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At least 451 records · Page 25

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DOE OSTI2026

A machine learning based approach to online electron reconstruction at CLAS12

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle’s type. Of particular interest to electro-production Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. Here, this approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real-time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.

Artificial intelligence↗

DOE OSTI2023

Empirical evidence that mean soil contaminant concentration is not a conservative indicator of external exposure to wildlife

Adequate information on external exposure is often a limiting factor in ecological risk assessments because of a lack of exposure data for free-ranging animals as they interact with a heterogeneous distribution of contaminants. Instead, most wildlife exposure data are estimated from model simulations. However, empirical exposure data are rarely available to verify model results, or to test fundamental assumptions inherent in exposure assessments. We used novel, GPS-coupled radiation dosimeters on reindeer (Rangifer tarandus) to quantify their external exposure for five months in a Chernobyl fallout affected mountainous area of Norway. The exposure data were then used to test a fundamental assumption in screening-level risk assessments: Mean soil contaminant concentrations can be used to conservatively estimate chronic external exposures to representative animals. External exposure and GPS coordinates were obtained hourly and compared to model simulations of exposure based on airborne surveys of 137 Cs soil contamination. Our objectives were to determine 1) if modelled dose estimates based on mean 137 Cs soil concentrations were conservative, and 2) if modelled dose estimates could be improved by adjusting for spatial–temporal heterogeneity of contaminants using 137 Cs concentrations within reindeers’ GPS-defined home ranges and core areas. We found that external exposure modelled from mean soil contaminant concentrations underestimated exposure by 70% compared to empirical measurements from GPS-dosimeter collars worn on individual animals. Using area-weighted mean contaminant levels within the animals’ home range improved the model estimates, but still underestimated the field dosimeter data by 53%. The best estimate of external exposure was obtained when high-use areas within their home range, along with spatial heterogeneity of the contaminant, was accounted for by using area-weighted mean soil contaminant levels within GPS-derived core areas. Accounting for temporal use of core areas within animals’ home range provided a 24% improvement. Here, a comparison of our analyses to similar work performed on Chernobyl wolves found the two studies concur that risk assessors should be cautious in relying on the paradigm of using averaged soil contaminant levels to provide conservative estimates of external exposure to representative individuals. Results of modelled external exposure based on mean soil contaminant concentrations could misguide assessors and cause inappropriate management decisions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Electrodeposition of near-equiatomic CoCuFeNi multi-principal element alloys from an acidic glycine-citrate-triton X100 aqueous electrolyte

Understanding the composition and morphology control of electrodeposited CoCuFeNi is the first step to finding a general strategy for developing electrodeposition processes of unconventional alloys with large redox potential differences and complicated deposition mechanisms. Here, in this work, we have successfully synthesized the near-equiatomic (<5 at% error) CoCuFeNi films with ~200nm thickness by electrodeposition from glycine-citrate-Triton X-100 acidic electrolytes. This system generally follows the Principle II in Brenner’s paradigm on alloy composition control in electrodeposition. X-ray diffraction (XRD) profiles show that the films only consist of one crystalline phase, different from the deposit from ammonia-citrate-boric acid electrolytes and the equilibrium phases predicted by CALPHAD. The near-equiatomic deposits at Ru substrates were successfully annealed at 400 ° C without significant intermixing between the substrate and the deposit, in which no phase separation of the crystalline phase was observed in its XRD profile. Xray photoelectron spectroscopy (XPS) reveals that some depositing elements (mainly Fe) exist as metal oxides. Cu is dissolved in the crystalline phases, stabilized by small crystalline domain size and the amorphous metal oxides inside the CoCuFeNi deposits.

CoCuFeNi↗

DOE OSTI2026

Quantum computing approach for building surface sunlit in urban-scale energy modeling

Solar shadow calculations are needed in building energy modeling and performance simulation of PV systems installed on roofs or facades of buildings. We present a quantum computing approach for calculation of building surface sunlit fractions by recasting solar visibility as a binary optimization problem solved by quantum annealing. Each triangulated surface centroid is encoded as a binary qubit indicating sunlit or shaded status. Geometric visibility constraints are derived from the Möller-Trumbore intersection algorithm and converted into a constrained quadratic binary model compatible with contemporary quantum annealers. The coefficients were embedded to D-Wave quantum computer. To demonstrate feasibility, we conducted a case study in San Francisco for a target building with 52 triangles and roughly 2700 nearby triangles within 50 m evaluated at representative winter and summer solar positions. The results demonstrated that quantum annealing can reliably calculate and distinguish sunlit from shaded surfaces. Quantum samples achieved average accuracy exceeding 92.4 %, with the aggregate surface-level agreement approaching 99.9 %. The outputs of quantum computers agreed closely with classical algorithms, indicating practical feasibility and promising scalability. Finally, the hourly sunlit fractions of building surfaces can be obtained for urban energy modelling. This is the first study to apply quantum computing to the solar shadow and building surface sunlit calculation. It introduces a new paradigm that differs fundamentally from traditional approaches.

