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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Domain-Decomposed A-ϕ Formulation Based on Lagrange Multipliers for Low-Frequency Problems

A domain-decomposed A-ϕ formulation based on Lagrange multipliers is proposed to simulate low-frequency elec- tromagnetic problems. This method partitions the computational domain into smaller subdomains, allowing each subdomain to be independently formulated using Lagrange multipliers as Dirichlet boundary conditions, while ensuring continuity of the fields across the interfaces. A mixed finite element method, utilizing both vector and scalar basis functions, is employed to discretize the formulation, resulting in a global system to be solved. The proposed method is validated using TEAM Problem 7 at 50 Hz, demonstrating its effectiveness in handling complex geometries and addressing the low-frequency breakdown issues commonly encountered in traditional finite element methods.

Hossain, Amzad↗
DOE OSTI2020

Rapid assessment of structural and compositional changes during early stages of zirconium alloy oxidation

A multimodal chemical imaging approach has been developed and applied to detail the dynamic, atomic-scale changes associated with oxidation of a zirconium alloy (Zircaloy-4). Scanning transmission electron microscopy, a gas-phase reactor chamber attached to an atom probe tomography instrument, and synchrotron-based X-ray absorption near-edge spectroscopy were employed to reveal morphology, composition, crystal, and electronic structure changes that occur during initial stages of oxidation at 300 °C. Oxidation was carried out in 10 mbar O 2 gas for short exposure times of 1 and 5 min. A multilayered oxide film with a cubic ZrO adjacent to the oxide/metal interface, a nanoscopic transition region with a graded composition of ZrO 2-x (where 0 < x < 1), and tetragonal ZrO 2 in the outermost oxide were formed. Partitioning of the major alloying element (tin) to the oxide/metal interface and heterogeneously within the oxide accompanied the development of the layered oxide. Our work provides a rapid, high-throughput approach for detailed characterisation of initial stages of zirconium alloy oxidation at an accelerated time scale, with implications for several other alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Force Balance Model Assessment for Mechanistic Prediction of Sliding Bubble Velocity in Vertical Subcooled Boiling Flow

The bubble sliding after departing from nucleation site is frequently observed in flow boiling systems and, the crucial impact on wall heat transfer has been evidenced through many experiments. As a result, the heat transfer modeling associated with sliding bubble has become one of the subjects of great attention in CFD boiling heat transfer community. The modeling efforts are primarily aimed at improving the existing Heat Flux Partitioning (HFP) model via the implementation of sliding bubble-induced heat transfer. The performance of HFP model depends inherently on the fidelity of sub-models used to predict the fundamental bubble parameters (e.g., bubble departure/lift-off diameter). In the same context, the accurate prediction of sliding bubble parameters (e.g., sliding bubble growth, sliding bubble velocity) is essential to achieving the successful heat transfer modeling associated with sliding bubble. Of many sliding bubble parameters, this paper deals with the sliding bubble velocity. Specifically, the force balance model was assessed in view of the sliding bubble velocity prediction. The parametric effect of key sub-models (e.g., drag force, bubble growth models) used in the force balance equation was investigated. The experimental data from Maity (2000) and Yoo et al. (2016) were used for demonstrating the model predictive performance. It was found that the force balance model proposed in this study was able to predict well the bubble sliding velocity based on the accurate prediction of bubble growth during sliding. The predictive performance was proven with the experimental data measured under various subcooled boiling conditions of both water and refrigerant (NOVEC-7000) flowing upward in vertical channels.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Representing lateral groundwater flow from land to river in Earth system models

Lateral groundwater flow (LGF) is an important hydrologic process in controlling water table dynamics. Due to the relatively coarse spatial resolutions of land surface models, the representation of this process is often overlooked or overly simplified. In this study, we developed a hillslope-based lateral groundwater flow model. Specifically, we first developed a hillslope definition model based on an existing watershed delineation model to represent the subgrid spatial variability in topography. Building upon this hillslope definition, we then developed a physical-based lateral groundwater flow using Darcy’s equation. This model explicitly considers the relationships between the groundwater table along the hillslope and the river water table levels. We coupled this intra-grid model to the land component (E3SM Land Model: ELM) and river component (MOdel for Scale Adaptive River Transport: MOSART) of the Energy Exascale Earth System Model (E3SM). We tested both the hillslope definition model and the lateral groundwater flow model and performed sensitivity experiments using different configurations. Simulations for a single grid cell at 0.5°×0.5° within the Amazon basin show that the definition of hillslope is the key to modeling lateral flow processes and the runoff partition between surface and subsurface can be dramatically changed using the hillslope approach. Although our method provides a pathway to improve the lateral flow process, future improvements are needed to better capture the subgrid structure to account for the spatial variability in hillslopes within the simulated grid of land surface models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Representations of Longwave Surface Emissivity to Reduce Surface and Atmospheric Heating Biases in Earth System Models

