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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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DOE OSTI2024

Countering Weapons of Mass Destruction (CWMD) Device Cybersecurity Characterization Process and Profile

Countering Weapons of Mass Destruction (CWMD) recognizes that threats in the cyberspace domain continue to grow, which requires CWMD devices and supporting systems to be both cybersecure (ability to protect or defend from cyber-attacks) and resilient (ability to maintain required capability in the face of adversity) to cyber threats. The CWMD cybersecurity characterization approach in this document supports existing cyber resilience activities within the Acquisition Lifecycle Framework. Similarly, this process supports existing Department of Homeland Security Cyber Resilience Test and Evaluation activities, which consist of iterative processes, starting at the initiation of system acquisition and continuing throughout the entire device and system life cycle. Cyber resilience is the ability of an information system to continue to operate while under attack, even if in a degraded or debilitated state,1 and to rapidly recover operational capabilities for essential functions after a successful attack.2 The goal of the security characterization task for CWMD is to support the development of a CBRN device-dependent profile that aligns with device network capabilities and maps to recommended security controls to create a characterization security profile impact levels. The impact levels for CWMD devices should be characterized as Low (L), Moderate (M), High (H) to align with the low, moderate, high control baselines. To estimate the impact levels, the device’s security-related attributes are translated into the security objectives: Confidentiality (C), Integrity (I), and Availability (A), known as the CIA triad. The potential impact for each device can be L, M, H, for devices that connect and transmit different types of data and may have different impact levels. National Institute of Standards and Technology Federal Information Processing Standards Publication 199 states, “the potential impact values assigned to the respective security objectives shall be the highest value from among those security categories that have been determined for each type of information resident on the information system.”3 As CWMD is determining the cybersecurity impact levels of CBRN devices based on network connections and data transfers, the impact levels are aligned with the associated attributes of network connections and communications. For example, if the device system is connected to a wireless network and transmits different data types based on the confidentiality of the data, the highest impact value for each security objective should represent the device’s CIA impact level. This document is intended to be used by test managers, test team, and program managers.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Proteomic insights into the physiology and metabolism of oleaginous yeasts and filamentous fungi

Fungi are vital to the bioeconomy, serving as key producers of food, beverages, biofuels, and medicines, while also acting as essential resource recyclers in ecosystem management. For nearly a century, oleaginous yeast and filamentous fungi have been explored for their proficiency in oleochemicals production and carbon storage. Lipogenesis is one of the most well-studied fungal processes, with substantial progress having been made through reductionist biochemical approaches; however, the physiology and metabolism of fungal systems operating under different conditions arise from the functions of thousands of proteins, for which very little is known outside of model yeast. In this review, we discuss how proteomics provides a valuable analytical approach to contextualize lipogenesis within a complex biological system, where lipid accumulation is fundamentally governed by changes in proteins of multiple pathways. In the past two decades, proteomics has been applied to study stress response to nutrient limitations, metabolism of various carbon and nitrogen sources, the lipid droplet hub of carbon storage, protein post-translational modifications and signaling pathways, as well as oleochemical biosynthesis, thereby advancing our understanding of the oleaginous phenotype. Over 40 studies are reviewed herein to evaluate the impact, critically assess the utility, and propose future applications of proteomics. In the coming years, large systems-level proteomics studies will lay a foundation for marrying modeling and metabolic engineering strategies to optimize oleochemicals production in oleaginous fungi.

