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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fixed Depth Hamiltonian Simulation via Cartan Decomposition

Simulating quantum dynamics on classical computers is challenging for large systems due to the significant memory requirements. Simulation on quantum computers is a promising alternative, but fully optimizing quantum circuits to minimize limited quantum resources remains an open problem. In this study, we tackle this problem by presenting a constructive algorithm, based on Cartan decomposition of the Lie algebra generated by the Hamiltonian, which generates quantum circuits with time-independent depth. We highlight our algorithm for special classes of models, including Anderson localization in one-dimensional transverse field $\mathrm{XY}$ model, where $\mathscr{O}$(n 2 )-gate circuits naturally emerge. Compared to product formulas with significantly larger gate counts, our algorithm drastically improves simulation precision. In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Symmetric second-harmonic generation in sub-wavelength periodically poled thin film lithium niobate

Second-harmonic generation (SHG) extensively employs periodically poled nonlinear crystals through forward quasi-phase-matching to achieve efficient frequency conversion. As poling periods approach sub-micrometers, backward quasi-phase-matching has also been demonstrated, albeit by utilizing pulsed laser drives. The realization of symmetric second-harmonic generation, characterized by counterpropagating pumps, however, has remained elusive despite theoretical predictions. The main challenge lies in achieving strong nonlinear coupling with the poling period below half the wavelength of the second-harmonic light. The recent emergence of high-quality ferroelectric lithium niobate thin films provides an opportunity for achieving precise domain control at submicron dimensions. In this paper, we demonstrate reliable control of ferroelectric domains in a thin film lithium niobate waveguide with a poling period down to 370 nm, thereby realizing highly efficient continuous-wave pumped symmetric SHG. This demonstration not only validates the feasibility of achieving subwavelength periodic poling on waveguides but could also enable submicron ferrolectric domain structures to be leveraged in integrated photonics and nonlinear optics research.

Optics↗
DOE OSTI2020

High-Temperature Particle Heat Exchanger for sCO2 Power Cycles

Particle receivers are being pursued to provide substantial performance improvements through higher temperatures (>700 °C) for more efficient and cost-effective concentrating solar power (CSP) systems with direct storage. However, the interface between the solar-collection and power-block subsystems - a high-temperature particle/supercritical CO 2 (sCO2) heat exchanger - has not been developed. The objective of this project is to design, construct, and test a first-of-a-kind particle-to-sCO2 heat exchanger. This work will enable emerging sCO2 power cycles that have the potential to meet SunShot targets of 50% thermal-to-electric efficiency, dry cooling with 40 °C ambient temperature, and $0.06/kWh for CSP systems. The development of next-generation particle-based systems and methods with potentially high consequences for improved performance and cost savings for CSP applications is an appropriate role for the government.

14 SOLAR ENERGY↗
DOE OSTI2023

Offshore Wind Market Report: 2023 Edition

The Offshore Wind Market Report: 2023 Edition provides detailed information on the U.S. and global offshore wind energy industries to inform policymakers, researchers, and analysts about technology, economic, and market trends. The scope of the report covers the status of over 293 global operating offshore wind energy projects as well as the broader global pipeline of projects in various stages of development through December 31, 2022. To provide up-to-date information and discussion on this emerging industry in the United States, this report tracks the significant U.S. domestic industry progress and events from January 1, 2022, through May 31, 2023. The U.S. offshore wind energy project development pipeline has reached a potential generating capacity of over 52 gigawatts, and the industry has seen strong support from state and federal governments (such as from the Inflation Reduction Act of 2022 and the announced Floating Offshore Wind Shot to reduce the cost of floating wind by 70%). There are contracts for over 17 gigawatts of the electricity from these offshore wind projects and state policies are in place to procure over 42 gigawatts by 2040. Although some projects are facing economic headwinds due to rising costs and higher interest rates (corresponding to project cost increases of 11% - 30% in 2022), there has still been significant investment in a domestic supply chain (including manufacturing facilities, new vessels, and upgraded or planned ports). Technologies continue to evolve as offshore wind turbines in the 15-megawatt class advance towards commercial production. Key offshore wind energy market indicators, such as commercial leasing, state energy planning targets, procurement policies, offtake agreements, and federal support for U.S. jobs and supply chain development, point toward sustained market growth when viewed together, but the macroeconomic hurdles facing the first generation of commercial projects could significantly stunt that growth.