Deng, Zhipeng↗

DOE OSTI2020

Cooperation or Rivalry? Impact of alternative development pathways on India's long-term electricity generation and associated water demands

The world has witnessed a significant shift in the last decade in the way countries interact with each other. A cooperation based international paradigm is being challenged by countries engaging in rivalry with each other in the name of their national interests. What developmental pathway the world charts in the future is going to be path defining for global energy and emissions use. India, expected to play a critical role in the emissions mitigation debate, would contribute to and would be impacted by these pathways. In India specific literature, however, little attention has been paid to development pathways related uncertainties and their impact on energy futures. We seek to address this gap by modelling India's electricity futures and associated water demands under different development pathways. Our analysis shows that alternative development pathways would have significantly different implications for the energy access, electricity generation mix, associated water withdrawals, and carbon dioxide emissions in India. We conclude by arguing that energy, emissions and water policies should be conceived with an explicit understanding of uncertainties related to potential development pathways that the world and India can chart.

India, Electricity-Water Nexus, Development Pathwa↗

DOE OSTI2025

Nuclear—thermal energy storage configurations for industrial combined heat and power supply—conceptual and thermodynamic study with high temperature gas-cooled reactor

Nuclear systems are promising candidates for delivering resilient heat and power for future energy security and independence. Traditionally, nuclear plants have been used for baseload electricity production and cogeneration of heat has seen relatively limited application utilizing typically only small portion of a reactor's thermal output. This paradigm may shift due to the increasing penetration of intermittent renewables and need for resource flexibility, various decarbonization efforts aimed at both electricity and heat demands, along with the perspective of small modular nuclear reactor applications, which can be sized based on local industrial needs. Here, this study provides a comprehensive guide for the nuclear and industrial sectors, emphasizing controllability in the combined heat and power configuration options for high temperature gas-cooled reactor and process steam supply. It investigates the integration of thermal energy storage to improve nuclear energy's responsiveness to varying industrial demands. The study emphasizes placing thermal energy storage between the nuclear primary loop and steam cycle to achieve greater efficiency and flexibility in power and heat output, surpassing traditional combined heat and power systems and avoiding efficiency losses seen in other thermal energy storage integration approaches.

combined heat and power (CHP)↗

DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

The flexibility gap: Socioeconomic and geographical factors driving residential flexibility

Residential consumers are moving to the center of electricity systems and their flexibility is seen as a key resource to integrate renewable energy sources and support the grid. However, residential flexibility capacities are not homogeneous, as they depend on household appliances, comfort patterns, occupancy, and climate conditions. In this work, we calculate the technical flexibility capacities of 45 consumer types in mainland Spain, organised according to income and regional criteria. We show that flexibility gaps exist at both regional and socioeconomic (income) levels with flexibility differences of up to 10 times more capacity between the household groups from the lowest to the highest capacities. These geographical and socioeconomic gaps in flexibility can lead to distortions in national markets and have the potential to exclude citizens from the provision of flexibility services. Our results show in quantitative terms that a consumer-centered approach without considering correcting measures nor these gaps in drafting energy policies may lead to increasing inequality levels in the residential sector. Under an economic competitive paradigm, households with lower income levels or located in regions with lower flexibility potential may be excluded from the provision of flexibility to the detriment of households with larger potential, raising justice concerns in a flexibility-based energy transition.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Decoupling the degradation factors of Ni-rich NMC/Li metal batteries using concentrated electrolytes

Battery degradation results from multiple factors that usually correlate with each other. Electrochemical corrosion of the current collector is one of the main reasons for battery failure, leading to poor internal electrical contact and the dissolution of harmful ions into electrolytes. This phenomenon is especially evident in lithium bistrifluoromethanesulfonimidate (LiTFSI)-based electrolytes, although they have many advantages over conventional lithium hexafluorophosphate (LiPF 6 )-based electrolytes, such as low viscosity, good thermal stability, and stable to water. Recently, highly concentrated electrolytes are found hopeful of inhibiting aluminum's corrosion and boosting new electrolyte development. Here we discuss the solvation structures of liquid electrolytes and decouple the degradation mechanisms of using LiTFSI-based electrolytes in Li(Ni 0.8 Co 0.1 Mn 0.1 )O 2 /Li metal batteries. The results suggest although concentrated electrolytes inhibit the Al corrosion, the growth of lithium dendrites and the protection of the cathode-electrolyte interphase are not significantly improved. Furthermore, this paper provides a paradigm to evaluate the performance of electrolytes comprehensively.