Lipid production↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Pulsed Electric Field Processing of Apples

Pulsed electric field (PEF) processing is a nonthermal technology that has the potential to improve the efficiency of apple processing, particularly for firm cultivars that are growing in popularity. This study evaluated the effects of PEF pretreatment on the processability and product quality of Cosmic Crisp apples, a modern cultivar known for its firmness. Apples were treated with PEF and assessed for tissue softening, mechanical energy requirements, particle size reduction, browning tendencies, and juice yield. PEF treatment significantly softened the apple tissue, as demonstrated by reduced compression forces in Instron testing, and this softening translated to decreased grinding energy during applesauce production. There were no changes to the particle size distribution of the resulting applesauce. PEF treatment also altered browning behavior and produced higher juice yields following mechanical disruption, suggesting enhanced membrane permeability and mass transfer. Together, these results show that PEF pretreatment can reduce mechanical energy demands and increase extractability without reducing product quality in firm apple varieties. These outcomes highlight the potential of PEF to increase the value recovered from processing-grade apples and to support more efficient and sustainable apple processing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2025

Past Approaches for Spent Nuclear Fuel, High-Level, and Transuranic Waste Disposal in the United States—Part 1: Safety Criteria and Treatment of Uncertainty

The United States (US), with its 50-year experience in developing deep geologic disposal for transuranic waste, spent nuclear fuel (SNF), and high-level radioactive waste (HLW), has much to share with other countries. Yet, other countries are better able to translate the US experience and corresponding policy decisions into solutions sensible for their country when they understand the compliance requirements in US laws and regulations. This paper presents past approaches in the generic and site-specific standards of the US Environmental Protection Agency (EPA) and implementing regulations of the US Nuclear Regulatory Commission (NRC) using the framework provided by (1) key questions of the Blue Ribbon Commission on America’s Nuclear Future, and (2) international consensus standards. Both the 1985 EPA generic standards, as updated in 1993 and applied at the operating Waste Isolation Pilot Plant in southern New Mexico for transuranic waste from atomic energy defense activities, and the EPA 2008 site-specific standards and implementing regulations, as applied at the proposed Yucca Mountain repository in southern Nevada for SNF and HLW, adopt the strategy of using quantitative, probabilistic analysis to assess performance and compliance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Effect of Temperature on the Pilot-Scale Catalytic Pyrolysis of Loblolly Pine

A pilot-scale biomass catalytic pyrolysis unit with a nominal throughput of one tonne of biomass per day (1TPD) has been in operation since 2013 to investigate the process parameters that have the largest influence on biocrude yield, oxygen content, and chemical composition. A parametric study was conducted to investigate the effect of pyrolysis temperature, ranging from 433 to 581 °C, on biocrude yield and quality. A locally sourced loblolly pine feedstock and a commercially available, spray dried, nonzeolitic γ-Al 2 O 3 catalyst were used in individual experiments conducted at each pyrolysis temperature to achieve a minimum of 4 h of steady-state continuous operation. Typically, 600–800 kg of biomass was fed over a 12-h period, with one experiment extended to almost 29 h (1144-kg biomass fed) and one experiment interrupted by a process upset after just 7 h (411-kg biomass fed). Comprehensive analysis of collected gas, liquid, and solid products were used to calculate carbon balances (77% to 107%) for each experiment. The biocrude yield ranged from 12 to 18 wt % C and, in general, decreased with increasing pyrolysis temperature. The average steady-state biocrude yield as a function of temperature translated to a biocrude production rate between 40 and 50 gallons per dry ton. The biocrude oxygen content varied between 21 and 31 wt %, on a dry basis, and as expected, decreased with increasing pyrolysis temperature. The identified components in the semivolatile biocrude products are mostly methoxyphenols and other multiphenolic compounds. The multiphenolic compounds are demethoxylated at pyrolysis temperatures above 500 °C, producing biocrudes with higher concentrations of monophenols and polycyclic aromatic hydrocarbons. The concentration of anhydrosugars, like levoglucosan in the biocrudes, decreased with increasing pyrolysis temperature from ∼15 to ∼1 vol %.

