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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Mapping the performance of a versatile water-based condensation particle counter (vWCPC) with numerical simulation and experimental study

Accurate airborne aerosol instrumentation is required to determine the spatial distribution of ambient aerosol particles, particularly when dealing with the complex vertical profiles and horizontal variations of atmospheric aerosols. A versatile water-based condensation particle counter (vWCPC) has been developed to provide aerosol concentration measurements under various environments with the advantage of reducing the health and safety concerns associated with using butanol or other chemicals as the working fluid. However, the airborne deployment of vWCPCs is relatively limited due to the lack of characterization of vWCPC performance at reduced pressures. Given the complex combinations of operating parameters in vWCPCs, modeling studies have advantages in mapping vWCPC performance. In this work, we thoroughly investigated the performance of a laminar-flow vWCPC using COMSOL Multiphysics® simulation coupled with MATLAB™. We compared it against a modified vWCPC (vWCPC model 3789, TSI, Shoreview, MN, USA). Our simulation determined the performance of particle activation and droplet growth in the vWCPC growth tube, including the supersaturation, $D$ p,kel,0 (smallest size of particle that can be activated), $D$ p,kel,50 (particle size activated with 50 % efficiency) profile, and final growth particle size D d under wide operating temperatures, inlet pressures $P$ (30–101 kPa), and growth tube geometry (diameter $D$ and initiator length $L$ ini ). The effect of inlet pressure and conditioner temperature on vWCPC 3789 performance was also examined and compared with laboratory experiments. The COMSOL simulation result showed that increasing the temperature difference (Δ$T$) between conditioner temperature $T$ con and initiator $T$ ini will reduce $D$ p,kel,0 and the cut-off size $D$ p,kel,50 of the vWCPC. In addition, lowering the temperature midpoint ($T$ mid = $\frac{T_{con}+T_{ini}}{2}$ increases the supersaturation and slightly decreases the $D$ p,kel . The droplet size at the end of the growth tube is not significantly dependent on raising or lowering the temperature midpoint but significantly decreases at reduced inlet pressure, which indirectly alters the vWCPC empirical cut-off size. Our study shows that the current simulated growth tube geometry ($D$=6.3 mm and $L$ ini =30 mm) is an optimized choice for current vWCPC flow and temperature settings. The current simulation can more realistically represent the $D$ p,kel for 7 nm vWCPC and also achieved good agreement with the 2 nm setting. Using the new simulation approach, we provide an optimized operation setting for the 7 nm setting. This study will guide further vWCPC performance optimization for applications requiring precise particle detection and atmospheric aerosol monitoring.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Combustion energy frontier research center (CEFRC) final report (August 1, 2009 – July 31, 2016)