Abstract Many Earth system models (ESMs) approximate surface emissivity as a broadband constant. This approximation reduces the computational burden, yet omits the spectral structure of emissivity and atmospheric absorption. Neglecting spectral variation in surface emission introduces biases in longwave (LW) atmospheric fluxes and heating. Biases are strongest over surfaces with strongly varying emissivity and minimal atmospheric opacity. We examine these biases over water, ice, and snow surfaces. We partition spectral emissivity into the 16 spectral bands utilized by a single‐column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs. We quantify flux and heating biases introduced by broadband assumptions relative to the spectrally resolved case for standard atmospheric profiles over each surface type. Current assumptions tend to overestimate upwelling surface fluxes; for example, the greybody assumption overestimates flux by 1.6 W/m 2 (0.52%) at the bottom of a mid‐latitude winter atmosphere over ice, and by 2.33 (1.0%) at the top of atmosphere. The blackbody assumption tends to artificially cool Earth's surface, stabilizing the lower troposphere. Interestingly, the optimal broadband emissivity can deviate from the Planck‐weighted mean by up to 3% depending on surface type and atmospheric profile. We investigate bias sensitivity to surface temperature, cloud water path, and atmospheric water vapor. Bias is most sensitive to water vapor content, and least sensitive to cloud water path. Lastly, we show that a modified greybody method with updated broadband values can reduce total surface flux bias up to 1.69 , comparable to a five‐band approach and at a fraction of the computational cost.

Manzo, L. [Department of Earth System Science Univ↗
DOE OSTI2022

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

Abstract Background The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs. Results Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload. Discussion Optimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality

Abstract. This paper describes the University of New Hampshire Water Balance Model, WBM, a process-based gridded global hydrologic model that simulates the land surface components of the global water cycle and includes water extraction for use in agriculture and domestic sectors. The WBM was first published in 1989; here, we describe the first fully open-source WBM version (v.1.0.0). Earlier descriptions of WBM methods provide the foundation for the most recent model version that is detailed here. We present an overview of the model functionality, utility, and evaluation of simulated global river discharge and irrigation water use. This new version adds a novel suite of water source tracking modules that enable the analysis of flow-path histories on water supply. A key feature of WBM v.1.0.0 is the ability to identify the partitioning of sources for each stock or flux within the model. Three different categories of tracking are available: (1) primary inputs of water to the surface of the terrestrial hydrologic cycle (liquid precipitation, snowmelt, glacier melt, and unsustainable groundwater); (2) water that has been extracted for human use and returned to the terrestrial hydrologic system; and (3) runoff originating from user-defined spatial land units. Such component tracking provides a more fully transparent model in that users can identify the underlying mechanisms generating the simulated behavior. We find that WBM v.1.0.0 simulates global river discharge and irrigation water withdrawals well, even with default parameter settings, and for the first time, we are able to show how the simulation arrives at these fluxes by using the novel tracking functions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying Message Aggregation Optimisations for Energy Savings in PGAS Models

Upon breaking past the exascale barrier, HPC systems are facing their greatest challenge yet - a power wall that must be addressed through new methods in both hardware and software. While energy costs are becoming a major issue at all levels, of particular concern is that of the network, as the relative cost of moving data is increasing faster than ever. The partitioned global address space (PGAS) model is critical within certain HPC domains, but is known to suffer from the small message problem, where irregular many-to-many access patterns result in congesting the network with excessive numbers of small messages. To address this, the conveyor aggregation library was developed to defer individual messages and group them for subsequent bulk processing. In this paper, we investigate its impact on energy use related to the network, with a focus on the Slingshot 11 interconnect. We will demonstrate that this strategy is not only highly performant, but also crucial to reducing energy footprints to remain within target power envelopes.