biofuels↗
DOE OSTI2021

Celestial amplitudes from UV to IR

Celestial amplitudes represent 4D scattering of particles in boost, rather than the usual energy-momentum, eigenstates and hence are sensitive to both UV and IR physics. We show that known UV and IR properties of quantum gravity translate into powerful constraints on the analytic structure of celestial amplitudes. For example the soft UV behavior of quantum gravity is shown to imply that the exact four-particle scattering amplitude is meromorphic in the complex boost weight plane with poles confined to even integers on the negative real axis. Would-be poles on the positive real axis from UV asymptotics are shown to be erased by a flat space analog of the AdS resolution of the bulk point singularity. The residues of the poles on the negative axis are identified with operator coefficients in the IR effective action. Far along the real positive axis, the scattering is argued to grow exponentially according to the black hole area law. Exclusive amplitudes are shown to simply factorize into conformally hard and conformally soft factors. The soft factor contains all IR divergences and is given by a celestial current algebra correlator of Goldstone bosons from spontaneously broken asymptotic symmetries. The hard factor describes the scattering of hard particles together with the boost-eigenstate clouds of soft photons or gravitons required by asymptotic symmetries. These provide an IR safe S-matrix for the scattering of hard particles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Exploring the Effects of Varying Pre-Chamber Geometry in a Heavy-Duty Natural Gas Optical Engine under Dilution Conditions

Pre-chamber combustion is an advanced ignition strategy that has been shown to enhance spark ignition (SI) combustion stability in natural gas (NG) engines by providing distributed ignition sites from turbulent jets and enhancing main-chamber turbulence. Pre-chamber combustion has been proven especially advantageous compared to SI in ultra-lean and dilute operating conditions. This work involves experimental investigation of the effects of varying passive pre-chamber nozzle configuration on pre-chamber and main chamber combustion under simulated exhaust gas recirculation (EGR) dilution (0 and 20%) conditions in a heavy-duty, single-cylinder, optically accessible NG engine at stoichiometric fuel-air ratio. Pre-chamber nozzle configurations include four pre-chambers with constant nozzle area to pre-chamber volume ratio (A/V) with different nozzle sizes and orientations and one configuration with larger nozzles. The optical engine is operated in a skip-fire sequence consisting of 18 motored cycles followed by two consecutive fired cycles to elucidate the effect of combustion residuals (internal EGR) on combustion evolution. Pressure-based diagnostics are used to monitor pre-chamber and subsequent main chamber combustion, and optical diagnostics include high-speed OH* chemiluminescence to visualize the development of pre-chamber jets and the resulting ignition of the main chamber charge. Heat release analysis of the in-cylinder pressure data indicates that a faster pre-chamber pressure rise does not always translate into faster main-chamber combustion. The pre-chamber with the smallest nozzle diameter produced the highest pre-chamber pressure rise and fastest combustion under non-diluted conditions. However, dilution delays the main chamber ignition for smaller nozzles despite a comparable rise in pre-chamber pressure compared to configurations with larger nozzles. This effect is more pronounced for cycles with in-cylinder combustion residuals in addition to external dilution. Additionally, it was observed that pre-chambers with swirling nozzles have a faster pressure rise in the pre-chamber and main chamber under dilute conditions. Optical diagnostics suggest that the main reason for the delay between the pre-chamber pressure rise and main-chamber combustion lies in jet quenching and delayed re-ignition, which can even lead to misfire if jets emitted from small nozzles combined with dilution fail to re-ignite.

Dhotre, Akash [University Of Minnesota-Twin Cities↗
DOE OSTI2025

Model-independent measurement of the matter-radiation equality scale in DESI 2024

