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DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

High-quality permanent draft genome sequence of the Lebeckia - nodulating Burkholderia dilworthii strain WSM3556T

Burkholderia dilworthii strain WSM3556T is an aerobic, motile, Gram-negative, non-spore-forming rod that was isolated from an effective N2-fixing root nodule of Lebeckia ambigua collected near Grotto Bay Nature Reserve, in the Western Cape of South Africa, in October 2004. This plant persists in infertile and deep sandy soils with acidic pH, and is therefore an ideal candidate for a perennial based agriculture system in Western Australia. WSM3556T thus represents a potential inoculant quality strain for L. ambigua for which we describe the general features, together with genome sequence and annotation. Lastly, the 7,679,067 bp high-quality permanent draft genome is arranged in 140 scaffolds of 141 contigs, contains 7,059 protein-coding genes and 64 RNA-only encoding genes, and is part of the GEBA-RNB project proposal.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗

NASA NTRS1997

Thermophotovoltaic Cell Technology Transferred to the Department of Energy Laboratory and a Commercial Manufacturer

Researchers in the NASA Lewis Research Center's Photovoltaic Branch have developed novel photovoltaic device, called a Monolithically Interconnected Module (MIM), for use in thermophotovoltaic (TPV) power systems. TPV power systems function by heating an emitter to produce light. This light is then converted into electricity by a photovoltaic device or a solar cell. Possible heat sources for the system include concentrated solar energy, the combustion of various fuels, and nuclear decay. NASA has an interest in TPV systems for deep space (nuclear-powered) and near-Sun (solar-powered) missions. There also are many commercial and military applications for TPV, given its potential for high efficiency, low noise, and reliable power. The Monolithically Interconnected Module consists of many small solar cells that are series-interconnected on a common substrate. The cells are fabricated from indium gallium arsenide (InGaAs), which can convert the near-infrared portion of the emitter output spectrum into electricity. The InGaAs devices are deposited on an indium phosphide (InP) substrate that provides electrical isolation. On the bottom the InP substrate is an infrared reflector that returns all the photons that are not converted by the InGaAs device back to the emitter where they are absorbed. This process helps maintain the emitter temperature and dramatically improves the system efficiency. Monolithically Interconnected Module InGaAs thermophotovoltaic cell developed by Lewis. Compared with conventional TPV cells, this TPV device has higher output voltages and lower resistive losses, higher output power density, simplified thermal management, improved reliability, and higher efficiency. The Monolithically Interconnected Module was initially developed under an internally funded effort (Director's Discretionary Fund). Development is now being funded by another government agency, and prototype devices are being produced by a commercial solar cell manufacturer.

Wilt, David M.↗

NASA NTRS2008

A Solar-Powered Enceladus Mission

We present the results of a concept design study for a New Frontiers or small Flagship-class mission to Enceladus, using solar power. By concentrating on the science objectives most critical for a Cassini follow-on, this mission maximizes the science return while maintaining a power consumption level that can be provided by a practical solar power system. The optimized instrument payload is the product of a broad science community-based Science Definition Team Study. The spacecraft and mission designs are the products of studies carried out by the GSFC Mission Design Lab and Ball Aerospace. In addition to the low isolation at Enceladus, its location deep in Saturn's gravity well makes it a challenging target to reach, meaning that careful consideration must be given to spacecraft mass and the potential mission types. This presentation summarizes the mission science objectives and payload, the dynamical work, and the notional operations plan of this mission.

Simon-Miller, Amy A.↗

NASA NTRS2013

What are the Progenitors of Compace, Massive, Quiescent Galaxies at z (equals) 2.3? The Population of Massive Galaxies at z (greater than) 3 From NMBS AND CANDELS

