipid/DNA complexes manufactured by microfluidics and bulk self-assembly exhibit different transfection behavior

Highlights: • Transfection efficiency of lipid/DNA complexes manufactured by microfluidics is low. • Lipid/DNA complexes prepared by microfluidics have a minor impact on cell viability. • Optimized microfluidic manufacture will produce effective and not toxic lipoplexes. Recent advances in biochemical and biophysical research have been achieved through the employment of microfluidic devices. Microfluidic mixing of therapeutic agents with biomaterials yields systems with finely tuned physical-chemical properties for applications in drug and gene delivery. Here, we investigate the role of preparation technology (microfluidic mixing vs. bulk self-assembly) on the transfection efficiency (TE) and cytotoxicity of multicomponent cationic liposome/DNA complexes (lipoplexes) in live Chinese hamster ovarian (CHO) cells. Decoupling TE and cytotoxicity allowed us to combine them in a unique coherent vision. While bulk self-assembly produces highly efficient and highly toxic MC lipoplexes, microfluidics manufacture leads to less efficient, but less cytotoxic complexes. This discrepancy is ascribed to two main factors controlling lipid-mediated cell transfection, i.e. the lipoplex concentration at the cell surface and the lipoplex arrangement at the nanoscale. Further research is required to optimize microfluidic manufacturing of lipoplexes to obtain highly efficient and not cytotoxic gene delivery systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Case Study: Hybrid Carbon Conversion Using Low-Carbon Energy Sources in Coal-Producing States

The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Optimization Methodology of Pebble Bed HTGR Start-Up and Running-in Strategy

In recent years interest in advanced reactor technologies has increased significantly. However, the methods used for analysis of traditional nuclear reactors are insufficient to consider all the different and varied advanced reactor designs without further development. One promising advanced reactor design is the pebble bed reactor (PBR). PBRs possess unique operational and fuel cycle features that require the development of specific analysis methodologies to adequately design and analyze the systems. There is a need in PBR research for a capability to analyze and optimize the process of transitioning from the start-up reactor core to the equilibrium core (known as the “running-in” of the reactor). The start-up of a PBR and the transition to the equilibrium core is a complex, multi-physics challenge that has not yet been well researched and has many opportunities for design, analysis, and optimization of the process. In this research, a methodology is defined to consider the potential strategies in PBR start-up and run-in to the equilibrium core. Multiple candidate software are considered, and their pros and cons are discussed for the PBR-specific application in the methodology. A preliminary software selection for the physics engine is made, and initial verification of key modules is performed. Software to support optimization of the reactor run-in strategies through reduced order modeling (ROM) and machine learning are considered. Challenges for a full implementation of the methodology are discussed. Additional code selections and verification of models relevant to the application are needed before full demonstration of the methodology can be achieved and an optimal strategy determined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Harnessing Quantum Capacitance in 2D Material/Molecular Layer Junctions for Novel Electronic Device Functionality

Two-dimensional (2D) materials promise advances in electronic devices beyond Moore’s scaling law through extended functionality, such as non-monotonic dependence of device parameters on input parameters. However, the robustness and performance of effects like negative differential resistance (NDR) and anti-ambipolar behavior have been limited in scale and robustness by relying on atomic defects and complex heterojunctions. In this paper, we introduce a novel device concept that utilizes the quantum capacitance of junctions between 2D materials and molecular layers. We realized a variable capacitance 2D molecular junction (vc2Dmj) diode through the scalable integration of graphene and single layers of stearic acid. The vc2Dmj exhibits NDR with a substantial peak-to-valley ratio even at room temperature and an active negative resistance region. The origin of this unique behavior was identified through thermoelectric measurements and ab initio calculations to be a hybridization effect between graphene and the molecular layer. The enhancement of device parameters through morphology optimization highlights the potential of our approach toward new functionalities that advance the landscape of future electronics.

2D materials↗