17 WIND ENERGY↗
DOE OSTI2024

Electric Vehicles as Mobile Power

Electric vehicles as mobile power (EV-AMP) can allow Texas Army National Guard and others to leverage as few as four electric vehicles (EVs) to provide emergency energy storage for 24 hours by installing bidirectional chargers and associated dark-start equipment. The presence of four or more EVs operating within a regional context creates a more resilient network than a single diesel generator or BESS, and the fleet of vehicles can travel to one of several locations equipped with bidirectional chargers for the service of critical loads.

25 ENERGY STORAGE↗
DOE OSTI2021

The Sonora Brown Dwarf Atmosphere and Evolution Models. I. Model Description and Application to Cloudless Atmospheres in Rainout Chemical Equilibrium

Here we present a new generation of substellar atmosphere and evolution models, appropriate for application to studies of L-, T-, and Y-type brown dwarfs and self-luminous extrasolar planets. The models describe the expected temperature-pressure profiles and emergent spectra of atmospheres in radiative-convective equilibrium with effective temperatures and gravities within the ranges 200 ≤ T eff ≤ 2400 K and $2.5\leqslant \mathrm{log}g\leqslant 5.5$. These ranges encompass masses from about 0.5 to 85 Jupiter masses for a set of metallicities ([M/H] = – 0.5 to + 0.5), C/O ratios (from 0.5 to 1.5 times that of solar), and ages. These models expand the diversity of model atmospheres currently available, notably to cooler effective temperatures and greater ranges in C/O. Notable improvements from past such models include updated opacities and atmospheric chemistry. Here we describe our modeling approach and present our initial tranche of models for cloudless, chemical equilibrium atmospheres. We compare the modeled spectra, photometry, and evolution to various data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Perspective on Solar Energy Materials, Design and Discovery, Defects, Disorder, and Interfaces

The energy transition still faces a daunting math. Currently, more than three quarters of final energy consumption occurs in form of fuels vs less than one quarter in electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, meeting the terawatt challenge requires enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies, for example via solar thermochemical routes. In this presentation, we will review successes and challenges in photovoltaic materials from silicon to CdTe to halide perovskites and potential emerging materials, with a particular perspective on the role of first principles calculations and predictions. Materials design and discovery is evolving to incorporate defects, disorder, and interfaces. These phenomena play also an important role in the thermochemical generation of hydrogen, allowing to utilize synergies between renewable electricity and fuels in computational research. At the same time, machine learning approaches are increasingly geared toward properties of non-ideal materials, for example defect formation, allowing the screening for more complex material behavior as needed for the respective applications.

fuels↗
DOE OSTI2020

Generation of camelina mid-oleic acid seed oil by identification and stacking of fatty acid biosynthetic mutants

Modifying oilseeds to obtain a desired fatty acid composition is often necessary to enable use as feedstocks for specific applications such as food processing, biofuels, or biolubricants. A mutant population of camelina (Camelina sativa), an emerging specialty oilseed crop, was screened by high-throughput gas chromatography for lines with altered seed oil fatty acid composition. By leveraging knowledge of fatty acid synthesis in Arabidopsis thaliana, mutations in orthologs of FATTY ACID ELONGASE1 (FAE1), FATTY ACID DESATURASE2 (FAD2), FATTY ACID DESATURASE3 (FAD3), and β-KETO-ACYL-ACP SYNTHASE II (KASII; FAB1) were identified. The mutations altered conserved amino acid residues in the encoded proteins. The ability of the mutations in FAE1, FAD2 and FAD3 to affect enzyme function was demonstrated by comparing in vivo activities of wild-type and mutant alleles in yeast. In addition, expression of wild-type cDNA in camelina complemented fatty acid phenotypes of these mutants. As camelina has a hexaploid genome, the effect of a mutation in one of the three homeologs for each gene resulted in no or less severe growth phenotypes compared to similar mutations in Arabidopsis. Mid-oleic oils with nearly 40 % oleic acid and reduced very long-chain (≤C20) fatty acid content were obtained by crossing to obtain a fae1c/fad2a/fae1a/fad3a quadruple mutant. Little effect on total seed oil content was observed in the stacked mutant line. Finally, the resulting mid-oleic acid oil had improved oxidative stability due to reductions in polyunsaturated fatty acid content, increasing its utility for biofuels and other applications.