The Combustion Energy Frontier Research Center (CEFRC) was established to tackle the single overarching grand challenge of energy sustainability, energy security and global warming: to develop a “validated, predictive, multi-scale, combustion modeling capability to optimize the design and operation of evolving fuels in advanced engines for transportation applications,” as identified in the DOE report on “Basic Energy Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels”. The challenge is particularly daunting since energy conversion efficiencies and exhaust emissions are governed by coupled chemical and transport processes at multiple length scales ranging from electron excitation to molecular rearrangements to nanoscale particulate formation to turbulent fuel/air mixing. To tackle this challenge, the CEFRC assembled a world-class team of 15 principal investigators, with the objectives to: 1) develop and test theoretical models to predict elementary reaction rates, molecule thermalization rates, chemical bond dissociation energies, and nonequilibrium transport properties using quantum chemistry calculations that account for strong electron correlation and multiple electronic potential energy surfaces; 2) develop automated kinetic mechanism generation, reduction, and error control methods for predicting alternative fuel including biofuel oxidation, fuel droplet decomposition, and NOx and particulate formation; 3) validate and improve the predictions of these models by measuring ignition delay times, species profiles, flame structures, burning limits, turbulence-kinetic coupling, and NOx and soot emissions at high-pressures and near-limit conditions, by using advanced experimental diagnostic techniques including multiple laser techniques, molecular beam sampling and synchrotron photoionization, and by conducting the measurements in high-pressure shock tubes, jet-stirred and flow reactors, flame bombs, counterflow flames, and advanced-design rapid compression ignition instruments; and 4) develop a suite of validated petascale high-fidelity simulation and modeling capabilities to understand and predict chemistry-turbulence-radiation coupling for new fuels in new regimes, including the high pressure, low-temperature combustion in advanced engine and turbine designs, and 5) establish a knowledge highway between researchers and engineers in academia, national laboratories, and industry to facilitate the dissemination and exchange of knowledge on national and international levels, and enrich the talent pool and capabilities of the next generation of combustion scientists and engineers. The technical activities of the CEFRC were conducted through three Disciplinary Working Groups – Chemistry Theory, Experiment and Mechanism, and Reacting Flows, which coordinated the Center’s research on the development of combustion chemistry of Foundation Fuels (C0–C4 hydrocarbons), Alcohols, and Biodiesel through three corresponding Mechanism Thrust Groups. Such a two-dimensional coordinated and tightly interwoven research structure has been proven to be highly effective in assuring the interplay between the developments of the fundamentals of combustion science and the utilization of the various categories of fuels. The Center has accomplished the above goals over the five year period (August 1, 2009 – July 31, 2014) with appropriated funding, followed by two additional no-cost-extension (NCE) years. The research results are documented in 230 journal publications, with six legacy review papers on the study of combustion chemistry using shock tubes, flow reactors, rapid compression machines, and flames, on uncertainty quantification, and on theoretical reaction dynamics and chemical modeling of combustion. A robust outreach program complemented these PI-led research activities, consisting of: 1) a roving post-doc program comprised of a corps of Center-appointed, co- or multi-sponsored post-doctoral fellows with rotating assignments to conduct seed projects initiated by at least two PIs, in residence with these sponsoring PIs, to rapidly pursue new and high-risk, high-payoff interdisciplinary ideas; 2) an annual summer school on combustion heavily attended (~200) by senior graduate students and practicing researchers covering advanced topics on chemical kinetics, fluid mechanics, turbulent combustion, engine combustion, new technologies, etc.; 3) a robust open web-site providing Center and community information as well as the lecture videos and notes of the summer school; and 4) widely distributed biannual newsletters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Correlating Nb-SRF Surface Processing with Evolution of Surface Electronic States

The few nanometers of the surface exposed to RF field plays a major role in defining the RF performance of superconducting cavities. Over the past two decades, several pioneering surface treatment and processing methods have emerged, enabling remarkable improvements in cavity performance by simultaneously achieving high Q with increasing Eacc. These processing methods include: thermal treatment under ultra-high vacuum (UHV) conditions across lo¬¬¬¬¬¬¬w-, mid-, and high-temperature ranges and high temperature treatments under controlled N2 atmosphere. These processes also produce distinct surface oxide configurations with different valence states, thicknesses, and uniformity, as well as different oxygen concentration profiles in bulk Nb. In this work, we are trying to understand how do surface-processing methods and the resulting oxide/oxygen profiles affect the electronic structure of surface and the mechanism of superconductivity? With the help of Fermilab’s in-house X-ray photoemission facility and, in collaboration with the synchrotron-based angle-resolved photoemission (ARPES) facility at Argonne National Laboratory, we are investigating how the valence band structure and density of states (DoS) near the Fermi level modify with different surface treatments. Our observations show that different surface-processing methods lead to distinct evolutions of the valence-band states near the Fermi level during the superconducting transition. This behavior suggests variations in Nb-O orbital hybridizations and points towards the possibility of different underlying mechanisms of superconductivity governed by the surface chemistry and oxide configuration. We also correlate these distinct superconducting mechanisms with RF cavity performance, specifically focusing on measured surface resistance, the nature of the Q-slope, and quench fields observed in SRF measurements. These results will enable us to identify the potential limiting factors and relevant controllable parameters that can be further optimized to improve the performance of SRF cavities.

Tripathi, Malvika [Fermilab]↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2017

Phosphoketolase overexpression increases biomass and lipid yield from methane in an obligate methanotrophic biocatalyst

