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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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This work presents NETL’s efforts to update the Baseline LCAs that utilize cross competency data from the 2022 Cost and Performance Baseline Updates. This presentation represents the updates to NGCC, SC PC, and Sub PC baseline models and the results from the LCA analysis of these updated systems.
Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan. Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features. Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin. Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.
This work will gather real-world data on the performance and potential industry impact on laboratory ventilation control of a new low-cost, open-source sensor that reports important metrics of the laboratory environment. A small network of sensors will be installed at an NREL laboratory facility.
This article continues the overview of the U.S. Department of Energy and IDEA collaboration by highlighting additional case studies that demonstrate innovative engineering approaches in district energy systems. It focuses on how campuses are improving efficiency, integrating advanced technologies, and reducing energy use and emissions through data-driven strategies. The University of Cincinnati is featured for its highly efficient chilled water system, using advanced algorithms and upgraded “smart chillers” to optimize performance, reduce energy consumption, and lower costs. Wake Forest University emphasizes a data-informed modernization process, using system optimization and targeted building improvements to significantly boost efficiency and reduce overall energy use. Arizona Western College showcases creative solutions to extreme cooling demands, including system redesign, smart controls, and thermal storage to improve reliability and cut peak energy costs. Overall, the article demonstrates that combining innovative engineering, real-time data analysis, and system optimization can significantly enhance performance, reduce emissions, and deliver cost savings in district energy systems.
Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.
The capture and subsequent immobilization of regulated volatile radionuclides from the off-gas streams of a used nuclear fuel (UNF) reprocessing facility has been a topic of substantial research interest for the US Department of Energy and its international counterparts. Removal of specific radionuclides from the plant effluent streams before discharge to the environment is required to meet regulations set forth by the US Environmental Protection Agency. Upon removal, the radionuclides, as well as associated sorbents that cannot be regenerated in a cost-effective manner, are destined for conversion to a waste form. Research in separation and capture methodologies has included a wide range of technology types, and studies of waste forms are correspondingly diverse. In considering the future development and implementation of both sorbents and waste forms, it is necessary to identify benchmark measures of performance to objectively evaluate each sorbent system or waste form. Sets of performance criteria and associated metrics have been developed for sorbent and waste form evaluation. These criteria address physical, radiological, and chemical characteristics, technical practicality, technical maturity, cost, and, for sorbents, system performance. The criteria and metrics appear to be robust and should be applicable despite the eventual waste classification (as either high- or low-level waste). They are flexible enough to address both aqueous reprocessing and electrochemical reprocessing of UNF. These criteria sets can serve as tools to evaluate performance at multiple stages within the development process, and in this revision (Revision 1) they have been used to assess technologies relating to krypton/xenon separations and iodine capture from off-gas streams arising from UNF reprocessing. Assessment of krypton/xenon separations using engineered forms of two zeolite minerals (silver mordenite and hydrogen mordenite in a polyacrylonitrile-based binder [AgZ-PAN/HZ-PAN]) found that the zeolite-based separation is relatively advanced in its development, but several key issues require resolution. First, desorption processes for both krypton and xenon require refinement to provide an understanding of the product purity that can be achieved. Second, adsorption rate data is needed in order to calculate the bed depth required for effective separation. Finally, it is strongly recommended that a technical review of krypton/xenon separation by AgZ-PAN/HZ-PAN be performed to synergize available data and assess the cost savings and operational benefits that may be realized from implementation of this technology. Assessment of metal organic frameworks (MOFs) for their use in the separation of krypton/xenon found that the ideal separation would be performed using a single-column system with a MOF selective for krypton over xenon. A robust research effort should work to identify a krypton-selective MOF designed to operate at temperatures of approximately 0°C or higher, which could be preferred over cryogenic krypton/xenon capture for used fuel reprocessing off-gas streams. In the case of the CaSDB-MOF (the most well-understood xenon sorbent to date), two issues are judged of high importance. First, xenon breakthrough capacity for the CaSDB-MOF in prototypical conditions should be determined. Preliminary research indicates that breakthrough may be near immediate, presenting a substantial obstacle in separative system design. Second, development of desorption methodology should be performed to determine regeneration time, energy requirements, and the product stream composition. Silver-based sorbents (AgZ and AgAero) for use in iodine capture from the dissolver off-gas were evaluated against the established criteria. These sorbents are significantly better understood for this application as a result of research efforts over the past decade. The potential implementation of AgAero at a large scale is hindered by its physical degradation by components of the dissolver off-gas stream. Less is known about the adsorption of iodine by these sorbents from other off-gas streams in the plant. Initial experimental efforts have been closely coordinated in an effort to understand organic iodine (such as would be found in the vessel off-gas) adsorption by AgZ and AgAero. Future work should expand this experimental program, and analysis of other reprocessing facility off-gas streams such as the vitrification off-gas stream should be conducted to better understand other potential applications for iodine sorbents. A review of iodine waste form development shows that this area is diverse and that multiple promising waste forms have been identified for the immobilization of radioactive iodine. Efforts related to the direct conversion of iodine sorbents (including AgZ and AgAero) should be continued because of the advantages of direct conversion in a waste management strategy and other sorbents should continue to be advanced as merited.