Using public data from the NEWFIRM Medium-Band Survey (NMBS) and the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS), we investigate the population of massive galaxies at z > 3. The main aim of this work is to identify the potential progenitors of z ∼ 2 compact, massive, quiescent galaxies (CMQGs), furthering our understanding of the onset and evolution of massive galaxies. Our work is enabled by high-resolution images from CANDELS data and accurate photometric redshifts, stellar masses, and star formation rates (SFRs) from 37-band NMBS photometry. The total number of massive galaxies at z > 3 is consistent with the number of massive, quiescent galaxies (MQGs) at z ∼ 2, implying that the SFRs for all of these galaxies must be much lower by z ∼ 2. We discover four CMQGs at z > 3, pushing back the time for which such galaxies have been observed. However, the volume density for these galaxies is significantly less than that of galaxies at z < 2 with similar masses, SFRs, and sizes, implying that additional CMQGs must be created in the intervening ∼1 Gyr between z = 3 and z = 2. We find five star-forming galaxies at z ∼ 3 that are compact (Re < 1.4 kpc) and have stellar mass M∗ > 1010.6M; these galaxies are likely to become members of the massive, quiescent, compact galaxy population at z ∼ 2. We evolve the stellar masses and SFRs of each individual z > 3 galaxy adopting five different star formation histories (SFHs) and studying the resulting population of massive galaxies at z = 2.3. We find that declining or truncated SFHs are necessary to match the observed number density of MQGs at z ∼ 2, whereas a constant delayed-exponential SFH would result in a number density significantly smaller than observed. All of our assumed SFHs imply number densities of CMQGs at z ∼ 2 that are consistent with the observed number density. Better agreement with the observed number density of CMQGs at z ∼ 2 is obtained if merging is included in the analysis and better still if star formation quenching is assumed to shortly follow the merging event, as implied by recent models of the formation of MQGs.

Massive↗

NASA NTRS2016

Investigation of Lithium Metal Hydride Materials for Mitigation of Deep Space Radiation

Radiation exposure to crew, electronics, and non-metallic materials is one of many concerns with long-term, deep space travel. Mitigating this exposure is approached via a multi-faceted methodology focusing on multi-functional materials, vehicle configuration, and operational or mission constraints. In this set of research, we are focusing on new multi-functional materials that may have advantages over traditional shielding materials, such as polyethylene. Metal hydride materials are of particular interest for deep space radiation shielding due to their ability to store hydrogen, a low-Z material known to be an excellent radiation mitigator and a potential fuel source. We have previously investigated 41 different metal hydrides for their radiation mitigation potential. Of these metal hydrides, we found a set of lithium hydrides to be of particular interest due to their excellent shielding of galactic cosmic radiation. Given these results, we will continue our investigation of lithium hydrides by expanding our data set to include dose equivalent and to further understand why these materials outperformed polyethylene in a heavy ion environment. For this study, we used HZETRN 2010, a one-dimensional transport code developed by NASA Langley Research Center, to simulate radiation transport through the lithium hydrides. We focused on the 1977 solar minimum Galactic Cosmic Radiation environment and thicknesses of 1, 5, 10, 20, 30, 50, and 100 g/cm2 to stay consistent with our previous studies. The details of this work and the subsequent results will be discussed in this paper.

Rojdev, Kristina↗

NASA NTRS2018

Direct Multipixel Imaging and Spectroscopy of an Exoplant with a Solar Gravity Lens Mission

We report here on the results of our initial study of a mission to the deep outer regions of our solar system, with the primary mission objective of conducting direct megapixel high-resolution imag- ing and spectroscopy of a potentially habitable exoplanet by exploiting the remarkable optical properties of the SGL. Our main goal was not to study how to get there (although this was also addressed), but rather, to investigate what it takes to operate spacecraft at such enormous distances with the needed precision. Specifically, we studied i) how a space mission to the focal region of the SGL may be used to obtain high-resolution direct imaging and spectroscopy of an exoplanet by detecting, tracking, and studying the Einstein ring around the Sun, and ii) how such information could be used to detect signs of life on another planet.