The peak of the matter power spectrum, known as the turnover (TO) scale, is determined by the horizon size at the time of matter-radiation equality. This scale can serve as a standard ruler, independent of other features in the matter power spectrum, such as baryon acoustic oscillations (BAO). Here, in this work, we present the first detection of the turnover in the galaxy autopower spectrum, utilizing the distribution of quasars and luminous red galaxies (LRG) measured by the Dark Energy Spectroscopic Instrument (DESI) during its first year of survey operations in a model-independent manner. To avoid confirmation bias, we first analyze the data using data blinding methods designed for the DESI baryon acoustic oscillation, redshift space distortion and scale-dependent bias signals. We measure the angle-averaged dilation distance 𝐷 V ⁡(𝑧 = 1.651) = (38.1 ± 2.5)⁢𝑟 H from the quasars and 𝐷 V ⁡(𝑧 = 0.733) = (21.8 ± 1.0)⁢𝑟 H from the LRG sample in units of the horizon 𝑟 H at the matter-radiation-equality epoch. Combining these two constraints and assuming a flat Λ⁢CDM model with three standard neutrino species, we can translate this into a constraint of Ω m ⁢ℎ 2 = 0.13⁢9$^{+0.036}_{−0.046}$. We can break the Ω m −𝐻 0 degeneracy with low-redshift distance measurements from type-Ia supernova (SN) data from Pantheon+, we obtain a sound-horizon free estimate of the Hubble-Lemaître parameter of 𝐻 0 = 65.2$^{+4.9}_{−6.2}$ km/s/Mpc, consistent with sound-horizon dependent DESI measurements. On the other hand, combining the DESI BAO and TO, we find a truly DESI-only measurement of 𝐻 0 = 74.0$^{+7.2}_{−3.5}$ km/s/Mpc, in line with DESI-only full-shape results where the sound-horizon scale is marginalized out. This discrepancy in 𝐻 0 can be reconciled in a 𝑤 0 ⁢𝑤 𝑎 ⁢ CDM cosmology, where the combination of DESI BAO and TO data yields 𝐻 0 = 66.5 ± 7.2 km/s/Mpc.

cosmological parameters↗
DOE OSTI2024

Characterization and classification of interacting ( 2 + 1 )-dimensional topological crystalline insulators with orientation-preserving wallpaper groups

While free fermion topological crystalline insulators have been largely classified, the analogous problem in the strongly interacting case has been only partially solved. In this work, we develop a characterization and classification of interacting, invertible fermionic topological phases in (2+1) dimensions with charge conservation, discrete magnetic translation and M-fold point group rotation symmetries, which form the group G f = U(1) f × Φ [Z 2 $\rtimes$Z M ] for M = 1,2,3,4, and 6. Φ is the magnetic flux per unit cell. We derive a topological response theory in terms of background crystalline gauge fields, which gives a complete classification of different phases and a physical characterization in terms of quantized response to symmetry defects. We then derive the same classification in terms of a set of real space invariants {$Θ^±_o$} that can be obtained from ground state expectation values of suitable partial rotation operators. We explicitly relate these real space invariants to the quantized coefficients in the topological response theory, and find the dependence of the invariants on the chiral central charge c – of the invertible phase. Finally, when Φ = 0 we derive an explicit map between the free and interacting classifications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Theoretical Investigation of the Adsorbate and Potential–Induced Stability of Cu Facets During Electrochemical CO 2 and CO Reduction

The activity and product selectivity of electrocatalysts for reactions like the carbon dioxide reduction reaction (CO 2 RR) are intimately dependent on the catalyst's structure and composition. While engineering catalytic surfaces can improve performance, discovering the key sets of rational design principles remains challenging due to limitations in modeling catalyst stability under operating conditions. Herein, we perform first-principles density functional calculations adopting implicit solvation methods with potential control to study the influence of adsorbates and applied potential on the stability of different facets of model Cu electrocatalysts. Using coverage dependencies extracted from microkinetic models, we describe an approach for calculating potential and adsorbate-dependent contributions to surface energies under reaction conditions, where Wulff constructions are used to understand the morphological evolution of Cu electrocatalysts under CO 2 RR conditions. Here we identify that CO*, a key reaction intermediate, exhibits higher kinetically and thermodynamically accessible coverages on (100) relative to (111) facets, which can translate into an increased relative stabilization of the (100) facet during CO 2 RR. Our results support the known tendency for increased (111) faceting of Cu nanoparticles under more reducing conditions and that the relative increase in (100) faceting observed under CO 2 RR conditions is likely attributed to differences in CO* coverage between these facets.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Linear differential

