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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2025

Equitable Urban Electric Vehicle Charging: Feasibility and Benefits of Streetlight Charging in Kansas City Right-of-Way

With an increasing global emphasis on sustainability, electric vehicles (EVs) play a crucial role in reducing urban pollution and carbon emissions. For EVs to be widely adopted, equitable and convenient access to charging infrastructure is essential. Equity in this paper refers to the proactive engagement with the community to ensure that the benefits of streetlight charging are distributed equitably across diverse neighborhoods in Kansas City, providing fair charging opportunities and resources to all community members. This research explores the potential of utilizing streetlights—ubiquitous elements of urban electrical infrastructure—as low-cost, equitable EV charging solutions. Compared with conventional chargers, streetlight charging offers several notable advantages, including proximity to roadways, potential boosts to the local economy, and easier usage due to city ownership. These chargers also leverage existing power setups to minimize costs and maximize efficiency by reutilizing existing structures. In this manuscript, we introduce a systematic framework to develop, analyze, and evaluate a scalable and cost-effective streetlight charging solution. Initially, we employ a two-tiered site selection framework that accounts for both charging demand and equity considerations to identify optimal locations for streetlight chargers. We then assess the feasibility of these chargers through observational data from 23 units installed in Kansas City, Missouri, comparing their performance with traditional chargers. Here, our evaluation extends to the environmental impact, comparing reduced gasoline consumption and greenhouse gas (GHG) emission reductions between streetlights and regular charging stations. Our findings reveal that streetlight charging stations offer faster charging speeds than conventional stations, validating their viability for EV charging. Additionally, the duration of stay at streetlight chargers tends to be shorter, likely due to parking constraints or associated costs. Furthermore, streetlight-powered chargers demonstrate considerable environmental advantages, achieving 11.94% greater avoided gasoline consumption and 11.24% higher GHG reductions compared with regular charging stations, highlighting their substantial environmental benefits.

42 ENGINEERING↗
DOE OSTI2022

Comparative Assessment of Severe Acute Respiratory Syndrome Coronavirus 2 Variants in the Ferret Model

The continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans necessitates evaluation of variants for enhanced virulence and transmission. We used the ferret model to perform a comparative analysis of four SARS-CoV-2 strains, including an early pandemic isolate from the United States (WA1), and representatives of the Alpha, Beta, and Delta lineages. While Beta virus was not capable of pronounced replication in ferrets, WA1, Alpha, and Delta viruses productively replicated in the ferret upper respiratory tract, despite causing only mild disease with no overt histopathological changes. Strain-specific transmissibility was observed; WA1 and Delta viruses transmitted in a direct contact setting, whereas Delta virus was also capable of limited airborne transmission. Viral RNA was shed in exhaled air particles from all inoculated animals but was highest for Delta virus. Prior infection with SARS-CoV-2 offered varied protection against reinfection with either homologous or heterologous variants. Notable genomic variants in the spike protein were most frequently detected following WA1 and Delta virus infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multilayer Electrodes with Metalized Polymer Current Collector for High-Energy Lithium-Ion Batteries with Extreme-Fast-Charging Capability

The pursuit of batteries capable of extreme fast charging (XFC), that also satisfy high energy and safety criteria, poses a significant challenge to current lithium-ion battery technologies. Additionally, the increasing demand for aluminum (Al) and copper (Cu) in electrification, and vehicle light weighting is driving these metals towards near-critical status in the medium term. This study introduced metalized polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate (PET) film – named mPET/Al and mPET/Cu, as lightweight, cost-effective alternatives to traditional metal current collectors in LIBs. We have utilized current collectors that significantly reduce weight (by 73%), thickness (by 33%), and cost (by 85%) compared to traditional metal foil counterparts. We conducted an extensive evaluation of their mechanical and electrical properties, including in-plane and through-plane resistivities, affirming their suitability for the roll-to-roll battery manufacturing process. Additionally, a novel XFC testing protocol was employed to thoroughly assess the cells' (both half and full-cell) performance across various C-rates and under long-term tests. These advancements have the potential to enhance energy density to 280 Wh/kg at the electrode level under 10-minute charging at 6C. Through testing, including a novel XFC protocol across various C-rates and long-term cycling (up to 1000 cycles) in different cell configurations, we have demonstrated the superior performance of these metalized polymer films. Notably, mPET/Cu and mPET/Al foils exhibited comparable capacities to conventional cells under XFC, with the mPET cells showing a 27% improvement in energy density at 6C and maintaining significant energy density after 1000 cycles. This study underscored the potential of mPET foils to revolutionize the roll-to-roll battery manufacturing process and significantly advance the performance metrics of LIBs in EV applications. Moreover, our results suggest that there is potential to enhance the performance of mPET foils, especially mPET/Al, by optimizing the manufacturing process to achieve higher conductivity.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A Comparative Study of Electrical Double Layer Effects for CO Reduction Reaction Kinetics

