Cost-optimal wave-powered persistent oceanographic observation
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Dillon, Trent (ORCID:0000000201036449)↗
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Results for “Spatial Generative Model”
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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A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
We employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equations (PDEs). We focus on steady-state solutions of coupled hydromechanical processes in heterogeneous porous media and present the parameterization of the spatially heterogeneous coefficients, which is exceedingly difficult using standard reduced-order modeling techniques. We show that our framework provides a speed-up of at least 2,000 times compared to a finite-element solver and achieves a relative root-mean-square error (r.m.s.e.) of less than 2% for forward modeling. For inverse modeling, the framework estimates the heterogeneous coefficients, given an input of pressure and/or displacement fields, with a relative r.m.s.e. of less than 7%, even for cases where the input data are incomplete and contaminated by noise. The framework also provides a speed-up of 120,000 times compared to a Gaussian prior-based inverse modeling approach while also delivering more accurate results.
97 MATHEMATICS AND COMPUTING↗
Resilience Analytics for Interdependent Power and Water Distribution Systems
The occurrence of large-scale outages in power distribution systems (PDSs) caused, for instance, by extreme weather events has raised concerns on the cascading impact of the power outages on the resilience of other critical infrastructures, including water distribution systems (WDSs), whose healthy operation highly relies on the availability of power from PDSs. Here, this paper proposes an analytics model for quantifying the interdependence between the resilience of power and water distribution systems. The proposed model first performs spatio-temporal outage analysis on the PDS and WDS to determine the PDS power serving capability and the resulting cascading impact on the WDS water serving capability during the occurrence of disruptions. The resilience interdependence is then determined by a proposed set of six metrics calculated using the power and water serving capability profiles, which trace and quantify the cascading impacts of power outages in the operation of WDSs during the degradation and recovery stages. The proposed analytics model is implemented on the IEEE 33-bus power distribution system supplying power for pumping stations of a 15-node test WDS, considering multiple cases of water storage capacity, single and multiple outage scenarios, and availability of distributed generation. The numerical results demonstrate that the proposed metrics track the temporal and spatial interdependence between the resilience of power and water distribution systems, providing a mechanism to identify weak spots, and assess options to enhance the resilience of the critical water infrastructure.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation
Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. As such, we investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs' spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs' original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.
33 ADVANCED PROPULSION SYSTEMS↗
Robust Adaptive Decentralized Dynamic State Estimation with Unknown Control Inputs using Field PMU Measurements
This paper proposes a robust adaptive decentralized dynamic state estimation method for power system with unknown inputs of the highly detailed synchronous machine model. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive model. The latter is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. Thanks to the consideration of implicit cross-correlations between the states and the unknown inputs, only generator terminal voltage and current phasors are needed. Test results on the US WECC system using the field PMU measurements show that the proposed method is able to track both the system dynamic states and unknown controller inputs. These information could significantly benefit the validation and calibration of generator controller parameters.
Zhao, Junbo↗
Uranium hydride corrosion. II Modeling spatially random growth using reaction and nucleation kinetics
A uranium hydride (UH 3 ) corrosion kinetics model has been developed, based on dimensionally restricted nucleation kinetics, which combines heretofore separate reaction and nucleation kinetics models into a single predictive construct. This theoretical framework accounts for the superficial and random pitting behaviour of UH 3 corrosion. The model can be used to generate the net corrosion surface area and volume fractions for a uranium specimen, generally accounting for its geometric form factor through the surface-to-volume ratio. Furthermore, the combined corrosion kinetics model enables the generation of quantities which may be directly compared to those obtained by non-destructive, non-contact evaluation methods.
36 MATERIALS SCIENCE↗
Spatial modeling algorithms for reactions and transport in biological cells
Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.