Astrophysics↗

NASA NTRS2018

Enceladus Vent Explorer Concept

Our concept, Enceladus Vent Explorer (EVE), is a robotic pathfinder mission to enter these doors. EVE's goals are to descend into erupting conduits up to ~2 km deep, characterize the unknown interior structure of the vent-conduit system, assess the accessibility to the subsurface ocean through the vent-conduit system, potentially reach the liquid interface, and perform astrobiology and volcanology observations in the vent-conduit system. EVE sends two types of modules: Surface Module (SM) and Descent Module (DM). SM is a lander that stays on the surface, while tens of small (~3 kg, 10 cm in width and 30 cm in length) DMs separate from SM, move to a vent, and descend into it. DMs rely on a power and communication link provided by SM through a cable. As the payload volume of DM is extremely limited, each DM can carry only a single miniaturized instrument. This limitation is complemented by heterogeneity. There are several types of DMs, all of which share the common mobility system but carry different instruments. For example, a "scout DM" creates a 3-D map of the geyser system with its stereo cameras and structured light. A "sample return DM" collects particles and ice cores in the vent and deliver them to the mass spectrometer in the SM. An "in-situ science DM" carries science instruments, such as a microscopic imager and a microfluidics chip for biosignature detection. DMs are sent either sequentially or in parallel.

Enceladus↗

NASA NTRS2023

Changing Characteristics of Tropical Extreme Precipitation-Cloud Regimes in Warmer Climates

In this paper, we have investigated the changing characteristics of climatic scale (monthly) tropical extreme precipitation in warming climates using the Energy Exascale Earth System Model (E3SM). Results are from Atmospheric Model Intercomparison Project (AMIP)-type simulations driven by a) control experiment with present-day sea surface temperature (SST) and CO 2 concentration, b) P4K, same as in a) but with uniform increase by 4K in SST globally, and c) same as in a), but with imposed SST and CO 2 concentration from outputs of coupled E3SM forced by 4xCO 2 concentration. We find that as the surface warms under P4K and 4xCO 2 , both convective and stratiform rain increase. Importantly, there is increasing fractional contribution of stratiform rain as a function of precipitation intensity, with the most extreme but rare events occurring preferentially over land more than ocean, more so under 4xCO 2 than P4K. Extreme precipitation is facilitated by increased precipitation efficiency, reflecting accelerated rates of recycling of precipitation-cloud water (both liquid and ice phases) in regions with colder anvil cloud tops. Changes in vertical profiles of clouds, condensation heating and vertical motions indicate increasing precipitation-cloud-circulation organization from control, P4K to 4xCO 2 . Results suggest that large-scale ocean warming, i.e., P4K, is the primary cause contributing to organization structure resembling the well-known Mesoscale Convective System (MCS) for increased extreme precipitation on shorter (hourly to daily) time scales. Additional 4xCO 2 atmospheric radiative heating, and dynamically consistent anomalous SST further amplifies the MCS organization under P4K. Analyses of the surface moist static energy distribution show that increase in surface moisture (temperature) under P4K and 4xCO 2 is the key driver leading to enhanced convective instability over tropical ocean (land). However, the fast and large increase in land surface temperature and lack of available local moisture result in strong reduction in land surface relative humidity, reflecting severe drying and enhanced convective inhibition (CIN). It is argued that very extreme and rare “record-breaking” precipitation events found over land under P4K, more so under 4xCO 2 , are likely due to the delayed onset of deep convection, i.e., the longer the suppression of deep convection by CIN, the more severe the extreme precipitation when eventually occur, due to the release of large amount of stored surplus convective available potential energy in the lower troposphere during prolonged CIN.