Solvation models describe how the interactions between the solutes and solvents affect the reactivity and selectivity in electrochemical processes. In this study, we developed a framework for evaluating the effects of applied potential and electrical double layer on CO reduction (COR), comparing fully explicit, implicit, and hybrid solvation models at the standard hydrogen electrode (SHE) scale. We analyzed all crucial intermediates leading to the production of C 1 and C 2 products and found good agreement across these models. Some notable differences were observed in the implicit description of *C and *CO adsorption at higher overpotentials and overall trends in the adsorption energies within the hybrid model. Using this unified SHE framework, we built comparable microkinetic models for COR kinetics and rates. Despite small differences in thermodynamics, solvation-model-based microkinetic simulations showed good agreement for onset potentials against the benchmark experiment. Only qualitative difference was observed for C 1+ versus hydrogen evolution at high overpotentials for the implicit model. Finally, we constructed a generalized C 2 selectivity map in descriptor space (U SHE , ΔG electrolyte CO ), which highlights the limitations of a copper-based COR catalyst and guides the search for optimal descriptor parameters to maximize C 2 selectivity. In conclusion, these findings demonstrate the importance of considering electrical double layer effects in reduction reactions and offer a useful framework for comparing solvation models and predicting optimal electrochemical conditions for specific applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Life-cycle analysis of microalgae-based polyurethane foams

Polyurethane plastics are essential in many consumer and commercial products such as insulation, furniture, automotive interiors, and clothing. Pathways for producing polyurethane from microalgae offer an opportunity to reduce greenhouse gas emissions and other environmental impacts and can incorporate processes that avoid the use of toxic isocyanates typically used in conventional polyurethane production processes. In this study, the greenhouse gas emissions, fossil energy, and water consumption of biobased polyurethane and biobased non-isocyanate polyurethane were evaluated via life-cycle analysis using the R&D Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies model. Microalgae-based polyurethane foam was found to achieve greenhouse gas emission reductions of up to 79% compared with conventional polyurethane foam production. The greenhouse gas reductions for the non-isocyanate microalgae polyurethane pathway are slightly lower at 58% compared with conventional polyurethane foam. However, it offers additional benefits by reducing toxicity potential compared to the isocyanate polyurethane pathway. The analysis also included a biorefinery-level analysis to evaluate the impact of incorporating polyurethane production into fuel-processing microalgae biorefineries. The sensitivity analyses conducted in this study reveal that improved algae cultivation strategies can lead to decreases of up to 127% and 80% in GHG emissions from the baseline process of Bio-PU and Bio-NIPU, respectively. Likewise, implementation of renewable electricity can result in up to 128% and 74% lower GHG emissions compared to the baseline production of Bio-PU and Bio-NIPU, respectively. Finally, the analysis evaluated different coproduct handling methods including displacement and allocation (based on mass, energy, and market-value). The results suggest that it is important to consider both the displacement and allocation methods as these led to significant differences in the environmental impacts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Equipment List Comparing Balance of Plant Containing a Heat Pump against a Reference Electricity Generating Plant