59 BASIC BIOLOGICAL SCIENCES↗
Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns
There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Operando Depth-Resolved Measurement of Solvation Entropy, Interfacial Transport, and Charge-Transfer Kinetics in Lithium-Ion Batteries
Understanding and improving the performance and longevity of lithium-ion batteries critically depends on insight into the dynamic processes occurring at buried electrode-electrolyte interfaces. However, direct, depth-resolved, and operando diagnosis of these interfaces remains a longstanding challenge due to their inaccessibility beneath bulk materials, the limitations of conventional surface- and bulk-sensitive characterization tools, and the difficulty of maintaining realistic cell environments during measurement. These challenges have made it nearly impossible to uniquely resolve important interfacial properties such as charge transfer resistance, SEI (solid electrolyte interphase) resistance, and solvation entropy at the individual electrode interfaces within a working cell, information that is essential for mechanistic insight and accelerated battery design. Here, in this study, we report the development of multiharmonic electro-thermal spectroscopy (METS), an operando technique that enables depth-resolved measurement of solvation entropy, interfacial transport resistance, charge-transfer resistance, and SEI resistance at individual electrode-electrolyte interfaces within practical lithium-ion batteries. By leveraging frequency-dependent, thermal-wave sensing and interface-specific modeling, METS uniquely attributes interfacial properties to specific electrodes, as validated by comparison with traditional electrochemical impedance spectroscopy (EIS). The ability to spatially and temporally resolve interfacial processes in real time provides new diagnostic capabilities that are crucial for mechanistic studies of battery degradation and for the rapid development of next-generation energy storage systems.
Chalise, Divya [University of California, Berkeley↗
Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations
Abstract This study is an early effort to generate a multi-decadal convection-permitting regional climate dataset that covers nearly the entire North American continent. We assessed a 20 year dynamically downscaled regional climate simulation at a 4 km spatial resolution with explicit convection across the contiguous United States (CONUS), Alaska, and Puerto Rico. Specifically, we evaluated the model’s performance in representing mean, 95th percentile, and extreme precipitation across regions. Our findings indicate that when compared with ERA5 reanalysis, the forcing data, convection-permitting simulation improves representations of seasonal, 95th percentile, and extreme precipitation over a large portion of the CONUS, Alaska, and Puerto Rico, particularly in areas where precipitation is heaviest. The simulation adds value over its forcing data (ERA5) in up to 53% of all grid cells in the CONUS, 68.8% in Alaska, and 84.0% in Puerto Rico. It is important to note that, however, despite improvements, model errors in Puerto Rico remain large. Similar improvements are observed in extreme indices, including consecutive dry days, maximum 5 days precipitation, and extreme precipitation. Analysis of the diurnal cycle of mean hourly precipitation suggests that representations of convective processes—including onset, dissipation, suppression, downstream propagation, and local circulation—improved overall.
54 ENVIRONMENTAL SCIENCES↗
The impact of varying inhomogeneous reionization histories on metrics of Ly α opacity
The epoch of hydrogen reionization is complete by z = 5, but its progression at higher redshifts is uncertain. Measurements of Ly α forest opacity show large scatter at z < 6, suggestive of spatial fluctuations in neutral fraction, temperature, or ionizing background, either individually or in combination. However, there are degeneracies in the impact of such fluctuations, necessitating careful modelling. We develop a framework for modelling the reionization history and associated temperature fluctuations, with the intention of incorporating ionizing background fluctuations at a later time. We generate several reionization histories using seminumerical code AMBER, and implement them in the Nyx cosmological hydrodynamics code to examine the impact on the evolution of gas within the simulation and the associated metrics of the Ly α forest opacity. We find that the pressure smoothing scale within the intergalactic medium is strongly correlated with the adiabatic index of the temperature–density relation. We find that while models with 20 000 K photoheating at reionization are better able to reproduce the shape of the observed z = 5 1D flux power spectrum than colder ones, they fail to match the highest wavenumbers. The simulated autocorrelation function and optical depth distributions are systematically low and narrow, respectively, compared to the observed values, but are in better agreement when the reionization history is longer in duration, more symmetric in its distribution of reionization redshifts, or if there are remaining neutral regions at z < 6.
79 ASTRONOMY AND ASTROPHYSICS↗
The cosmic muon and detector simulation framework of the extreme energy events (EEE) experiment
Abstract This paper describes the simulation framework of the extreme energy events (EEE) experiment. EEE is a network of cosmic muon trackers, each made of three multi-gap resistive plate chambers (MRPC), able to precisely measure the absolute muon crossing time and the muon integrated angular flux at the ground level. The response of a single MRPC and the combination of three chambers have been implemented in a GEANT4-based framework (GEMC) to study the telescope response. The detector geometry, as well as details about the surrounding materials and the location of the telescopes have been included in the simulations in order to realistically reproduce the experimental set-up of each telescope. A model based on the latest parametrization of the cosmic muon flux has been used to generate single muon events. After validating the framework by comparing simulations to selected EEE telescope data, it has been used to determine detector parameters not accessible by analysing experimental data only, such as detection efficiency, angular and spatial resolution.