climate-scale extreme tropical precipitation↗

DOE OSTI2015

Evaluations of CMIP5 simulations over cropland

Cropland is one of major sources of carbon lost to the atmosphere and directly contributes to the emissions of greenhouse gases. There is, however, large potential for cropland to reduce its carbon flux to the atmosphere and sequester soil carbon through conservative agriculture management including no-tillage, perennial and/or deep root crops, irrigation, and organic fertilization etc. But these estimations on carbon emissions and sequestrations over cropland under future climate changes and variability remain largest uncertain among all other terrestrial biomes. Global climate and earth system models are an effective tool to study the cropland responses and feedbacks to present and future climate, yet most models in the latest couple model intercomparsion project phase 5 (CMIP5), generally treat cropland similarly as grassland with tuned parameters and do not account for realistic crop phenology, physiology, and management. In this study, we will evaluate the limitations and deficiencies of the CMIP5 models without process-based crop growth models over cropland by comparing their simulations against FLUXNET observations at eight cropland sites. The results show that: (1) the observed and simulated annual cycles generally are not consistent in either phase or amplitude; (2) the MPI and IPSL model families have better skills in the annual cycles of gross primary product (GPP), net ecosystem production (NEP), and terrestrial ecosystem respiration (TER) than other models at the corn/soybean and cereal sites respectively; (3) none of the CMIP5 models successfully simulate the observed two-peak pattern in the annual cycles of sensible heat fluxes at the corn/soybean sites; (4) the simulated GPPs and NEPs of the CESM1, BCC model families and NorESM1-M are much smaller than the observations for entire year; (5) model members from same model family normally simulate similar annual cycles both in phase and magnitude, but the model members from the CESM1 model family with different atmospheric models have different annual cycles; (6) the biases both in phases and magnitudes of annual cycles for the biogeochemical variables (GPP, NEP and TER) are generally larger than those biogeophysical variables (sensible and latent heat fluxes). Because of the above limitations and deficiencies of CMIP5 simulations over cropland, it is essential to incorporate process-based crop growth models into ESMs to improve the model physics and performance over cropland.

AGRICULTURE↗

DOE OSTI2021

System Retrofits in Efficiency Programs: Track Record and Outlook

Commercial building retrofits are often limited to simple upgrades of individual building components such as equipment or lamp replacements. These equipment- or component-level retrofits have been shown to have less potential for whole building energy savings (50% less in studied cases) compared to comprehensive system-based approaches. System retrofits with their potential for much greater savings are critical to achieving aggressive energy reduction goals in the existing building stock but to date there has been little deep analysis of the track record and trends for systems retrofits in commercial buildings. This paper addresses several questions: 1. To what extent are systems retrofits taking place in the building retrofit marketplace today? 2. Do current systems retrofits in fact save more energy than component retrofits? 3. What kinds of efficiency measures are currently most prevalent in system retrofits? 4. Does systems adoption vary across different retrofit programmatic approaches (e.g. utility incentive programs, federal retrofit programs, ESCOs)? Findings, based on an analysis of retrofit data from 12,000 projects across the U.S. from custom utility incentive programs, federal retrofit programs, and Energy Service Companies (ESCOs), indicate the state of the current market with respect to adoption of systems technologies. A wide range of stakeholders were also interviewed to define the challenges and opportunities for greater deployment. A range of barriers is presented including technical and structural (i.e. programmatic, policy), along with recommendations to accelerate deployment of these strategic approaches.

Regnier, Cynthia↗

DOE OSTI2021

Reconstructing the kinematics of deep inelastic scattering with deep learning

We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance. The method is studied with simulated events at HERA and the future Electron–Ion Collider (EIC). We show that the DNN method outperforms all the traditional methods over the full phase space, improving resolution and reducing bias. Our method has the potential to extend the kinematic reach of future experiments at the EIC, and thus their discovery potential in polarized and nuclear DIS.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2020

Power Beaming for Deep Space and Permanently Shadowed Regions

Power beaming involves the wireless transfer of power, and could provide a revolutionary new way to power spacecraft and vehicles operating in difficult to access regions. Power beaming has the potential to represent an alternative solution to power spacecraft and landers where sunlight is unavailable. It could provide a source of power to robotic systems in permanently shadowed regions or power landers and rovers from orbiting spacecraft (e.g.,Moon, Mars, Europa, Enceladeus, Miranda).

Brophy, John↗

NASA NTRS2021

Power Beaming for Deep Space and Permanently Shadowed Regions

Power beaming involves the wireless transfer of power, and could provide a revolutionary new way to power spacecraft and vehicles operating in difficult to access regions. Power beaming has the potential to represent an alternative solution to power spacecraft and landers where sunlight is unavailable. It could provide a source of power to robotic systems in permanently shadowed regions (PSR) or power landers and rovers from orbiting spacecraft (e.g., Moon, Mars, Europa, Enceladeus, Miranda).