Approximately two-thirds of U.S. energy consumption in the industrial and transportation sectors relies on fossil fuels. These sectors require high-quality heat, i.e., thermal energy at very high temperatures, for molecular transformation processes. The Integrated Energy Systems (IES) program aims to assess the economic potential of utilizing nuclear-grade heat from Advanced Reactors (ARs) to meet the high-quality heat demands. By having industrial processes (IPs) supplied with nuclear-generated heat, manufacturers could benefit from more stable and potentially lower energy costs, reducing reliance on volatile fossil fuel markets. The main outcome of the FY24 research was the thermodynamic assessment and gap analysis of steam generation for IP applications. The study completed in June 2024 demonstrated that the required steam temperatures could be achieved by integrating a heat pump into an AR power plant. After identifying the thermal demands of target IPs, multiple balance of plant configurations for the Xe-100 reactor by X-energy, incorporating a heat pump, were analyzed. Their technical feasibilities were assessed, including the design of suitable axial compressors for these applications. Comparative performance analysis showed that thermal efficiency alone is insufficient to evaluate system suitability. To address this, a new indicator (heat factor) was introduced in the report released in September 2024 to quantify the low-quality thermal power needed to produce one unit of high-quality heat for the IP. Results showed that integrating heat pumps into Rankine cycles enables higher steam temperatures, though at the expense of increased thermal energy input. This report builds upon and completes the foundational work previously undertaken. It focuses on the design of two Balance of Plant (BOP) configurations, both based on Rankine energy conversion cycles: “Case 1”, which involves electricity generation only, and “Case 2”, which combines electricity and high-temperature heat generation for industrial use. For each configuration, a comprehensive equipment list was developed, detailing all major components such as turbines, compressors, heat exchangers, pumps, and control systems. These lists will serve as the basis for future comparative cost analyses, with the goal of assessing the number and type of components required to integrate a heat pump into the Rankine cycle and to establish a heat transport system capable of delivering thermal energy from the nuclear plant to an industrial facility. Using the constitutive equations presented in the June 2024 milestone, the operating conditions of all BOP components for both “Case 1” and “Case 2” were evaluated. These parameters—such as temperature, pressure, mass flow rate, and steam quality—served as the basis for calculating the associated thermal and mechanical power flows. The net power required from the heat pump to raise the steam temperature to the target level was also determined. The thermodynamic performance of the configuration was then assessed using the heat factor metric. The key outcome of this analysis is a comparative table that presents the equipment that was used in the “Case 1” and “Case 2” configurations. This study offers a preliminary comparison of the two designs, providing insight into the impact of integrating a heat pump in terms of component requirements and thermal efficiency. This equipment list, together with the evaluated operating conditions, also serves as a foundation for the economic analyses scheduled for the current fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Power System Resilience Evaluation Framework and Metric Review

Power system resilience has been an emerging hot topic in recent years to investigate the increasing threats of extreme events, such as natural disasters, severe weather, and cyberattacks. Although much research has been done to define, model, and quantify resilience from different aspects, the lack of universally accepted evaluation methods and resilience metrics makes it difficult to assess and compare resilience across different power systems, such as what is typically done in power system reliability studies. In this paper, first, we review the definitions of resilience, and we summarize two core concepts shared by most of the literature. Then, we develop a new framework to assess power system resilience from two perspectives - i.e., pre-event estimation and post-event evaluation - to capture system resilience performance in both general and specific fashions. We conduct a thorough review of existing resilience metrics and categorize them using the proposed framework, where recommendations are also proposed to capture core concepts of resilience.

power system resilience↗
DOE OSTI2021

Bio-Based Polyisoprene Can Mitigate Climate Change and Deforestation in Expanding Rubber Production

Biomass is a promising renewable feedstock to produce polyisoprene for the rubber industry. Through metabolic engineering, sugars derived from pretreated and hydrolyzed cellulose and hemicellulose can be directly fermented to isoprene to produce rubber. Here we investigate the life cycle environmental impact of isoprene fermentation to produce bio-polyisoprene from agricultural residues (of Zea mays L.). Results show that the greenhouse gas (GHG) intensity of bio-polyisoprene (−4.59 kg CO2e kg−1) is significantly lower than that of natural rubber (Hevea brasiliensis) and synthetic rubber (−0.79 and 2.41 kg CO2e kg−1, respectively), while supporting a circular biogenic carbon economy. We found the land use intensity of bio-polyisoprene to be 0.25 ha metric ton−1, which is 84% lower than that from rubber tree plantations. We compare the direct fermentation to isoprene results with indirect fermentation to isoprene through the intermediate, methyl butyl ether, where dehydration to isoprene is required. The direct fermentation of isoprene reduces reaction steps and unit operations, an expected outcome when employing process intensification, but our results show additional energy conservation and reduced contribution to climate change. Among the ReCiPe life cycle environmental impact metrics evaluated, air emission related impacts are high for bio-polyisoprene compared to those for natural and synthetic rubber. Those impacts can be reduced with air emission controls during production. All other metrics showed an improvement for bio-polyisoprene compared to natural and synthetic rubber.