The Hydropower Fleet Intelligence application can integrate hydropower facilities data sources to perform analyses on asset age, reliability and costs, as well as produce visualizations that combine time series data collected by mechanical component sensors with event history.
Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.
With the growing number of applications designed for heterogeneous HPC devices, application programmers and users are finding it challenging to compose scalable workflows as ensembles of these applications, that are portable, performant and resilient. The Kokkos C++ library has been designed to simplify this cumbersome procedure by providing an intra-application uniform programming model and portable performance. However, assembling multiple Kokkos-enabled applications into a complex workflow is still a challenge. Although Kokkos enables a uniform programming model, the inter-application data exchange still remains a challenge from both performance and software development cost perspectives. In order to address this issue, we propose Kokkos data staging memory space, an extension of Kokkos' data abstraction (memory space) for heterogeneous computing systems. This new abstraction allows to express data on a virtual shared-space for multiple Kokkos applications, thus extending Kokkos to support inter-application data exchange to build an efficient application workflow. Additionally, we study the effectiveness of asynchronous data layout conversions for applications requiring different memory access patterns for the shared data. Our preliminary evaluation with a synthetic benchmark indicate the effectiveness of this conversion adapted to three different scenarios representing access frequency and use patterns of the shared data.
A lack of data has been identified as a major challenge in human reliability analysis (HRA). Accordingly, several institutes and researchers have tried to collect HRA data from different data sources such as actual historical measurements, expert judgements, or simulator studies. While the most recent studies predominantly focus on collecting data using full-scope simulators with actual operators, Idaho National Laboratory (INL) has begun to collect HRA data using a simplified simulator, i.e., the Rancor Microworld simulator, with student participants. Full-scope studies have been known to have several intrinsic challenges to securing enough quantity of the data due to many reasons like the high cost for performing experiments or requiring actual operators’ cooperation. The ultimate goal of INL’s effort aims to infer actual operators’ data collected from a full-scope simulator on the basis of microworld data with student subjects as well as collect additional data that could be missed in the full-scope research. As a first step to achieve this goal, this paper projects an experimental plan for investigating the differences in human performance between individuals in two groups: 1) an actual operator and 2) a student when using the Rancor Microworld simulator. A randomized factorial experiment design has been developed with two independent variables, i.e., type of scenario and type of subject. Six human performance measures, i.e., 1) time, 2) error, 3) workload, 4) situation awareness, 5) patterns of attention and 6) the number of manipulations were selected. A couple of scenarios and their procedures available to the Rancor Microworld simulator have been developed.
Utility-scale solar photovoltaics (PV) is the largest and fastest-growing sector of the solar energy market, and plays an important role in ensuring that state and local jurisdictions can meet renewable energy targets. Potential adverse environmental impacts of utility-scale solar PV are well-documented, and the effects of diverse mitigation and dual land use strategies under the banner of ’low-impact solar’ are justly receiving more attention; this article seeks to contribute to improving understanding of this topic. Capital costs for different PV configurations are well-documented; however, operation and maintenance (O&M) costs for vegetation management at low-impact utility-scale solar PV sites are not as well-understood, particularly as they compare to costs for sites that use more conventional ground cover practices, such as turfgrass or gravel. After a literature review of different vegetation strategies and O&M cost considerations, we collected data from utility-scale solar PV O&M stakeholders, including site owners/operators, O&M service providers, vegetation maintenance companies, and solar graziers, on costs and activities associated with vegetation management at low-impact, agrivoltaic, and conventional PV sites. In this paper, we perform data analysis to detail the per-activity and total O&M costs for vegetation management at PV sites with different ground covers and management practices, providing the most comprehensive and detailed assessment of PV vegetation O&M costs to date. For the 54 sites included in our analysis, we found that while the per-acre and per-kilowattdc (kWdc) costs for individual activities, such as mowing, trimming, and herbicide application at native or pollinator friendly ground covers, were lower than at turfgrass sites, the total combined vegetation O&M costs were slightly higher; this is presumably because more individual activities are required for the first 3–5 years of vegetation establishment. Qualitative results include recommendations from data providers for site and system design, and ongoing vegetation management operations.
Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data for 845 projects from interconnection studies for the Southwest Power Pool (SPP) Territory. The studies were performed between 2002 and 2023 and include all of the most refined cost estimates available. Project-level cost summary data are available for download on this page. We find: -Project-specific interconnection costs can differ widely. -Average interconnection costs are stable for projects that complete all interconnection studies but have escalated for those that withdraw. -Broader network upgrade costs are the primary driver of recent cost increases, especially for withdrawn projects. -Potential interconnection costs of all solar and wind requests have been greater than those of storage and natural gas projects. -Economies of scale exist for completed wind and solar projects but not for other fuel types or withdrawn projects. -Interconnection costs vary by location. Berkeley Lab publishes a series of short analytical papers of generator interconnection costs to the transmission system for MISO, PJM, SPP, ISO-NE and NYISO, which you can find at https://emp.lbl.gov/interconnection_costs.
Modern supercomputing systems are increasingly reliant on hierarchical, multi-tiered file and storage system architectures due to cost-performance-capacity trade-offs. Within such multi-tiered systems, data management services are required to maintain healthy utilization, performance, and capacity levels. We present PoliMOR, a pragmatic and reliable policy-driven data management framework. PoliMOR is composed of modular, single-purpose agents that gather file system metadata and enforce policies on storage systems. PoliMOR facilitates automated and scalable data management with customizable agents tailored to HPC facility-specific storage systems and policies. Our evaluations demonstrate the scalability and performance of PoliMOR both by its individual agents and as a collective entity. We believe PoliMOR is widely applicable across HPC facilities with large-scale data management challenges and will garner interest from the HPC community, given its flexible and open-source nature.
While in-situ workflow formulations have addressed some of the data-related challenges associated with extreme-scale scientific workflows, these workflows involve complex interactions and different modes of data exchange. In the context of increasing system complexity, such workflows present significant resource management challenges, requiring complex cost-performance tradeoffs. This paper presents RISE, an intelligent staging-based data management middleware, which builds on the DataSpaces framework and performs intelligent scheduling of data management operations to reduce I/O contention. In RISE, data are always written immediately to local buffers to reduce the effect of the transfer impact upon application performance. RISE identifies applications’ data access patterns and moves data towards data consumers only when the network is expected to be idle, reducing the impact of asynchronous background data movement upon critical data read/write requests. Here, we experimentally demonstrate that RISE can take advantage of staging nodes to offload data during writes without degrading application data movement performance.
The albedo of the ground surface is an important factor in the cost-effectiveness of a bifacial photovoltaic (PV) system. To improve the availability of reliable albedo data, datasets of ground albedo and associated meteorological data were developed by using existing measurement network data and data measured by the PV industry. The measured datasets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Satellite-derived values of albedo are available from the National Solar Radiation Data Base (NSRDB). The NSRDB albedos were compared to the measured albedos for Surface Radiation budget (SURFRAD) network locations for the period 2001–2017, and the mean bias difference results were from -0.044 to +0.056. Overall, these differences are greater than the albedo measurement uncertainty of ±0.02; consequently, the NSRDB albedos should be used with caution for estimating the performance of bifacial PV systems. Differences between SURFRAD and NSRDB albedos are attributed to the NSRDB method for determining albedo and to the ground surfaces within the NSRDB 4 km spatial resolution pixel consisting of a mixture of surface types rather than just the single surface types viewed by the albedometers at the SURFRAD stations.
Bandwidth demand for datacenter networks continues as performance increases and is further fueled by the exploding demand for AI and new HPC workloads. Managing power and costs will require a range of solutions including new networking and workload specialized architectures, composable systems and optical circuit switching. In this study we focus primarily on two topics, examining the benefits of flatter networks (enabled mainly by means of co-packaged-optics-enabled switches) and the utilization improvement potential for composable (disaggregated) systems, while discussing specialized hardware and networks, and optical circuit switching more briefly.
Increases in data volumes are forcing high-energy and nuclear physics experiments to store more frequently accessed data on tape. Extracting the maximum performance from tape drives is critical to make this viable from a data availability and system cost standpoint. The nature of data ingest and retrieval in an experimental physics environment make achieving high access performance difficult given the inherent limitations of magnetic tape. Tailoring the layout of data on tape is one key to improving read performance. This paper highlights the work in progress to characterize ATLAS data ingested in the tape system, understand how data layout, i.e. file co-location on tape and file distribution over tapes, affect read performance and how optimal data layout might be achieved in a production environment.