09 BIOMASS FUELS↗
DOE OSTI2026

Roper Resonance Structure and Exploration of Emergent Hadron Mass from CLAS Electroproduction Data

The N(1440)1/2 + nucleon resonance, first identified in 1964 by Roper and collaborators in analyses of πN hadroproduction data, has continued to provide pivotal insights that serve to advance our understanding of nucleon excited states. In this contribution, we present results from studies of the structure of the Roper resonance based on exclusive πN and π + π − p electro production data measured with the CLAS detector at Jefferson Lab. These analyses have revealed the Roper resonance as a complex interplay between an inner core of three dressed quarks and an external meson–baryon cloud. Analyses of the CLAS results on the evolution of the Roper resonance electroexcitation amplitudes with photon virtuality Q 2 , within the framework of the Continuum Schwinger Method, have conclusively demonstrated the capability to gain insight into the strong interaction dynamics responsible for generating more than 98% of hadron mass. Further extension of such studies to higher Q 2 , through experiments currently underway with the CLAS12 detector and in the future with a potential CEBAF energy upgrade to 22 GeV, offers the only foreseeable opportunity to explore the full range of distances where the dominant portion of hadron mass and resonance structure emerge.

Mokeev, Victor I. [Thomas Jefferson National Accel↗
DOE OSTI2023

An Assessment of Deploying Advanced Pumped Storage Hydropower Technology in U.S. Electricity Markets

Pumped storage hydropower (PSH) is a type of hydropower technology where energy can be stored and generated by moving water between two reservoirs of differing elevations. In addition to providing 97% of the total utility-scale hydropower storage in the United States, PSH plants have operational characteristics - such as high ramp rates and the ability to provide reserves - that contribute to greater flexibility and reliability of the power grid. New PSH technologies can provide additional flexibility beyond existing, fixed-speed units. With the emergence of high levels of variable renewable energy resources (e.g., wind and solar), energy storage is expected to be crucial to the reliability and reliance of the power grid in a low-carbon future. At diurnal and longer durations, PSH plants have some of the lowest costs per unit of energy, have been proven to be reliable and efficient, are not cycle-limited, and typically have long lives, often exceeding 50 years. The work presented here focuses on a new generation of PSH: ternary PSH and quaternary PSH (together referred to as T/Q-PSH). Given recent experience in Europe, grid operators in the United States and elsewhere are increasingly focusing their attention on T/Q-PSH as a proven, financeable technology that can offer utility-scale, long-duration, fast-acting energy storage capabilities and grid services. T/Q-PSH differs from conventional PSH in that it can provide fast-response ancillary services during both generating and pumping operations. This capability together with fast mode switching times are key attributes that make T/Q-PSH technologies attractive for managing and stabilizing electricity systems with high amounts of variable renewable energy.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2022

Controlling Structural, Electronic, and Energy Flow Dynamics of Catalytic Processes through Tailored Nanostructures