Batten, Rahamim↗
DOE OSTI2020

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter‐mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional‐OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine‐drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra‐individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady‐state effect regardless of OCT2 directionality assumptions. However, only the bidirectional‐OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

Scotcher, Daniel↗
DOE OSTI2025

Molecular beam epitaxy growth and optoelectronic properties of droplet-free lattice-matched GaInAsSbBi on GaSb with wavelength extension exceeding 5 μ m

GaInAsSbBi alloys are grown lattice-matched on GaSb by molecular beam epitaxy demonstrating smooth surface morphologies, >5 μm wavelength photoluminescence emission, and minority carrier lifetimes >1 μs. At a growth temperature of 400 °C, the Ga flux is systematically increased and the Bi flux systematically decreased to identify GaInAsSbBi growth conditions that yield smooth droplet-free surface morphologies. The minority carrier lifetime is evaluated using time-resolved photoluminescence, where it is observed that GaInAsSbBi samples exhibit minority carrier lifetimes comparable to their Bi-free GaInAsSb counterparts, on the order of 1.5–2 μs. The bandgap and Urbach energy are evaluated from steady-state photoluminescence to gain insight into the impact of the incorporated Bi. Coupled with Rutherford backscattering spectrometry measurements of the Bi mole fraction, bandgap reduction rates of 97 meV/% Bi in InAsSbBi and 150 meV/% Bi in GaInAsSbBi are observed, significantly higher than previous evaluations in InAsSbBi (35–55 meV/% Bi). Detailed comparisons of the Bi mole fraction, bandgap energy, and Urbach energy indicate that the bandgap reduction potential in this alloy system is inhibited by the formation of Bi clusters; however, the inclusion of Ga in the quinary alloy is effective in suppressing Bi's tendency to incorporate in clusters for Ga mole fractions >9%, maximizing the bandgap reduction per unit Bi and overall optoelectronic quality.

Atomic force microscopy↗
DOE OSTI2024

Ducted Fuel Injection And Cooled Spray Technologies For Particulate Control In Heavy-duty Diesel Engines (Final Report)

Cooled Spray (CS) and Ducted Fuel Injection (DFI) are in-cylinder technologies for diesel engines that can reduce particulate matter and soot emissions and data has been published showing that these technologies can reduce soot emissions by 75-100% for some engines at some operating conditions. However, little is known about scaling the devices for engine size. Additionally, the performance of either technology over the engine duty cycle has not been explored. This project addresses both of these points through single-cylinder engine investigations. The objectives of this project are to provide details about dimensional scaling of these devices and to demonstrate 75% PM reduction over a range of operating conditions on a single-cylinder engine. Two engines were used for this project: a 125mm bore optically accessible engine at Sandia National Laboratories and a 168mm bore metal engine at Southwest Research Institute. The optical engine was used to study the performance of DFI and CS inserts for a large injector orifice diameter injector that is characteristic of a locomotive engine and to perform scaling studies for DFI. The metal engine was used to perform scaling and alignment studies for CS and to evaluate the technology for both EGR and non-EGR engines over the engine operating map. Modifications were required for both engines to accept the prototype inserts being tested. The optical engine required a new fuel injector, cylinder head and piston so that tests could be run at the pressures and engine speeds required. Additionally, a novel rotating stage was designed for the optical engine to simplify alignment of the modules. The metal engine required a modified cylinder head to accept CS inserts and a modified piston to provide additional space around the fuel injector for the CS inserts. Tests on the optical engine showed that DFI reduces PM emissions for both small injector orifices (0.170mm diameter) and large injector orifices (0.290mm). For high load testing, the DFI modules were not as effective as at low load testing, but it was acknowledged that minimal geometric optimization was performed and more improvements may be possible. Comparing DFI to CS and conventional diesel combustion (CDC), DFI performed better than CS or CDC. The CS geometries used in these studies may not be ideal for that engine and additional modifications likely would improve performance. Tests on the metal engine showed PM reductions as high has 80% at some operating conditions with duty-cycle PM reductions of ~50% for EGR and non-EGR configurations. The CS testing on the metal engine showed that chamfering of the fuel passage inlet either through hydro-erosion or mechanical grinding provided significant improvements in the PM reduction capabilities of the insert. Additionally, alignment sensitivities were explored and the data show that the tolerance to misalignment is approximately 0.05 to 0.1mm for the inserts that were studied here. Air-fuel ratio was shown to be important in the effectiveness of the CS inserts. In several tests, it was shown that the CS inserts are more effective at reducing the PM for high-AFR operating conditions compared to low AFR conditions. In summary, multiple designs were evaluated on both engines. It was found that for the conditions and configurations studied here, a fuel passage diameter of ~2.5mm performed best overall. Significant duty-cycle PM reductions are possible using these technologies and sensitivities to AFR, alignment fuel passage diameter and inlet fuel passage shaping were explored and are reported here. More PM reduction may be possible with improved geometric design and attention to alignment practices.