25 ENERGY STORAGE↗

DOE OSTI2022

An overview of visualization and visual analytics applications in water resources management

Recent advances in information, communication, and environmental monitoring technologies have increased the availability, spatiotemporal resolution, and quality of water-related data, thereby leading to the emergence of many innovative big data applications. Among these applications, visualization and visual analytics, also known as the visual computing techniques, empower the synergy of computational methods (e.g., machine learning and statistical models) with human reasoning to improve the understanding and solution toward complex science and engineering problems. These approaches are frequently integrated with geographic information systems and cyberinfrastructure to provide new opportunities and methods for enhancing water resources management. Here, we present a comprehensive review of recent hydroinformatics applications that employ visual computing techniques to (1) support complex data-driven research problems, and (2) support the communication and decision-makings in the water resources management sector. Then, we conduct a technical review of the state-of-the-art web-based visualization technologies and libraries to share our experiences on developing shareable, adaptive, and interactive visualizations and visual interfaces for water resources management applications. We close with a vision that applies the emerging visual computing technologies and paradigms to develop the next generation of hydroinformatics applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗

DOE OSTI2022

Online Distribution System State Estimation via Stochastic Gradient Algorithm

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder.

distribution system state estimation↗

DOE OSTI2022

Economic analysis of the benefits to petroleum refiners for low carbon boosted spark ignition biofuels

A refinery modeling framework is developed to estimate the benefits of blending high-quality biofuels directly with refinery gasoline components for attaining a premium grade fuel (also termed as Co-Optima Boosted SI gasoline here). Our results change the paradigm of bio-blendstocks (BBs) being competitors to fossil components, by identifying opportunities for refineries to add value to their product slate, from some favorable BB properties. This potential value can be characterized by calculating the breakeven value (BEV), as defined down below. The proposed modeling framework incorporates extensive data from (1) projected product over the next few decades, (2) crude oil and refinery products pricing, and (3) fuel specifications. The complete refinery models serve as a basis for assessing the value of biofuels, assuming profitability remains the same for representative petroleum refinery configurations. Our assessment showed wide range of variation of biofuels BEV from $\$$20-$\$$120/bbl, within the considered blending level and crude prices. Further, the BEV was correlated with the fuel octane ratings such as octane numbers (research, RON and motor octane numbers, MON) and both antiknock index (AKI, average of RON and MON) and sensitivity (S, difference between RON and MON), with a slightly higher correlation with the sensitivity. However, the expected decrease in gasoline demand for the upcoming years could negatively impact biofuels demand and value, in a business-as-usual scenario. Our analysis also showed a more valuable bio-blendstocks incorporation in smaller refineries since they can enhance the capabilities for producing specialty, high-value fuels/products, and introduce high octane-barrels into otherwise constrained blending operations. Additional implications towards refiners include opportunities to rebalance operations, access to high-value fuel markets, and synchronization with broader transportation industry trends. Furthermore, results indicate the value of Co-Optima boosted spark ignition (BSI) efficiency gains can extend to refiners to incentivize decarbonization and diversified feedstock production.

09 BIOMASS FUELS↗

DOE OSTI2024

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Rieffel, Eleanor G.↗

DOE OSTI2026

DTLMod: A simulation framework for in situ workflow optimization

In situ processing workflows have become essential for coping with the explosion in data volume and velocity in large-scale scientific computing, providing domain scientists with early insights at runtime. Multiple frameworks implement this paradigm through a data transport layer (DTL), offering different data access modes and deployment schemes, but researchers currently lack the appropriate tools to assess design and deployment options before committing to costly real experiments. We introduce DTLMod, an open-source simulated DTL that enables performance evaluation of in situ workflow configurations at scale. Built on SimGrid, it links into any SimGrid-based simulator and is available in C++ and Python. We evaluate DTLMod along four axes: scalability (tens of thousands of simulated processes across interconnected clusters in seconds, with linear memory scaling), versatility (three implementation variants trading fidelity for speed), accuracy (simulated times faithfully reflecting real behavior), and practical utility (two use cases demonstrating evidence-based workflow design decisions).

Suter, Fred [ORNL] (ORCID:0000000319021955)↗

DOE OSTI2025

Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships

Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional- and global-scale soil–environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg−1 averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.

Deep learning↗

DOE OSTI2022

Social vulnerability and power loss mitigation: A case study of Puerto Rico

The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures — such as power grids — as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. Here, in this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-advancing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening.

24 POWER TRANSMISSION AND DISTRIBUTION↗