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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2023

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

Samuel, David↗
DOE OSTI2021

Demystifying asynchronous I/O Interference in HPC applications

With increasing complexity of HPC workflows, data management services need to perform expensive I/O operations asynchronously in the background, aiming to overlap the I/O with the application runtime. However, this may cause interference due to competition for resources: CPU, memory/network bandwidth. The advent of multi-core architectures has exacerbated this problem, as many I/O operations are issued concurrently, thereby competing not only with the application but also among themselves. Furthermore, the interference patterns can dynamically change as a response to variations in application behavior and I/O subsystems (e.g. multiple users sharing a parallel file system). Without a thorough understanding, I/O operations may perform suboptimally, potentially even worse than in the blocking case. To fill this gap, here we investigate the causes and consequences of interference due to asynchronous I/O on HPC systems. Specifically, we focus on multi-core CPUs and memory bandwidth, isolating the interference due to each resource. Then, we perform an in-depth study to explain the interplay and contention in a variety of resource sharing scenarios such as varying priority and number of background I/O threads and different I/O strategies: sendfile, read/write, mmap/write underlining trade-offs. The insights from this study are important both to enable guided optimizations of existing background I/O, as well as to open new opportunities to design advanced asynchronous I/O strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

S4PST: Sustainability for Programming Systems and Tools: May Workshop Report

The US Department of Energy (DOE) Exascale Computing Project (ECP) has fostered and strengthened the use of modern software engineering practices for developing applications and libraries, and this effort has resulted in the coordinated and interoperable E4S1 and xSDK2 ecosystems. Although this approach is cost-effective, it relies on robust programming systems and tools (PST) as the underlying foundation for our HPC software. At present, our primary PST stack consists of traditional high-performance computing (HPC) languages, namely Fortran, C, C++, and the popular Python language for data analysis and AI workflows. These languages support various programming frameworks and run-time abstractions that enable parallelism and concurrency across multiple node architectures and thousands of nodes through a variety of interconnect systems. However, to accommodate users’ diverse needs, certain aspects of the HPC ecosystem are delegated to vendor-specific or third-party implementations that extend beyond a particular scientific domain. This broader scope results in a multitude of specifications and variations, which leads to a complex orchestration of many-ecosystems. Unfortunately, this complexity in the ecosystem imposes additional overhead costs on consumers during the latter stages of the development cycle. In addition to the software ecosystem challenge, the upcoming conclusion of the ECP by December 2023 has raised significant concerns within the HPC programming systems community, from both the economic and social perspectives. The ECP has implemented a management structure for software development and funding decisions across all ECP participants by following a conventional hierarchical and centralized approach. However, this structure has prompted certain considerations within the community, particularly in anticipation of the Software Sustainability initiative by the DOE’s Advanced Scientific Computing Research Program (ASCR). For the success of this new initiative, it is of utmost importance to secure consistent funding and foster close engagement with researchers and core developers of existing programming-system products. This collaboration is vital to maintaining the critical capabilities of the current software during the transition phase while proactively adapting to future technology and workforce trends. The community recognizes the significance of adapting to emerging trends and is aware of the inherent fragility of the HPC software ecosystem, particularly in relation to programming systems that cater to all users. The ability to adapt and evolve is essential to staying relevant and effectively addressing these technical, economic, and social challenges. The S4PST team, which represents one of the six ASCR Software Sustainability seedling projects, is dedicated to tackling these challenges through community-based approaches that go beyond the scope of the DOE. This involves collaboration between national laboratories with academia, non-DOE institutions, hardware and system vendors, and international partners. By fostering these partnerships, we aim to create a robust and sustainable HPC software ecosystem that can effectively meet the needs of the community. This new community effort, driven by the eight DOE labs, will take on the responsibility of guiding funding decisions for programming-systems development and maintenance with transparency and consistency across all decisions. Additionally, the team will offer common technical services to the programming systems community, irrespective of their funding situations, and facilitate community-wide incubation to proactively nurture the software ecosystem. By actively engaging with stakeholders and employing a collaborative approach, we can collectively shape the future of programming systems and ensure a robust and thriving HPC software landscape. On May 11–12, 2023, the S4PST team conducted its inaugural kick-off workshop at the Innovative Computing Laboratory (ICL) in the University of Tennessee, Knoxville, hosted by Hartwig Anzt. The workshop encompassed various sessions dedicated to presentations and discussions, with the aim of comprehending the team members’ perspectives on the vision of software sustainability. Additionally, the workshop aimed to identify the technical, economic, and social requirements for sustaining the programming-systems community in the field of HPC. This report provides a summary of the S4PST effort by highlighting five major thrust areas discussed during the workshop: (i) community, (ii) technical support, (iii) training and diversity, (iv) verification, validation and correctness, and (v) emerging technologies. It also encompasses an overview of the presentations and discussions held throughout the event, our views and potential synergies with other seedling efforts, along with the outcomes and key takeaways from our initial discussions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats

Abstract Data standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories did not use key features that could help developers to gather user feedback, or to create and revise standards that build on previous work. We provide guidance for community‐driven standard development and associated documentation on GitHub based on a systematic review of existing practices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗
DOE OSTI2022

Pulse-level noisy quantum circuits with QuTiP

The study of the impact of noise on quantum circuits is especially relevant to guide the progress of Noisy Intermediate-Scale Quantum (NISQ) computing. In this paper, we address the pulse-level simulation of noisy quantum circuits with the Quantum Toolbox in Python (QuTiP). We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian that describes the unitary evolution of the physical qubits. Various types of noise can be introduced based on the physical model, e.g., by simulating the Lindblad density-matrix dynamics or Monte Carlo quantum trajectories. In particular, the user can define environment-induced decoherence at the processor level and include noise simulation at the level of control pulses. We illustrate how the Deutsch-Jozsa algorithm is compiled and executed on a superconducting-qubit-based processor, on a spin-chain-based processor and using control optimization algorithms. We also show how to easily reproduce experimental results on cross-talk noise in an ion-based processor, and how a Ramsey experiment can be modeled with Lindblad dynamics. Finally, we illustrate how to integrate these features with other software frameworks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

Background: Exploring metagenomic contigs and “binning” them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure. Results: We present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time. In demonstration of BinaRena’s usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset. Conclusions: BinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena, together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SMATool: Strength of materials analysis toolkit

The study of the strength of materials is a cornerstone in material science and engineering, playing a critical role in shaping the progress and application of materials in diverse industrial sectors. The strength of a material is meticulously examined to understand the behavior of the material under different stress conditions and environments, thereby guiding material selection and structural design. Herein, we introduce the SMATool, a computational toolkit for the efficient calculation and analysis of material strength at both zero and finite temperatures for 3D, 2D, 1D, and tubular 2D-based nanostructures and nanotubes, as well as 1D nanoribbons. The toolkit is capable of calculating tensile, shear, ultimate, yield, and indentation (Vickers' hardness) strengths in various dimensions, as well as the energy storage capacity. We conducted several calculations both at zero and finite temperatures to validate the accuracy and reliability of the developed software. Here, the results show that the SMATool package provides accurate predictions that align with existing data on material strength. SMATool integrates seamlessly with widely used electronic structure codes like VASP and Quantum Espresso, providing a user-friendly interface catering to academic researchers and industry professionals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Western Combined Heat and Power Technical Assistance Partnership (Final Technical Report)

Since 2018, the Western Combined Heat and Power Technical Assistance Partnership (CHP TAP) has been dedicated to advancing the adoption and implementation of Combined Heat and Power. The CHP TAP has diligently worked towards its mission of fostering end-user engagement, promoting technical solutions, and enhancing energy security within the region. As we reflect on our journey, we are proud to announce the completion of the required metrics set forth at the beginning of our endeavor. From the outset, our aim has been clear: to leverage CHP technology as a catalyst for bolstering American competitiveness, utilizing local fuels, and fortifying energy resilience. Through collaborative efforts with a diverse array of stakeholders, including manufacturing, commercial, institutional, and federal facilities, and campuses, we have strived to realize this vision. In this report, we outline the achievements, activities, and impact of the Western CHP TAP, highlighting the transformative journey undertaken since 2018. From providing technical assistance and guidance to conducting educational workshops and advocating for supportive policies, our initiatives have been guided by a commitment to excellence and innovation. As we celebrate the completion of our required metrics, we reaffirm our dedication to continuing our mission, building upon our accomplishments, and shaping a sustainable energy future for the Western region. Through ongoing collaboration and engagement, we are confident that the Western CHP TAP will continue to serve as a beacon of progress and a catalyst for positive change in the years to come.