Microbial conversion of methane to high-value bio-based chemicals and materials offers a path to mitigate GHG emissions and valorize this abundant-yet -underutilized carbon source. In addition to fermentation optimization strategies, rational methanotrophic bacterial strain engineering offers a means to reach industrially relevant titers, carbon yields, and productivities of target products. The phosphoketolase pathway functions in heterofermentative bacteria where carbon flux through two sugar catabolic pathways to mixed acids (lactic acid and acetic acid) increases cellular ATP production. Importantly, this pathway also serves as an alternative route to produce acetyl-CoA that bypasses the CO 2 lost through pyruvate decarboxylation in the Embden-Meyerhof-Parnas pathway. Thus, the phosphoketolase pathway can be leveraged for carbon efficient biocatalysis to acetyl-CoA-derived intermediates and products. Here, we show that the industrially promising methane biocatalyst, Methylomicrobium buryatense , encodes two phosphoketolase isoforms that are expressed in methanol- and methane-grown cells. Overexpression of the PktB isoform led to a 2-fold increase in intracellular acetyl-CoA concentration, and a 2.6-fold yield enhancement from methane to microbial biomass and lipids compared to wild-type, increasing the potential for methanotroph lipid-based fuel production. Off-gas analysis and metabolite profiling indicated that global metabolic rearrangements, including significant increases in post-translational protein acetylation and gene expression of the tetrahydromethanopterin-linked pathway, along with decreases in several excreted products, coincided with the superior biomass and lipid yield observed in the engineered strain. Further, these data suggest that phosphoketolase may play a key regulatory role in methanotrophic bacterial metabolism. As a result, given that acetyl-CoA is a key intermediate in several biosynthetic pathways, phosphoketolase overexpression offers a viable strategy to enhance the economics of an array of biological methane conversion processes.

09 BIOMASS FUELS↗
DOE OSTI2016

SU-F-T-164: Investigation of PRESAGE Formulation On Signal Quenching in a Proton Beam

Purpose: The radiochromic polyurethane PRESAGE by Heuris Pharma has had limited applications with protons because of a dose response dependence on LET resulting in signal quenching in the Bragg peak. This is due to the radical initiator, a halocarbon, radically recombining in high-LET irradiations. This study investigated the use of alternative halocarbons at various chemical concentrations to determine their significance in signal quenching. Methods: PRESAGE was manufactured in-house and cast in small volume cuvettes (1×1×4cm^3). Several compositions were evaluated to determine the influence of the radical initiator component. Mixtures contained one of two halocarbons, chloroform or bromoform, at concentrations of 5%/10%/15%(w/w). A large volume, cylindrical PRESAGE dosimeter made following the mixture described by Heuris Pharma, 4cm(D)×8.5cm(H), was irradiated with 200-MeV protons to study regions of low- and high-LET along a 10cm spread out Bragg peak isodose profile. Depths corresponding to regions of low quenching (<3%) and high quenching (>20%) were determined. These depths were used for cuvette placement in a solid water phantom. Samples of each formulation were placed at each depth and irradiated to doses between 0 and 10Gy. Results: The cuvettes indicated different levels of quenching for different radical initiator types, concentrations, and total doses. Chloroform formulations showed reduced quenching from 29%(5%-w/w) to 21%(15%-w/w) while bromoform reduced quenching from 27%(5%-w/w) to 17%(15%-w/w). The reduction in quenching was found to be non-linear with concentration of radical initiator. A quenching dose-dependency was also found that changed with formulation. In all cases, quenching was relatively consistent from 0–5Gy but increased at 10Gy. The quenching decreased as concentrations of radical initiator increased. Conclusion: The radical initiator component in PRESAGE is correlated with the signal quenching observed in proton irradiations and formulation adjustments show promise as a method of reducing this quenching. Future work will further investigate concentration limits and optimize the formulation. Grant number 5RO1CA100835.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

III-V/Active-Silicon Integration for Low-Cost High-Performance Concentrator Photovoltaics