MoS 2 (molybdenum disulfide) is a highly-versatile catalyst material for support of numerous reactions from hydrodesulfurization and denitrogenation to the focus of this renewal proposal: hydrogenation of CO/CO 2 towards (higher) alcohols. At the same time, MoS 2 is a non-toxic, environmentally-benign and rather inert material- which under ambient conditions for some time has served as a lubricant and, more recently, as next-generation electronic material. The apparent contrast between inertness and stability in ambient, and catalytic activity under reactive conditions is puzzling and calls for a synergistic theoretical and experimental investigation with the long-term objective of enabling the rational design of MoS 2 -based catalysts for alcohol-formation reactions by providing a microscopic understanding of the environmental factors that determines site activity and selectivity. Our research project seeks answers to the questions (a) what conformation does MoS 2 adopt under reaction conditions (as opposed to that under ultrahigh vacuum and low temperatures)?; (b) what reaction pathways exist on such a material?; (c) how can the local environment of the active sites be manipulated so as to make MoS 2 an efficient catalyst for production of higher alcohol from syngas? In particular, research strategies will explore how the basal plane composed of sulfur atoms can be activated so as to exhibit a reactivity of its own, by addressing three research targets and building on extensive preliminary and enabling work: (1) vacancies and vacancy aggregates on the basal plane; (2) non-local catalyst transformation through alkali doping, hydrogenation and phase transition; (3) fabrication of a metal-nanoparticle-activated MoS 2 system, in which particle anchoring, reactive sites and pathways as well as selectivity are controlled by design, as an example of predictive development of a catalyst material,. All strategies are directed to improve the efficacy of the key reactive sites and selectivity of chemical pathways by design, to replace the inefficient methodology of trial and error in catalyst development. This research project represents a synergistic combination of computational guidance, foundational surface-science-based experiments and validation under reactive conditions that aims at transformative new insights into the working of MoS 2 -based hydrogenation-catalysts, a topic squarely at the center of the interest of DOE BES. Guided and led by Talat Rahman, a computational physicist, this project will apply density functional theory to understand structure, reaction pathways and chemical potentials associated with MoS 2 -based CO/CO 2 hydrogenation, augmented by kinetic Monte Carlo methods for reaction rates and prefactors as well as ab-initio molecular dynamics for evaluation of thermal stability. Complementary experimental input and validation will originate from co-PI Ludwig Bartels, a physical chemist and materials scientist, whose group focuses on local imaging and preparation of MoS 2 materials, from co-PI Peter Dowben, an experimental physicist, whose group is expert in the spectroscopy of occupied and unoccupied electronic states, and from senior collaborator Michael White of Brookhaven National Laboratory, whose group generates high-resolution electronic and activity information on size-selected well-defined metal chalcogenide clusters. This collaborative effort will enable a comprehensive understanding of the correlation of structural integrity and catalytic activity of MoS 2 in forms ranging from extended films to individual particles with known geometries and binding sites. Alcohol formation from syngas is a rapidly emerging application that has great potential through facile, economic, and decentralized biomass gasification. CO 2 activation is one of the most pressing concerns of our time: increasing CO 2 levels in the atmosphere change the climate and expose the globe to environmental transformations with the potential for enormous economic and societal impact. We will investigate CO/CO 2 hydrogenation via an interdisciplinary research collaboration with established synergy – one that, in accordance with the mission of the DOE, involves accredited Hispanic-Serving Institutions and that, through student exchange with international collaborators and National Labs, will directly benefit a broad spectrum of communities and generate human resources in sciences essential to their future.

2D materials↗
DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Salathe, Marco↗
DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Prins, Nicholas↗
DOE OSTI2025

Geologic hydrogen as an emerging fuel: experimental insights, thermodynamics, kinetics, and reactive transport modeling

Geologic hydrogen (GeoH 2 ) is emerging as a viable clean energy source. It is largely produced through serpentinization, a geological process in which ultramafic rocks react with water under suitable temperature and pressure. Here, this review synthesizes the current understanding of H 2 generation by serpentinization, with an emphasis on reaction mechanisms, kinetics, and thermodynamics, as well as on modeling flow and transport of reacting fluids in geological formations. We describe the role of mineral assemblages, such as olivine and pyroxene, fluid-rock interactions, and catalytic surfaces, in influencing GeoH 2 yield and reaction rates. By integrating models of reaction kinetics, subsurface reactive flow and transport, and the serpentinization process, and by accounting for the thermodynamic state of the system, this review aims to guide future GeoH 2 research and to evaluate the potential of natural hydrogen as a sustainable clean energy source.

Moradi, Rasoul [Univ. of Southern California, Los ↗