Apparatuses and methods of operating a linear differential (100, 600) are described herein. The linear differential (100, 600) contains a slide portion (102) with parallel right-hand and left-hand threaded rods (112, 114). Threaded onto the right-hand and left-hand threaded rods (112, 114) and attached to the slide portion (102) are right-hand and left-hand gears (116, 118). Meshed between the right-hand and left-hand gears (116, 118) and also attached to the slide portion (102) is a driven gear (200). An end effector (104) is attached to the driven gear (200) and is configured to translate along a translation axis (110) and rotate around a rotation axis (120).

Crawford, Anthony↗
DOE OSTI2026

Numerical simulation of vortex-induced vibration response of a single IEA 10-MW wind turbine blade

Three-dimensional simulation of vortex-induced vibration (VIV) of a single International Energy Agency (IEA) 10-MW reference wind turbine blade with a length of 97.325 m is performed using the ExaWind stack, an open-source suite of codes. This study aims to illustrate the spanwise VIV response characteristics and cross-validate the results with an existing commercial framework. Five near-body meshes and three time steps are selected for the convergence study. To improve computational efficiency, several VIV triggering methods are also compared to shorten the VIV development period. The ExaWind-based VIV simulation strategy for a single IEA 10-MW blade is determined. First, the modal shape is validated against published results. Then, spanwise VIV responses of four blade configurations under a fixed and varied incoming flow velocity are analyzed. Results show that the VIV response is dominated by the first edgewise (second overall) mode. Little first-mode contributions appear near the second-mode node, producing a pi phase jump, and a higher harmonics response occurs near the blade root. Rotational degrees of freedom are minor compared with translational motion. The response versus reduced velocity is analyzed, showing a two-branch behavior similar to that of VIV for a bluff cylinder. Across all tested cases, the dominant frequency remains locked to the natural frequency of the second mode with no observed desynchronization. A mild deviation is observed for the case of 90-degree pitch and 310-degree azimuth rotation near a reduced velocity of 6, which will be examined with additional cases in future work. These findings indicate that severe VIV responses can arise under specific configurations and flow conditions, thereby increasing the potential for VIV fatigue damage and requiring greater attention during operation.

17 WIND ENERGY↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
DOE OSTI2025

​​Hydrogen Transportation and Distributed Energy Systems Seismic Risk Assessment for Cascadia Subduction Zone Airport Facilities​

Portland International Airport in Oregon is exploring operating a fleet of 28 fuel cell electric buses to support airport operations and possibly provide backup power during outages. In this report, we evaluate the risk of hydrogen deployment at an airport considering the potential for seismic activity. We present a seismic risk assessment for pressurized piping for a generic hydrogen refueling station at the airport. Only pressurized piping was considered in the risk analysis because of its vulnerability and availability of fragility information unlike the other components. Seismic capacity of other components should be included as more information becomes available. To characterize the seismic hazard, we considered the initiating event frequency of a Cascadia Subduction Zone earthquake event based on data from the literature. Seismic stressors that translate to the site from an earthquake event are expressed as peak ground acceleration. Site disturbance is characterized as a function of soil conditions, represented by different shear wave velocity values, which affect how seismic waves propagate and impact structures. Pipe fragility curves as a function of seismic capacity were used to correlate ground acceleration and failure probability. These fragility characteristics were combined with site-specific soil conditions to calculate the probability of pipe rupture during a seismic event. It should be noted that multiple simultaneous failures due to an earthquake event as a common cause are not considered. Some epistemic uncertainties such as self-exciting shocks, aftershocks, and the time variant nature of the ground motion are not considered either.