This FPACE project was motivated by the need to establish the foundational pathway to achieve concentrator solar cell efficiencies greater than 50%. At such an efficiency, DOE modeling projected that a III-V CPV module cost of $0.50/W or better could be achieved. Therefore, the goal of this project was to investigate, develop and advance a III-V/Si mulitjunction (MJ) CPV technology that can simultaneously address the primary cost barrier for III-V MJ solar cells while enabling nearly ideal MJ bandgap profiles that can yield efficiencies in excess of 50% under concentrated sunlight. The proposed methodology was based on use of our recently developed GaAsP metamorphic graded buffer as a pathway to integrate unique GaAsP and Ga-rich GaInP middle and top junctions having bandgaps that are adjustable between 1.45 – 1.65 eV and 1.9 – 2.1 eV, respectively, with an underlying, 1.1 eV active Si subcell/substrate. With this design, the Si can be an active component sub-cell due to the semi-transparent nature of the GaAsP buffer with respect to Si as well as a low-cost alternative substrate that is amenable to scaling with existing Si foundry infrastructure, providing a reduction in materials cost and a low cost path to manufacturing at scale. By backside bonding of a SiGe, a path to exceed 50% efficiency is possible. Throughout the course of this effort, an expansive range of new understanding was achieved that has stimulated worldwide efforts in III-V/Si PV R&D that spanned materials development, metamorphic device optimization, and complete III-V/Si monolithic integration. Highlights include the demonstration of the first ideal GaP/Si interfaces grown by industry-standard MOCVD processes, the first high performance metamorphic tunnel junctions designed for III-V/Si integration, record performance of specific metamorphic sub-cell designs, the first fully integrated GaInP/GaAsP/Si double (1.7 eV/1.1 eV) and triple (1.95 eV/1.5 eV/1.1 eV) junction solar cells, the first high performance GaAsP/Si double junction cell, the demonstration of a new method that allow for rapid, quantitative and non-destructive characterization of dislocations (ECCI-electron channeling contrast imaging), the first observation, explanation and solution of the now commonly reported lifetime degradation and recovery phenomena in III-V/Si MOCVD growth, the first demonstration of a high performance SiGe cell with a bandgap of 0.9 eV, amongst other highlights. The impact of the program on the international community has been significant. At the start of our FPACE1 project and for the immediate prior years, 1-2 conference papers/annually were presented at IEEE PVSC. Once FPACE1 commenced in 2011, related efforts sprouted across the US, Europe and Asia and by 2015 there were 26 papers presented on III-V/Si multijunctions in the 2015 PVSC, demonstrating the excitement that was stimulated by the results of this FPACE1 effort.

14 SOLAR ENERGY↗
DOE OSTI2015

Sensitivity and variability of Presage dosimeter formulations in sheet form with application to SBRT and SRS QA

Purpose: To measure sensitivity and stability of the Presage dosimeter in sheet form for various chemical concentrations over a range of clinical photon energies and examine its use for stereotactic body radiation therapy (SBRT) and stereotactic radiosurgery (SRS) QA. Methods: Presage polymer dosimeters were formulated to investigate and optimize their sensitivity and stability. The dosimeter is composed of clear polyurethane base, leucomalachite green (LMG) reporting dye, and bromoform radical initiator in 0.9–1.0 mm thick sheets. The chemicals are mixed together for 2 min, cast in an aluminum mold, and left to cure at 60 psi for a minimum of two days. Dosimeter response was characterized at energies Co-60, 6 MV, 10 MV flattening-filter free, 15 MV, 50 kVp (mean 19.2 keV), and Ir-192. The dosimeters were scanned by a Microtek Scanmaker i800 at 300 dpi, 2{sup 16} bit depth per color channel. Red component images were analyzed with ImageJ and RIT. SBRT QA was done with gamma analysis tolerances of 2% and 2 mm DTA. Results: The sensitivity of the Presage dosimeter increased with increasing concentration of bromoform. Addition of tin catalyst decreased curing time and had negligible effect on sensitivity. LMG concentration should be at least as high as the bromoform, with ideal concentration being 2% wt. Gamma Knife SRS QA measurements of relative output and profile widths were within 2% of manufacturer’s values validated at commissioning, except the 4 mm collimator relative output which was within 3%. The gamma pass rate of Presage with SBRT was 73.7%, compared to 93.1% for EBT2 Gafchromic film. Conclusions: The Presage dosimeter in sheet form was capable of detecting radiation over all tested photon energies and chemical concentrations. The best sensitivity and photostability of the dosimeter were achieved with 2.5% wt. LMG and 8.2% wt. bromoform. Scanner used should not emit any UV radiation as it will expose the dosimeter, as with the Epson 10000 XL scanner. Presage dosimeter in this form was sensitive enough for use in SRS and SBRT QA. The lower gamma pass rate for Presage compared to Gafchromic film can be attributed to the simple equipment used in the fabrication process, which limited the dosimeter’s sensitivity uniformity by agglomeration of air bubbles in the material, nonuniform concentration of chemicals throughout the material, and thickness variations. This demands improvements in mixing tools and molds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Shifting agricultural practices to produce sustainable, low carbon intensity feedstocks for biofuel production