Abbrescia, M.↗
Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations
Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.
58 GEOSCIENCES↗
Data-Driven Method for Groundwater-Level Mapping and Monitoring-Well Network Optimization at Hanford
This report summarizes the initial results and outcomes of a physics-informed, data-driven groundwater level (GWL) mapping capability for the Hanford Site. GWL mapping at Hanford is typically conducted annually and requires a significant amount of computational and expert resources, and it does not allow assessment of the informational value of specific monitoring wells. The proposed method produces spatially and temporally resolved fields consistent with sparse, irregularly sampled, and nonuniformly distributed well measurements. Implemented successfully, this capability will allow rapid mapping of groundwater levels and provide an opportunity to optimize monitoring activities (both location and sampling frequency) based on data information value evaluation. The approach integrates a diffusion-based generative model – trained on MODFLOW simulation data from the Plateau-to-River (P2R) model – with score-based data assimilation (SDA), allowing observation-conditioned mapping without retraining for each monitoring-network layout.
54 ENVIRONMENTAL SCIENCES↗
Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation
Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.
Ren, Yanghang [Department of Earth System Science ↗
Incorporating the Impacts of Climate Change on Hydrology in a Performance Assessment Model - 20403
The evidence of climate change is increasingly well-documented and impacts should be incorporated in performance assessment studies. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. Probabilistic modeling is a core requirement for quantifying uncertainty and evaluating its impacts. Not evaluating future climate states in a performance assessment because of the existence of uncertainty is contradictory to good modeling practices - the most uncertain issues and parameters require the most attention in effective probabilistic modeling. Excluding climate change limits development of modeling information that could aid in effective decision making. In this work we develop methods to use the output from hydrologic models and analysis of historical aerial imagery to quantify and implement the impacts of climate change on hydrologic processes at a nuclear waste site in West Valley, New York. Specifically, we used the HELP (Hydraulic Performance of Landfill Performance) model to characterize key hydrologic processes under both current and future climate conditions to assess the impacts of changing climate on hydrology. A suite of previous climatic models were reviewed and synthesized to produce a cohesive representation of the current state of knowledge of the impact of climate change on important model inputs such as precipitation. Output from the HELP simulations was coupled to the GoldSim model that was used to develop the Probabilistic Performance Assessment (PPA) approach through the application of a novel 'nearest neighbor' technique. First, several thousand realizations were generated from the HELP model using a Latin Hypercube experimental design to ensure adequate coverage of the parameter space of explanatory variables used to drive HELP. For example, porosity is a physical parameter that is used as an input to both HELP and the GoldSim PA model. We then ran sensitivity analysis (SA) algorithms on the output of HELP for each of the responses of interest. For each predictor, each time we build an SA model we get a different value for the sensitivity index (SI). From the collection of all the SA models, the average was computed among all of the SIs to represent the predictor within the context of the nearest neighbor approach. That is, we conduct SA on each HELP outcome for each scenario. This gives us parameter sensitivity indices for the outcomes. We average the parameter sensitivity indices across the outcomes to get the average SI for a scenario. For each realization that is generated from the Goldsim PA model, Goldsim generates random values for physical/empirical parameters that HELP uses as well. For each vector of physical/empirical parameters that Goldsim generates, the vector from the 5,000 HELP runs that is most 'similar' to the Goldsim vector is computed using the nearest neighbor approach. In this context 'similar' means minimization of the SA-weighted sum of the absolute differences among the 5,000 values computed for this statistic, where each value corresponds to a different HELP realization. In order to account for the impacts of climate change, this process was repeated using the spatially downscaled future climate projections. For each of the key parameters of interest, it was assumed that a linear change depicted the relationship between the values for the present day and those for 2100. In this way, the climatically-driven changes in key parameters used to inform the GoldSim model are quantified and incorporated into the PA model output for the future. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Deep-learning based artificial intelligence tool for melt pools and defect segmentation
Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.
additive manufacturing↗
Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling
Abstract Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.
54 ENVIRONMENTAL SCIENCES↗