03 NATURAL GAS↗
DOE OSTI2021

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗
DOE OSTI2024

NREL Hydrogen Sensor Testing Laboratory

Relevance: Detection is recognized as a critical element for hydrogen facility safety design and supports risk mitigation. Detection methodologies will support validation of H2 behavior research. Hydrogen point sensors play a critical role for safety and process monitoring, but other methodologies can be developed. Approach: NREL Sensor Laboratory tests and verifies sensor performance for manufacturers, developers, end-users, regulatory agencies and SDOs/CDOs NREL deployment activity supports regulatory requirement verification, hydrogen behavior models, and method development for use by stakeholders. Accomplishments and Progress: NREL's R&D accomplishments have supported developers, industry, and SDOs by providing sensor performance and deployment expertise not otherwise available. Development of alternative detection strategies for hydrogen applications have been initiated. HyWAM and advanced detection methodologies deployments at H2@Scale Facilities are being implemented. Collaborations: Collaboration with government laboratories, universities, private organizations and regulatory agencies has leveraged the NREL Sensor Laboratory's success in advancing hydrogen safety sensors and process control. Proposed Future Work: NREL will support hydrogen deployment by the proper implementation of hydrogen sensors and advanced detection strategies. NREL will continue to support science-based codes and standards. This effort will be guided by the needs of the hydrogen community.

codes and standards↗
DOE OSTI2024

Workflow for Developing and Operating Subsurface Hydrogen Storage Facilities in Porous Reservoirs

Long-duration (seasonal) storage of natural gas (NG), which primarily consists of methane (CH 4 ), has been practiced for more than a hundred years at underground gas storage (UGS) facilities that use depleted hydrocarbon reservoirs, saline aquifers, and salt caverns. To enable hydrogen (H 2 ) to be used as a long-duration, energy-storage medium, similar facilities are envisioned for underground H 2 storage (UHS) of either H 2 or H 2 /NG mixtures. Experience with UGS can be used to guide recommended practices for developing and operating UHS facilities in porous reservoirs. The most important factors (formation/fluid properties and engineering choices) that influence the performance of UHS reservoirs have been identified and quantified in previous studies. These factors and choices influence phenomena that determine the sweep efficiency of the stored working gas. These phenomena include viscous fingering, hysteretic capillary trapping, and gravity override of the working gas, as well as the upconing of nonproductive fluid that determine the sweep efficiency of the stored working gas. This report describes initial recommended-practices and a project-development workflow for UHS facilities that utilize porous reservoirs, based on the current state-of-knowledge about H 2 behavior in the subsurface. The workflow sequentially addresses all aspects of UHS project development, including the identification of H 2 sources and users, site ranking and down-selection, geologic and reservoir-engineering characterization, reservoir design, testing, risk management, commissioning, operations, and monitoring for a UHS facility. The goal is to enable UHS facilities to be developed in an efficient and timely manner, while carefully managing project risks. This workflow is similar to that which has been developed for UGS facilities (see Figure 1 of API, 2022), with the addition of tasks and subtasks specific to H 2 and UHS. The project-development workflow is broken down into three major stages: (1) define the H 2 use case; (2) rank, down-select, and characterize potential, candidate UHS sites; and (3) reservoir design, integrity testing, risk assessment, commissioning, operations, and monitoring for selected UHS sites. Each major stage is further broken down into tasks and subtasks, which are described at a high level. This report also provides more detailed descriptions of all tasks and subtasks that involve reservoir analysis and testing.

08 HYDROGEN↗
DOE OSTI2026

Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics

Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high-dimensional print parameter space makes tuning print parameters time-intensive. We present a Bayesian optimization study that constructs a digital twin from printed-silver data to benchmark surrogate models, acquisition functions, and batch sizes head-to-head to achieve user-specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Altogether, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.

Bevel, Colton [Auburn University, AL (United State↗
DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Beamline K11 DIAD: a new instrument for dual imaging and diffraction at Diamond Light Source

The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source is a new dual-beam instrument for full-field imaging/tomography and powder diffraction. This instrument provides the user community with the capability to dynamically image 2D and 3D complex structures and perform phase identification and/or strain mapping using micro-diffraction. The aim is to enable in situ and in operando experiments that require spatially correlated results from both techniques, by providing measurements from the same specimen location quasi-simultaneously. Using an unusual optical layout, DIAD has two independent beams originating from one source that operate in the medium energy range (7–38 keV) and are combined at one sample position. Here, either radiography or tomography can be performed using monochromatic or pink beam, with a 1.4 mm × 1.2 mm field of view and a feature resolution of 1.2 µm. Micro-diffraction is possible with a variable beam size between 13 µm × 4 µm and 50 µm × 50 µm. One key functionality of the beamline is image-guided diffraction, a setup in which the micro-diffraction beam can be scanned over the complete area of the imaging field-of-view. This moving beam setup enables the collection of location-specific information about the phase composition and/or strains at any given position within the image/tomography field of view. The dual beam design allows fast switching between imaging and diffraction mode without the need of complicated and time-consuming mode switches. Real-time selection of areas of interest for diffraction measurements as well as the simultaneous collection of both imaging and diffraction data of (irreversible) in situ and in operando experiments are possible.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