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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗
DOE OSTI2026

Strategies for Superconducting Transmon Qubits with Millisecond T1 Relaxation Time

Enhancing coherence in superconducting transmon qubits requires suppressing dielectric loss and quasiparticle-mediated dissipation at metal–substrate, metal–vacuum, and substrate–vacuum interfaces. We recently demonstrated a five-fold enhancement of the energy relaxation time (T₁) by encapsulating Nb thin films with a low-loss passivation layer that inhibits NbOₓ formation, thereby reducing two-level system (TLS) participation at the metal surface. To extend T₁ into the millisecond regime, we are implementing a comprehensive materials- and process-level optimization strategy. This includes engineered substrate surface treatments, evaluation of alternative low-loss superconducting and dielectric material stacks, development of ultra-low-loss capping layers, and redesigns of transmon geometries to suppress electric-field participation ratio in lossy regions. Additionally, we are pioneering novel etching processes to further lower surface participation ratios. We are also exploring novel Josephson-junction materials and device layouts to enhance coherence further. We report T₁ measurements from these efforts, with the leading devices achieving relaxation times>1 ms.

Crisa, Francesco↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
NASA NTRS2023

Exploring the Complexities of Drug Formulation Selection, Storage, and Shelf-Life for Exploration Spaceflight

Medications have been a part of space travel dating back as far as the Apollo missions. Currently, medical kits aboard the ISS contain medications and supplies to help crew members cope with a variety of possible medical events. NASA reported that 1,867 medical events occurred from 1981 to 1998 on space shuttle flights, STS-1 to STS-89; 498 out of the 508 crewmembers on those flights reported experiencing a medical event other than space motion sickness. In 2000, the Institute of Medicine (IOM) convened a committee of experts, Committee on Creating a Vision for Space Medicine during Travel beyond Earth Orbit, to examine the issues surrounding astronaut health and safety for long duration space missions. The primary theme of the committee’s final report is that there is not enough known about the risks to human health during long-duration missions beyond Earth’s orbit and ways to effectively mitigate those risks in an environment of deep space. In 2014, the IOM convened the Committee on Ethics Principles and Guidelines for Health Standards for Long Duration and Exploration Spaceflights and released a report emphasizing the importance of prevention, mitigation, and treatment of major risks to human health during exploration spaceflight. NASA’s Human Research Program has organized five distinct categories of spaceflight hazards summarized by the acronym “RIDGE” (Space Radiation, Isolation and Confinement, Distance from Earth, Gravity fields, and Hostile/Closed Environments) that astronauts may encounter during exploration spaceflight. From those hazards, NASA further derived 30 of the most critical human health and performance risks, including limits to medical care resulting from pharmaceutical degradation. As we prepare for more distant exploration missions to Mars and beyond, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. The present editorial will summarize the current state of knowledge regarding innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

drug degradation risk assessment↗
DOE OSTI2023

Active Site Engineering via Optimizing the Heterogeneous Support Structure for Single-Atom Catalysis

Supported single-atom catalysts show a large range of activities and selectivities that depend on the local environment of the catalytic sites. Here a theory-based optimization strategy is presented that is based on a density functional theory determination of the transition states and intermediates for a low-dimensional coordinate representation of the heterogeneity of the active sites. The approach is applied to a vanadium catalyst on an amorphous SiO 2 support that involves a large kinetic network described using a full chemistry model. Without assuming a priori scaling relations or mechanism reduction, the optimal state of heterogeneity is found to lie at atomic configurations where the activation energies for two distinct key chemical processes are equal. It is found a posteriori that the behavior of the system is consistent with linear free energy scaling relations in the randomness parameters. The energetic span theory proves quite useful in reducing the full chemistry model to a small number of key reactions. The use of a nonlinear optimization algorithm in combination with energetic span theory provides significant simplification in treating disordered systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Southwest Wind Power R&D Test Site Development at the National Wind Technology Center (CRADA CRD-12-00472 Final Report)