The carbon intensity (CI) of biofuel's well-to-pump life cycle is calculated by life cycle analysis (LCA) to account for the energy/material inputs of the feedstock production and fuel conversion stages and the associated greenhouse gas (GHG) emissions during these stages. The LCA is used by the California Air Resources Board's Low Carbon Fuel Standard (LCFS) program to calculate CI and monetary credits are issued based on the difference between a given fuel's CI and a reference fuel's CI. Through the Tier 2 certification program under which individual fuel production facilities can submit their own CIs with their facility input data, the LCFS has driven innovative technologies to biofuel conversion facilities, resulting in substantial reductions in GHG emissions as compared to the baseline gasoline or diesel. A similar approach can be taken to allow feedstock petition in the LCFS so that lower-CI feedstock can be rewarded. Here we examined the potential for various agronomic practices to improve the GHG profiles of corn ethanol by performing feedstock-level CI analysis for the Midwestern United States. Our system boundary covers GHG emissions from the cradle-to-farm-gate activities (i.e. farm input manufacturing and feedstock production), along with the potential impacts of soil organic carbon change during feedstock production. We conducted scenario-based CI analysis of ethanol, coupled with regionalized inventory data, for various farming practices to manage corn fields, and identified key parameters affecting cradle-to-farm-gate GHG emissions. The results demonstrate large spatial variations in CI of ethanol due to farm input use and land management practices. In particular, adopting conservation tillage, reducing nitrogen fertilizer use, and implementing cover crops has the potential to reduce GHG emissions per unit corn produced when compared to a baseline scenario of corn–soybean rotation. This work shows a large potential emission offset opportunity by allowing feedstock producers a path to Tier 2 petitions that reward low-CI feedstocks and further reduce biofuels' CI. The prevalence of significant acreage that has not been optimized for CI suggests that policy changes that incentivize optimization of this parameter could provide significant additionality over current trends in farm efficiency and adoption of conservation practice.

09 BIOMASS FUELS↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2016

TH-CD-207A-02: Implementation of Live EPID-Based Inspiration Level Assessment (LEILA) for Deepinspiration Breath-Hold (DIBH) Monitoring Using MV Fluoroscopy

Purpose: As prior work has shown that current DIBH monitoring approaches using surrogate measures (marker block on chest) do not always correspond with the clinical quantity of interest (lung depth, LD), a software tool and workflow are introduced to use MV fluoroscopy during treatment for real-time / Live EPID-based Inspiration Level Assessment (LEILA). Methods: A prototype software tool calculates and displays the LD during the treatment of left sided breast cancer. Calculations are based on MV cine images which are acquired with the treatment beam thereby not incurring any additional imaging dose. Image capture and processing are implemented using a dedicated frame grabber computer. The calculation engine automatically detects image orientation and includes provisions for large treatment fields that exceed the size of the EPID panel. LD is measured along a line profile in the middle of the field. LEILA’s interface displays the current MV image, a reference image (DRR), the current LD, as well as a trace of LD over treatment time. The display includes patient specific LD tolerances. Tolerances are specified for each field and loaded before the treatment. A visual warning is generated when the tolerance is exceeded. LEILA is initially run in parallel with current DIBH techniques. When later run by itself DIBH setup will be done using skin marks and room laser. Results: Offline tests of LEILA confirmed accurate automatic LD measurement for a variety of patient geometries. Deployment of the EPID during all left sided breast treatments was well tolerated by patients and staff during a multi-month pilot. The frame grabber provides 11 frames-per-second; the MATLAB based LEILA prototype software can analyze five frames-per-second standalone on standard desktop hardware. Conclusion: LEILA provides an automated approach to quantitatively monitor LD on MV images during DIBH treatment. Future improvements include a database and further speed optimization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Predicting core transport in ITER baseline discharges with neon injections

Achieving self-consistent performance predictions for ITER requires integrated modeling of core transport and divertor power exhaust under realistic impurity conditions. We present results from a systematic power-flow and impurity-content study for the ITER 15 MA baseline scenario constrained directly by existing SOLPS-ITER neon-seeded divertor solutions. Using the OMFIT STEP workflow, stationary temperature and density profiles are predicted with TGYRO for $1.5 \unicode{x2A7D} Z_\textrm{eff} \unicode{x2A7D} 2.5$, and the corresponding power crossing the separatrix $P_\textrm{sep}$ is evaluated. We find that $P_\textrm{sep}$ varies by more than a factor of 1.7 across this scan and matches the ${\sim}100$ MW SOLPS-ITER prediction when $Z_\textrm{eff} \simeq 1.6$ or when auxiliary heating is reduced to ${\sim}75\%$ of nominal. Rotation-sensitivity studies show that plausible variations in toroidal flow magnitude modify $P_\textrm{sep}$ by $\lesssim 20\%$, while AURORA modeling confirms that charge-exchange radiation inside the separatrix is dynamically negligible under predicted ITER neutral densities. These results identify a restricted compatibility window, $Z_\textrm{eff} \approx 1.6$ –1.75 and $0.75 \lesssim f_{P_\textrm{aux}} \unicode{x2A7D} 1.0$, in which core transport predictions remain aligned with neon-seeded divertor protection targets. This self-consistent, model-constrained framework provides actionable guidance for impurity control and auxiliary-heating scheduling in early ITER operation and supports future whole-device scenario optimization.