02 PETROLEUM↗
DOE OSTI2020

Methodology for Generating Covariance Data of Thermal Neutron Scattering Cross Sections

This paper details and implements a framework for evaluating thermal neutron scattering cross sections that provide S(α,β) data and covariance data for hydrogen in light water. This methodology involves perturbing model parameters of molecular dynamics potentials and fitting the simulation results to experimental data. The framework is general and can be applied to any material or simulation method. The fit is made using the Unified Monte Carlo method to experimentally measure double-differential scattering cross sections of light water at the Spallation Neutron Source at Oak Ridge National Laboratory. Mean values and covariance data were generated for model parameters, phonon density of states, double-differential cross sections, and total scattering cross sections. These posterior parameter values were very similar to their prior values with a maximum relative error of 0.54%. This falls within in the Unified Monte Carlo–calculated uncertainties on the order of 2.7%. Additionally, posterior double-differential cross sections agree favorably with ENDF/B-VIII.0 cross sections. The new thermal scattering law was tested by comparing it against benchmarks from the International Criticality Safety Benchmark Evaluation Project Handbook, which showed a slight improvement over the ENDF/B-VIII.0 library. Additionally, the covariance matrix of the phonon density of states was validated to confirm that the spread of k eff from the density of states used to generate the covariance matrix was similar to the spread of k eff from the density of states of the sampled covariance matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Uncertainty quantification for deep learning in particle accelerator applications

With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Improved Method for Evaluating and Specifying the Chromaticity of Light Sources

This article recommends a method for calculating and specifying light source chromaticity using the CIE 2015 10° color matching functions (CMFs), which, according to analysis of existing psychophysical experiment data, can reduce visual mismatch compared to specifications based on the traditional CIE 1931 2° CMFs. Specifically, this work evaluates, documents, and recommends for adoption a supporting system of measures to be used with the CIE 2015 10° CMFs: a new uniform chromaticity scale (UCS) diagram with coordinates (s, t), a measure of correlated color temperature (CCTst), and a measure of distance from the Planckian locus (Dst). It also presents options for updating nominal classification quadrangles. A complete method of this nature has not yet been standardized, which may be contributing to the slow uptake of the CIE 2015 CMFs. The proposed tools are analogous to u, v, CCT, Duv, and the ANSI C78.377 nominal classification quadrangles that are currently specified in the CIE 1960 UCS using the CIE 1931 2° CMFs. While conceptually equivalent, the differences between the current standard method and the new st system are important for reducing unintended mismatch in the chromaticity of light. Furthermore, the implications of changing chromaticity specification methods are identified by a comparison over a diverse set of real light source SPDs. Finally, as a step toward improving the design and specification of light sources for illumination purposes, we propose a process for implementing the recommendations.

42 ENGINEERING↗
DOE OSTI2022

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

We describe the experience of converting a CUDA implementation of a parallel graph coloring algorithm to SYCL. The goals are for our work to be useful to application and compiler developers by providing a detailed description of migration paths between CUDA and SYCL. We will describe how CUDA functions are mapped to SYCL functions. Evaluating the CUDA and SYCL implementations of the algorithm shows that the performance of SYCL and CUDA kernels are comparable over the test graph set on NVIDIA P100 and V100 GPUs. The SYCL program also allows for performance evaluation with the OpenCL and Level Zero interfaces and power profiling on an Intel GPU computing platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Feasibility Analysis of Converter-Interfaced Combined Heat and Power System

As a promising new design concept, the converter-interfaced combined heat and power (CHP) system is coupled to the bulk grid through a rectifier and a grid-ready tied inverter. Compared to the traditional directly-coupled CHP system, it removes the requirement for oversizing the CHP generator, limits the short-circuit contribution of the generator and simplifies the grid integration process of CHP system. This paper evaluates the economic benefits of this concept by calculating the annualized Return-on-Investment (ROI) and comparing it to the directly-coupled system. The economic analysis includes timeseries simulations to compute energy transactions with the bulk grid as well as sizing the equipment to calculate the capital and operational costs. Obtained results indicate that in majority of user cases evaluated, the converter-interfaced CHP systems can provide better ROI than directly-coupled systems. Given the additional technical benefits provided by inverter-based distributed energy resources (DERs), the proposed concept is proved to be technically viable and economically feasible.

03 NATURAL GAS↗