Southwest Windpower, Inc. (SWWP) has been designing and distributing small wind turbines for more than 22 years and is the recognized global leader in the design, manufacturing and distribution of small wind systems (400-3000 watts). The company has been a pioneer in the development of wind technology and has built and shipped more than 170,000 wind turbines to over 120 countries worldwide. Headquartered in Flagstaff AZ, SWWP has sales representatives in over 88 countries. Applications for SWWP systems include residential homes, commercial properties, micro grids, remote cabins, telecom transmitters, offshore platforms, water pumping and sailboats. In addition, U.S. Department of Defense uses SWWP’s products in Forward Operating Bases (FOB’s), USGS uses them for remote monitoring of glacier movements and the CIA uses them to provide power seismic monitoring. Today, SWWP plans development of new technologies to address distributed energy market needs and become even more aggressive in international markets. This strategy includes extensive Research, Development, Demonstration & Deployment (RDD&D) activities to improve existing production line into more simple, reliable, and cost-effective wind turbine systems. Important part of such activities is the field testing of the new systems, which should provide: (i) validation of the reliable operation of hardware and software; (ii) validation of numerical models of the system and its components; (iii) field comparison of various control and optimization strategies; (iv) customized reliability testing; (v) customized monitoring and analysis testing; and (vi) IEC certification testing (vii) filed demonstration of system performance and reliability. Such field testing facility should encompass multiple towers and appropriate data acquisition systems. SWWP is exploring to move part of the engineering department to Colorado, and execute its RDD&D strategies in this new location. Partnership with NREL will provide great insights into SWWP’s RDD&D processes and more efficient commercialization by utilizing NWTC as the field testing site, and working close with NREL personnel. NWTC is ideally suited to strain the design. NREL is very experienced with customized and IEC testing. During this project, Southwest Windpower aims to improve the value proposition of distributed renewable energy, and make it a competitive choice in the energy markets. As of today, more then 170,000 small wind turbines, manufactured in the U.S., are installed in more then 180 countries. With the improved value proposition, Southwest Windpower wants to create and address new market needs, substantially increase its sales domestically and internationally, which should have positive impact on the creation of American jobs, competitiveness of U.S. economy, export or U.S. products, and the use of renewable energy.

17 WIND ENERGY↗
DOE OSTI2020

Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence

Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2014

Aircraft Trajectory Design Based on Reducing the Combined Effects of Carbon-Dioxide, Oxides of Nitrogen and Contrails

Aircraft operations need to meet the combined requirements of safety, efficiency, capacity and reduced environmental impact. Aircraft routes can be made efficient by flying wind optimal routes. However, the desire to reduce the impact of aviation emissions and contrails may result in trajectories, which deviate from wind optimal trajectories leading to extra fuel use. The lifetime associated with different emissions and contrails varies from a few hours to several hundred years. The impact of certain gases depends on the amount and location of the emission, and the decision-making horizon, in years, when the impact is estimated. The Absolute Global Temperature Potential (AGTP) is used as a metric to measure the combined effects of emissions and contrails. This paper extends earlier work by the authors to include the effect of oxides of nitrogen in the development of aircraft trajectories to reduce the combined effects of carbon dioxide, oxides of nitrogen (NOX) and contrails. The methodology is applied to air traffic in the continental US. The paper shows the trade-offs between reducing emissions and the cost of extra fuel using a fuel sensitivity index, defined as the reduction in AGTP per kg of fuel. The paper shows the performance of the optimization strategy for decision intervals of 10, 25 and 100 years. Based on the simplified models, the inclusion of NOX emissions has a slight influence on the minimal climate impact trajectories when the decision horizons are around 25 years.

air trafic optimization↗
DOE OSTI2025

Volumetric Additive Manufacturing of Dormant Catalytic Chemistries to Generate Silicone Micro‐ and Millifluidic Devices and Instant Molds

Tomographic volumetric additive manufacturing (T‐VAM) rapidly prints solid objects within minutes, accessing photochemistries that are traditionally challenging for layer‐based additive manufacturing methods. This includes high‐viscosity materials, air‐free chemistries, and solid‐state systems. Catalytic chemistries are appealing as a pathway to engineering advanced materials, including tough thermosets, silicone elastomers, and complex block copolymers. However, photoactivated dormant catalytic chemistries, where the catalyst irreversibly activates upon exposure to light, are incompatible with typical tomographic VAM approaches. To address this limitation, a zero‐dose optimization strategy is devised to preserve dormant catalysts in desired regions by keeping them unexposed to light. VAM printed micro‐ and millifluidic devices and instant molds are successfully produced within minutes in silicones polymerized using photoactivated dormant platinum photohydrosilylation catalysts. The printed channels are programmed to be 500 and 2500 µm for the micro‐ and millifluidic devices, and print fidelity is assessed by X‐ray computed tomography. This work demonstrates the potential of zero‐dose optimization to expand the range of chemistries accessible for VAM, enabling the rapid fabrication of complex devices.