Welch, Aaron [ORNL]↗
DOE OSTI2020

Integrative Circuit-Host Modeling of a Genetic Switch in Varying Environments

Synthetic biology is advancing into a new phase where real-world applications are emphasized. There is hence an urgent need for mathematical modeling that can quantitatively describe the behaviors of genetic devices in natural, fluctuating environments. We utilize an integrative circuit-host modeling framework to examine the dynamics of a genetic switch and its host cell in varying environments. For both steady-state and transient cases, we find increasing nutrient reduces the bistability region of the phase space and eventually drives the switch from bistability to monostability. In response, cellular growth and proteome partitioning experience the same transition. Antibiotic perturbations cause the similar circuit and host responses as nutrient variations. However, one difference is the trend of growth rate, which augments with nutrient but declines with antibiotic levels. The framework provides a mechanistic scheme to account for both the dynamic and static characteristics of the circuit-host system upon environmental perturbations, underscoring the intimacy of gene circuits and their hosts and elucidating the complexity of circuit behaviors arising from environmental variations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Progress Toward Simulating Departure from Nucleate Boiling at High-Pressure Applications with Selected Wall Boiling Closures

Recently, a Eulerian-based two-fluid computational fluid dynamics (CFD) framework with a wall heat flux partitioning approach has been intensively investigated for departure from nucleate boiling (DNB) simulation under the U.S. Department of Energy–funded Consortium for Advanced Simulation of Light Water Reactors (CASL) program. Understanding of the DNB characteristics over a range of pressurized water reactor–like operating conditions and accurate prediction of boiling crisis in the nuclear power system have been grand challenges because of the large impact of DNB on reactor safety and operational economics. The ultimate goal of this task in the CASL program is to introduce a robust multiphase CFD–based DNB modeling framework that is capable of characterizing an entire boiling history in which the wall boiling mode experiences the following through multiple stages of heat transfer mode: (1) single-phase convective heat transfer, (2) nucleate boiling heat transfer, and (3) identification of the departure of nucleate boiling. To validate the CASL boiling model, we have benchmarked simulated DNB over three different flow channel configurations (pipe flow, 5 × 5 fuel bundle with mixing vane tests, and 5 × 5 fuel bundle without mixing vane tests) against experimental measurements, and the validation result with open literature is reported. The DNB detection criteria in the simulation are checked by monitoring the peak wall temperature, wall dryout factor, and net energy balance. In addition to the DNB performance test, some preliminary sensitivity results on closure model selection are reported to address the prediction capability of local void profile against measurements. The boiling simulation tested in this study exhibits a maximum deviation of 24% from the measured DNB value in a high-pressure (i.e., 138 bars) subcooled pipe flow test. The ranges of operating conditions are as follows: 1650 to 2650 kg/m 2 ·s for mass flux and 8.5 to 96 K for subcooled inlet temperature. The deviation is even reduced to 7% when the subcooled temperature is less than 40 K. Besides accuracy, base practice guidelines for DNB detection criteria are tested by monitoring three simulation variables: (1) maximum wall temperature, (2) wall dryout factor (i.e., K-value), and (3) energy balance. Numerical robustness of DNB simulation is largely achieved in most of the validation test except for a few high subcooled test cases.

42 ENGINEERING↗
DOE OSTI2025

Characterization of Li in the Salton Sea Geothermal Field

Abstract The behavior of lithium during geothermal brine and host-rock interactions in the Salton Sea geothermal field is underconstrained. The lithium brine reservoir inventory is between 4 and 18 million metric tons of lithium carbonate equivalent, with an even larger amount present within the reservoir rock mineral phases. Here, we present bulk-rock and brine Li concentration and δ7Li, and in situ Li concentrations of minerals from the California State 2-14 scientific drill core and commercial wells in the Salton Sea geothermal field to identify the mineral hosts of Li and constrain Li behavior during brine-rock interactions. Lithium contents are highest in chlorite (270–580 ppm, ~2,358 m), which encases pyrite, indicating that Li is fixed from the brine into the host rocks during hydrothermal alteration. Lithium abundances in chlorite decrease with depth (70–100 ppm, ~2,882 m), as does whole-rock Li content, whereas whole-rock δ7Li increases (δ7Li = 2.0–4.3‰, ~2,485-m depth; δ7Li = 4.3–7.9‰ from ~2,819 to ~2,882 m). This change in behavior of Li at ~2,500 m suggests temperature dependent partitioning of Li in chlorite; Li becomes more incompatible in chlorite at depths >~2,500 m, corresponding to ~325°C in the reservoir. The brines have δ7Li = 3.7 to 4.7‰ and calculated isotopic fractionation factors between the brine and the host rock agree with a change in Li behavior at ~325°C. Simple closed-system batch modeling does not describe the geothermal system, suggesting open-system behavior of Li within the Salton Sea geothermal field.