08 HYDROGEN↗
DOE OSTI2023

Stratospheric temperature measurements from nanosatellite stellar occultation observations of refractive bending

Abstract. Stellar occultation observations from space can probe the stratosphere and mesosphere at a fine vertical scale around the globe. Unlike other measurement techniques like radiosondes and aircraft, stellar occultation has the potential to observe the atmosphere above 30 km, and unlike radio occultation, stellar occultation probes fine-scale phenomena with potential to observe atmospheric turbulence. We imaged the refractive bending angle of a star centroid for a series of occultations by the atmosphere. Atmospheric refractivity, density, and then temperature are retrieved from the bending observations with the Abel transformation and Edlén's law, the hydrostatic equation, and the ideal gas law. The retrieval technique is applied to data collected by two nanosatellites operated by Terran Orbital. Measurements were primarily taken by the GEOStare SV2 mission, with a dedicated imaging telescope, supplemented with images captured by spacecraft bus sensors, namely the star trackers on other Terran Orbital missions. The bending angle noise floor is 10 and 30 arcsec for the star tracker and GEOStare SV2 data, respectively. The most significant sources of uncertainty are due to centroiding errors due to the fairly low-resolution stellar images and telescope pointing knowledge derived from noisy satellite attitude sensors. The former mainly affects the star tracker data, while the latter limits the GEOStare SV2 accuracy, with both providing low vertical resolution. This translates to a temperature profile retrieval up to roughly 20 km for both star tracker and GEOStare SV2 datasets. In preparation of an upcoming 2023 mission designed to correct these deficiencies, SOHIP, we simulated bending angle measurements with varying magnitudes of error. The expected maximum altitude of retrieved temperature is 41 km on average for these simulated measurements with a noise floor of 0.39 arcsec. Our work highlights the capabilities of stellar occultation observations from nanosatellites for atmospheric sounding. Future work will investigate high-frequency observations of atmospheric gravity waves and turbulence, mitigating the major uncertainties observed in these datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrate Latimer Controls' Solution into RTAC (CRADA Final Report, CRD-23-24672)

Latimer Controls, Inc. was awarded two vouchers under the Department of Energy's American-Made Solar Prize Round 6 to conduct collaborative research at a national laboratory. The National Renewable Energy Laboratory (NREL) was selected as a partner to assist Latimer Controls in the performance evaluation of its photovoltaic (PV) control software. This collaboration focuses on developing a hardware-in-the-loop (HIL) testbed at NREL, which will be used to test and validate the Latimer PV control technology in a realistic yet de-risked environment. Both Latimer and NREL teams will work together to analyze the collected test data, derive insights, and disseminate the scientific findings. Recent studies underscore the potential of solar energy as a zero-marginal-cost and zero-emission flexibility resource within the bulk power system, particularly when integrated with advanced control systems. To enhance the performance of such systems, Latimer Controls has developed leading-edge technologies, including machine learning (ML) algorithms and hierarchical inverter set-point allocation methods. These innovations are designed to estimate the operational headroom of large PV plants for grid integration and control. However, comprehensive validation under real-world conditions remains necessary. To address this gap, the concurrent CRADA project proposes the real-world application and validation of the Latimer Control solution within a HIL environment. Initially, the Latimer algorithm was developed and tested within MATLAB Simulink, a platform suitable for research-level simulations and iterative development. However, transitioning this technology to a real solar site as an industry-ready solution necessitates implementation in a format compatible with widely used solar power plant controllers. In this additional CRADA work, the MATLAB Simulink-based logic will be translated into Structured Text, a programming language compliant with IEC 61131 standards, which is commonly used for custom logic implementations in industry-leading programmable logic controllers (PLCs), such as the Schweitzer SEL real-time automation controller (RTAC). This transition will facilitate the deployment of the Latimer Control solution in real-world solar power plants, thereby advancing the technology towards commercialization.

14 SOLAR ENERGY↗