Additive Manufacturing↗
DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Programming Strategies for Irregular Algorithms on the Emu Chick

The Emu Chick prototype implements migratory memory-side processing in a novel hardware system. Rather than transferring large amounts of data across the system interconnect, the Emu Chick moves lightweight thread contexts to near-memory cores before the beginning of each remote memory read. Previous work has characterized the performance of the Chick prototype in terms of memory bandwidth and programming differences from more typical, non-migratory platforms, but there has not yet been an analysis of algorithms on this system. This work evaluates irregular algorithms that could benefit from the lightweight, memory-side processing of the Chick and demonstrates techniques and optimization strategies for achieving performance in sparse matrix-vector multiply operation (SpMV), breadth-first search (BFS), and graph alignment across up to eight distributed nodes encompassing 64 nodelets in the Chick system. We also define and justify relative metrics to compare prototype FPGA-based hardware with established ASIC architectures. The Chick currently supports up to 68x scaling for graph alignment, 80 MTEPS for BFS on balanced graphs, and 50% of measured STREAM bandwidth for SpMV.

Hein, Eric↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗
DOE OSTI2025

Characterization of Kariya ( Hildegardia barteri (Mast.) Kosterm) Seed Oil Fatty Acid Methyl Ester Prepared from Basic Catalytic Transesterification

The rising global energy demand, alongside concerns regarding environmental deterioration due to the use of fossil fuels, has spurred extensive investigation into renewable energy alternatives. Biomass-derived biodiesel, especially from lesser-known oil sources, emerges as a promising option. This research focuses on analyzing the fatty acid methyl esters (FAMEs) derived from Kariya (Hildegardia barteri (Mast.) Kosterm) seed oil through basic catalytic transesterification using gas chromatography–flame ionization detector (GC–FID) analysis, assessing its potential as a biodiesel feedstock. Oil extraction from Kariya seeds was carried out using three solvents (n-hexane, ethanol, and a 1:1 blend of hexane and ethanol), followed by transesterification with methanol. Gas chromatography–mass spectrometry (GC–MS) and GC–FID analyses were utilized to identify and quantify FAMEs in the resulting biodiesel. The results revealed various FAMEs, including methyl myristate, methyl palmitate, methyl stearate, methyl oleate, methyl linoleate, and methyl linolenate. Significant differences in FAME composition were observed among the samples, with hexane–ethanol Kariya oil biodiesel (HE-KOB) showing the highest FAME content (76.1%). This combination of solvents exhibited synergistic effects on the composition of HE-KOB, suggesting potential optimization strategies for biodiesel production. Fourier transform infrared spectroscopy (FTIR) provided additional insights into the molecular composition of the biodiesel samples, confirming their biodiesel nature through the identified functional groups such as methyl, methylene, hydrocarbon, ester, aldehyde, and alkene. Thermogravimetric analysis (TGA) for thermal decomposition also gave an insight into FAME composition and its contribution to the degree of conversion of biodiesel to energy. These findings highlight the feasibility of utilizing Kariya seed oil as a biodiesel feedstock, emphasizing the importance of solvent selection and transesterification conditions in optimizing FAME yield and composition. This research contributes to the exploration of underutilized oil sources for sustainable biodiesel production, aligning with the global shift towards cleaner and renewable energy sources.