ITER↗
DOE OSTI2025

Simulations of edge and SOL turbulence in diverted negative and positive triangularity plasmas

Optimizing the performance of magnetic confinement fusion devices is critical to achieving an attractive fusion reactor design. Negative triangularity (NT) scenarios have been shown to achieve excellent levels of energy confinement, while avoiding edge localized modes. Modeling turbulent transport in the edge and SOL is key in understanding the impact of NT on turbulence and extrapolating the results to future devices and regimes. Previous gyrokinetic turbulence studies have reported beneficial effects of NT across a broad range of parameters. However, most simulations have focused on the inner plasma region, neglecting the impact of NT on the outermost edge. In this work, we investigate the effect of NT in edge and scrape-off layer simulations, including the magnetic X-point and separatrix. For the first time, we employ a multi-fidelity approach, combining global, non-linear gyrokinetic simulations with drift-reduced fluid simulations, to gain a deeper understanding of the underlying physics at play. First-principles simulations using the GENE-X code demonstrate that in comparable NT and PT geometries, similar profiles are achieved, while the turbulent heat flux is reduced by more than 50% in NT. Comparisons with results from the drift-reduced fluid turbulence code GRILLIX suggest that the turbulence is driven by trapped electron modes. The parallel heat flux width on the divertor targets is reduced in NT, primarily due to a lower spreading factor S.

GENE-X↗
DOE OSTI2024

Fault-Tolerant Decentralized Control for Large-Scale Inverter-Based Resources for Active Power Tracking

Integration of inverter-based resources (IBRs) which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG)-based sources presents a new challenge in the form of analyzing the grid stability under their presence. While the dynamic composition of IBRs differs from that of the SGs, the control objective remains similar in terms of tracking the desired active power. This letter presents a decentralized primal-dual-based fault-tolerant control framework for the power allocation in IBRs. Overall, a hierarchical control algorithm is developed with a lower level addressing the current control and the parameter estimation for the IBRs and the higher level acting as the reference power generator to the low level based on the desired active power profile. The decentralized network-based algorithm adaptively splits the desired power between the IBRs taking into consideration the health of the IBRs transmission lines. The proposed framework is tested through a simulation on the network of IBRs and the high-level controller performance is compared against the existing framework in the literature. The proposed algorithm shows significant performance improvement in the magnitude of power deviation and settling time to the nominal value under faulty conditions as compared to the algorithm in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Predicting plug loads with occupant count data through a deep learning approach

Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-hour predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next eight hours. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%-6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.

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DOE OSTI2025

Final Report for CSP Tower Public Opinion and Education Project

As part of the CSP Plant Optimization Study for the California Power Market (DE-EE0009809) the project wanted to understand the public’s opinion of the technology and and explore the types of community engagement that is needed to support such development. The initial objectives for the public perception activities were twofold. The primary objective was to gather public opinion and feedback from the communities living near the two operating solar power tower plants, Ivanpah and Crescent Dunes, and to distill lessons learned from the community engagement conducted before, during, and after the plants were developed to inform future development. This included outreach to nearby airports. The other objective was to gauge public opinion about large-scale solar, specifically CSP towers, to start educating the public on the benefits and to begin building relationships with communities of interest, initially targeting the Kingman, Arizona area. The objectives shifted after the first exploratory trip to Ivanpah and Kingman, however, as it became apparent that it would be challenging to gather public opinion from the community surrounding Ivanpah that could be useful for other community profiles, and the company wasn’t ready to address the concerns in Kingman. Another area was chosen, therefore, to represent those where future development is possible. The recently published Lawrence Berkeley National Lab Perceptions of Large-Scale Solar Project Neighbors Study exemplified public perception polling based on social science and served as the foundation for the survey questions taken to the field. The intent was to ensure that people knew their input was valued and that the time they spent was valuable for the participant as well. It has been noted in the literature that in-person interaction has greater benefits than activities online or via mail, as well as limits the expense.

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