Elliott, John↗

DOE OSTI2025

Water reactive polyurethane grouting for deep vadose zone contaminant immobilization

Contaminants in the deep vadose zone (DVZ) pose a long-term threat to groundwater, human health, and the environment. Polyurethane grouting is a type of chemical grouting that is potentially advantageous in immobilizing contaminants in DVZs. Polyurethane resin has low viscosity that makes it feasible to penetrate and fill the pore space between fine particles. Here, in this study a series of laboratory pressure grouting and leaching tests were conducted to simulate and assess the effectiveness of polyurethane grouting for immobilizing contaminants in DVZs. Soil with fine particles was prepared with 127 I (as iodide) that served as a non-radioactive surrogate for radioactive 129 I. After grouting and curing, leaching tests were used to measure and compare contaminant diffusivity and leachability index values. Additionally, changes in porosity and saturated hydraulic conductivity of grouted soil were measured. X-Ray Computer Tomography (XCT) results showed that the cured polyurethane was distributed nearly homogeneously and approximately half of the voids were filled with cured polyurethane. Grouting reduced the saturated hydraulic conductivity of soil by 37 %. The effective diffusivity decreased by more than 80 % as compared with the ungrouted soil. The leachability index of the grouted soil was 6.5; meeting the criteria established by the U.S. Nuclear Regulatory Commission (NRC) standard. The results obtained in this study provide a valuable assessment of polyurethane grouting for iodide immobilization in the DVZ and indicate this approach may be a viable method for contaminant remediation in DVZ soils.

Contaminant Diffusion↗

NASA NTRS2016

Spatial and Temporal Trends in Precambrian Nitrogen Cycling: A Mesoproterozoic Offshore Nitrate Minimum

Fixed nitrogen is an essential nutrient for eukaryotes. As N2 fixation and assimilation of nitrate are catalyzed by metalloenzymes, it has been hypothesized that in Mesoproterozoic oceans nitrate was limited in offshore environments by low trace metal concentrations and high rates of denitrification in anoxic and episodically euxinic deep water masses, restricting eukaryotes to near-shore environments and limiting their evolutionary innovation. To date this hypothesis has only been tested in the Belt Supergroup (∼1.4 Ga), with results that support an onshore-offshore nitrate gradient as a potential control on eukaryote ecology. Here we present bulk nitrogen and organic carbon isotopic data from non-isochronous cross-basinal facies across the Bangemall (∼1.5 Ga) and the Roper (∼1.4–1.5 Ga) basins to better understand the extent and variability of onshore-offshore nitrogen isotope gradients in the Mesoproterozoic. Both basins show an average ∼1-2‰ enrichment in δ-15N(sub bulk) from deep to shallow facies, with a maximum range from ~1‰ offshore to +7.5‰ onshore. Unlike the Belt basin, the Bangemall and Roper basins show some offshore δ-15N(sub bulk) values that are enriched beyond the isotopic range associated with biological N2 fixation alone. This suggests a mixture of aerobic and anaerobic metabolisms offshore. In shallow waters, where δ-15N(sub bulk) enrichment peaks, an aerobic nitrogen cycle was evidently operating. Even though isotopic signatures of aerobic nitrogen cycling are seen in all parts of the Bangemall and Roper basins, our data are consistent with a lateral gradient in nitrate availability within the photic zone, with higher concentrations in near-shore environments than offshore. The variability in δ-15N(sub bulk) values in each depositional environment and the consistently low δ-15N(sub bulk) values from Mesoproterozoic units compared to the Paleoproterozoic and Neoproterozoic suggest that nitrate concentrations in the global ocean were likely low. This trend is now seen in all three Mesoproterozoic basins so far examined, and contrasts with the Paleoproterozoic and Neoproterozoic where nearly all δ-15N(sub bulk) data plot above the N2 fixation window. Thus, we propose that the Mesoproterozoic ocean was characterized by a nitrate minimum, with the lowest concentrations in offshore environments. This inference is consistent with a Mesoproterozoic O2 decline following a temporary Paleoproterozoic O2 peak, and it further supports the idea that nitrate limitation offshore may have contributed to the restriction of photosynthetic eukaryotes to near-shore environments, delaying their rise to ecological dominance until the Neoproterozoic Era.

Mesoproterozoic↗