Humphreys, J↗
DOE OSTI2026

Cave-based chronology of valley incision in the glaciated Northern Carpathians

While glaciers leave a mark on mountain landscapes, the importance of glacial erosion in mountains at a global scale remains insufficiently constrained despite over a century of research aimed at quantifying the rates and magnitudes of such erosion. Measuring rates of landscape change across the Quaternary in repeatedly glaciated mountain landscapes requires the ability to accurately partition the relative role of glacial versus fluvial processes in forming relief; however, the modification of landforms by surface erosion hinders quantification of process rates over the last few million years. This study presents 32 paired 26 Al and 10 Be cosmogenic nuclide burial ages from allochthonous, quartz-bearing sediment deposited within a paleophreatic karst system in the northern Carpathians. These data quantify Pliocene-to-present timing of valley incision and karst system reactivation, as well as erosion rates over time. Post-burial, muon production-correction yields 24 fully resolved ages, six minimum ages, and two unresolvable ages. Burial ages are interpreted to document the tempo of valley incision via phreatic passage formation and reveal that ∼80% of current valley relief (∼700 m) had developed by approximately 1.6 Ma. Paleoerosion rates derived from this dataset suggest little change in the processes generating sediment washed into the karst networks since the Pliocene. These results suggest that river incision dominated relief creation in Carpathian valleys that bear the imprint of glaciation. Our findings suggest the importance of investigating other mid-latitude mountainous regions worldwide to determine if they have similar erosion and topographic histories.

Carpathians↗
DOE OSTI2021

Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs

This paper proposes a novel model-free/data-driven centralized training and decentralized execution multi-agent deep reinforcement learning (MADRL) framework for distribution system voltage control with high penetration of PVs. The proposed MADRL can coordinate both the real and reactive power control of PVs with existing static var compensators and battery storage systems. Unlike the existing DRL-based voltage control methods, our proposed method does not rely on a system model during both the training and execution stages. This is achieved by developing a new interaction scheme between the surrogate modeling of the original system and the multi-agent soft actor critic (MASAC) MADRL algorithm. In particular, the sparse pseudo-Gaussian process with a few-shots of measurements is utilized to construct the surrogate model of the original environment, i.e., power flow model. This is a data-driven process and no model parameters are needed. Furthermore, the MASAC enabled MADRL allows to achieve better scalability by dividing the original system into different voltage control regions with the aid of real and reactive power sensitivities to voltage, where each region is treated as an agent. This also serves as the foundation for the centralized training and decentralized execution, thus significantly reducing the communication requirements as only local measurements are required for control. Comparative results with other alternatives on the IEEE 123-nodes and 342-nodes systems demonstrate the superiority of the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Systems and methods for modeling water quality

A system, method, device and computer-readable medium for creating an ensemble model of water quality. The ensemble model is generated by determining a set of optimal component models for spectral regions of a body of water, and combining the optimal models. The optimal models can be based on remote sensing data, including satellite imagery. A K-fold partition approach or a global approach can be used to determine the optimal component models, and the optimal component models can be combined through spectral space partition rules to generate an ensemble model of water quality. The ensemble model not only has improved water quality prediction ability, but also has strong spatial and temporal extensibility. The spatial and temporal extensibility of the ensemble model is fundamentally important and desirable for long-term and large-scale remote sensing monitoring and assessment of water quality.

Liu, Hongxing↗
DOE OSTI2022

A Multistage Stochastic Transmission Expansion Algorithm for Wide-Area Planning under Uncertainty

The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Regional patterns in hydrologic response, a new three-component metric for hydrograph analysis and implications for ecohydrology, Northwest Volcanic Aquifer Study Area, USA

Spatial patterns of hydrologic response were examined for the Northwest Volcanic Aquifer Study Area (NVASA). The utility of established hydrograph-separation methods for assessing hydrologic response in permeable volcanic terranes was assessed and a new three-component metric for hydrograph analysis was developed. The new metric, which partitions streamflow into subcomponents defined by the timescales of hydrologic response (e.g., fast-runoff, intermediate-interflow and slow-baseflow), was used to gain a fundamental understanding of the regional hydrology, investigate sub-regional differences, influencing factors, and ecohydrological implications. The combined effects of NVASA’s physiography, climate and geology create a strongly coupled surface-groundwater system that produces copious baseflow and limited quantities of runoff and interflow. Patterns of hydrologic response are influenced by the type and rate of precipitation and permeability of the underlying geology. Under variable precipitation conditions the hydrologic response of volcanic terranes with similar permeability and subsurface-storage capacity can be significantly different. From a water management and ecohydrology perspective, understanding regional patterns of hydrologic response and sub-regional differences is fundamental. Results indicate that minimum-flow methods provide the most conservative estimate of baseflow and may be the most robust for filtering out snowmelt bias in baseflow estimates. Baseflow contributes ~75% of the perennial streamflow across the NVASA and represents a critical component of the regional water supply that provides critical cold-water habitat.

54 ENVIRONMENTAL SCIENCES↗