09 BIOMASS FUELS↗
NASA NTRS2020

Capabilities Development: From International Space Station and the Moon to Mars

The President of the United States, in signing Space Policy Directive-1, directed the NASA Administrator “to lead an innovative and sustainable program of exploration with commercial and international partners to enable human expansion across the solar system and to bring back to Earth new knowledge and opportunities. Beginning with missions beyond low-Earth orbit (LEO), the United States will lead the return of humans to the Moon for long-term exploration and utilization, followed by human missions to Mars and other destinations.” NASA is charged to land American astronauts on the lunar South Pole in 2024 and to continue a campaign of sustainable lunar surface exploration in order to develop necessary technologies and capabilities to enable initial human missions to Mars. NASA’s lunar surface exploration plans are part of a continuum of activities utilizing platforms in low Earth orbit (LEO), cislunar space, and the lunar surface to demonstrate advanced technologies, advance operations concepts, and develop countermeasures to lessen the impacts of the space environment and long duration exposure on the crew working in space. NASA is using a capability-driven approach to identify critical gaps to be addressed as part of a focused program to reduce risk for future deep space exploration missions building to eventual human missions to the surface of Mars. Teams of discipline experts from across NASA identify capability gaps between the current state of the art and the needs of proposed exploration missions and develop integrated strategies and roadmaps for filling those gaps. These inputs include assessment of platform needs for demonstration and testing of new capabilities. Generally, the International Space Station (ISS) and Gateway are needed for demonstration of capabilities for Mars transit, while Lunar surface activities focus on development of capabilities and operational protocols for Mars surface. This paper discusses the activities required to advance critical exploration capabilities, focusing on selection of demonstration and test location based upon the unique environments and characteristics of the ISS, Gateway, and potential lunar surface assets. The optimal strategy will be a combination of ISS/LEO, Gateway, and lunar surface testing; however, not all capabilities require a deep space exploration missions.

Boggs, Kathleen Gallagher↗
NASA NTRSDate not supplied

Optimal Communication Topology Determination and Sensor Selection for Independent Airspace Surveillance

The paper presents an approach to sensors selection and network topology determination for independent airspace surveillance with maximum outcome and minimum cost using ground based distributed sensing, computing and communication network infrastructure. The selection criteria includes minimum estimation error, maximum airspace coverage, minimum communication time and power consumption while guaranteeing the system observability and providing in-time quality information to a monitoring observer. The developed algorithm uses multi-objective optimization strategy taking into account trade-offs between conflicting objectives and relaxations for in time implementation. It is implemented utilizing graph theoretic tools. The approach is validated in a desktop simulation environment using synthetic sensors data generated for a simulated multi-vehicle flight scenario in the selected regional airspace.

Distributed sensing↗
DOE OSTI2020

Thermal power plant upgrade via a rotating detonation combustor and retrofitted turbine with optimized endwalls

Over the past decade, pressure gain combustion research has promised over 10 percentage-points of increase in power plant thermal efficiency. Alas, to realize such potential gain, one must effectively couple the turbine with the detonation combustor, whose exhaust conditions differ substantially from current state of the art gas turbines. This paper presents a modeling approach that enables a superior thermodynamic cycle with a rotating detonation combustor and a retrofitted gas turbine by including a diffuser downstream of the combustor and by contouring the turbine endwall, while preserving the airfoil geometry. We propose a multi-step optimization strategy, parametrizing the endwall geometry with a few control points, without altering the airfoil geometry, and with the stage turbine efficiency as objective function. In a first step the turbine performance is assessed with steady inlet conditions by solving the steady three-dimensional Reynolds-Averaged Navier-Stokes equations. In a second step, the inlet conditions are unsteady, as predicted from a detonation combustor and a diffuser, and three-dimensional full unsteady simulations are performed with an unsteady Reynolds-Averaged Navier-Stokes solver. By altering the vane endwall, the steady optimization yielded an efficiency increase of 12% relative to the baseline, while the unsteady optimization resulted in 21% increase compared to the datum turbine. Finally, a full engine analysis demonstrated the superiority of pressure gain combustion which included a realistic thermodynamic cycle of the combustor, the diffuser and the optimized turbine components.

42 ENGINEERING↗
DOE OSTI2020

Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing

Additive manufacturing (AM) enables the fabrication of complex designs that are difficult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is qualified for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatability. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This work describes a fast, scalable method for part-scale process optimization of arbitrary geometries. Additionally, the computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of fidelity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.

3D printing↗