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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Automation & Control Systems↗
DOE OSTI2022

Mesoscale evaluation of AMPS using AWARE radar observations of a wind and precipitation event over the Ross Island region of Antarctica

Surface, upper-air, and radar observations are used to assess the performance of the Antarctic Mesoscale Prediction System (AMPS) in simulating the mesoscale aspects of a wind and precipitation event over the Ross Island region of Antarctica that spanned January 16–20, 2016. The observations, collected during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE), provide a unique dataset for evaluating AMPS, especially the radar observations that facilitate a three-dimensional depiction of winds and precipitation. Comparisons of AMPS forecast data with surface meteorology, balloon-sounding, and profiling radar observations at and above sites near McMurdo Station reveal a mixture of similarities and differences. A generally southerly flow is evident at low levels in both the AMPS simulations and observed Doppler radial velocities. AMPS winds are comparable to those observed at the surface and aloft in terms of magnitude, direction, and timing but the strongest simulated southerly flow is displaced eastward relative to the observations. AMPS-simulated reflectivity over the broader Ross Island region is more limited in areal extent and smaller in magnitude than observed by a scanning Doppler radar. Three episodes of surface precipitation are observed near McMurdo Station over the five-day event with peak rates of ~3 mm h -1 and a total accumulation of ~22 mm. However, AMPS produces no surface precipitation at that location over the five-day event due to a low-level dry bias in the forecasts. Herein, the results show the first observationally based three-dimensional understanding of meteorology in the Ross Island region.

AMPS↗
DOE OSTI2020

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

17 WIND ENERGY↗
DOE OSTI2026

Mesoscale Organization in Cumulus-Coupled Stratocumulus

Marine cloud systems cover a substantial portion of the world’s oceans. Most of these clouds form relatively close to the ocean surface, typically within one to two kilometers, a region referred to by meteorologists as the marine boundary layer. They are composed predominantly of liquid water, although ice particles can occur in mid- and high-latitude marine clouds during winter. In satellite imagery, these clouds appear bright against the darker ocean surface below, reflecting a large fraction of incoming sunlight back into space that would otherwise warm the ocean. Because marine boundary layer clouds cover such an extensive area of the ocean, they exert a significant influence on Earth’s overall transfer of solar energy absorbed by the surface and thermal energy emitted to space, a balance known as the planetary radiation budget. Marine boundary layer clouds are typically thin, and their formation and dissipation depend on a delicate balance between processes acting at the ocean surface below and the warm, dry air above. They are notoriously difficult to simulate accurately in weather forecast models, which often produce too few marine low clouds in midlatitudes and clouds in tropical regions that are excessively bright, meaning they reflect too much solar radiation. The marine boundary layer is frequently characterized by widespread overcast cloud cover that often transitions from a continuous, single-layer deck to more broken cloud fields toward the tropics. These transitions typically proceed through an intermediate stage in which shallow, broken clouds form beneath the overlying stratiform cloud deck. Once broken clouds develop below the overcast, they frequently self-organize into cloud clusters known as marine boundary layer convective complexes (MBLCCs), although the mechanisms governing the formation and organization of MBLCCs remain poorly understood. Accurately representing these transitions in long-range weather forecast models is essential because they influence the properties of air masses advected over the continental United States and Europe, and they become increasingly important for forecasts on seasonal and longer timescales. We employed two complementary approaches to investigate the processes controlling MBLCCs and their impact on marine cloud cover. Long-term observations from the U.S. Department of Energy’s Eastern North Atlantic (ENA) Observatory provided a unique dataset that allowed us to characterize fundamental properties of MBLCCs, including their typical size and frequency of occurrence. These observations were combined with high-resolution numerical simulations performed on supercomputers to examine the evolution of MBLCCs during cold-air outbreaks over the ENA region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Feasibility and Prospects of the High Resolution Rapid Refresh Model For Dynamic Line Rating

The ampacity of transmission lines is defined as the maximum amount of current the conductor can safely carry. It is necessary for transmission line operators to apply ampacity limits due to the thermal properties of the conductor. Dynamic Line Rating (DLR) is a technology and technique that uses the environmental conditions or a set of the conditions to calculate the ampacity of the conductor. The way the DLR is calculated has depended upon some amount of physical technology to implement the solution. The High Resolution Rapid Refresh (HRRR) Model is an atmospheric model that may provide enough resolution to perform dynamic line rating without any hardware at all. The objective is to use this model to perform Dynamic Line Rating. This will solve two of the most pervasive problems in Dynamic Line Rating, how to instrument a transmission line hundreds of miles long and then how to predict near future ampacity sufficient for transmission operators to effectively use, This technique will use the Idaho National Laboratory site as the test location for analysis. The High Resolution Rapid Refresh model is a reliable, freely available, and well-maintained weather forecasting model for the entire United States of America. The model provides enough special resolution that additional local weather monitoring devices at location are not necessary to conduct dynamic line rating. The HRRR ratings are lower on average than the locally measured ratings which allows for an additional margin of safety. The adoption of using purely digital methods for dynamic line rating could allow for wide scale adoption of the technology with very little overhead expenditure of implementation.

17 WIND ENERGY↗
DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗
DOE OSTI2026

Simulation of wind and solar energy generation over California with E3SM SCREAM regionally refined models at 3.25 km and 800 m resolutions

This study presents wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) Regionally Refined Models (RRM) over California at 3.25 km and 800 m horizontal resolutions, using the Python wrapper for the System Advisor Model (PySAM). The resulting wind and solar generation estimates are compared to monthly capacity factors reported to the Energy Information Administration (EIA), High-Resolution Rapid Refresh (HRRR, 3 km resolution) forecast model, and E3SM North American regionally refined model (NARRM, 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM performs better than HRRR for solar power generation in single- and dual-axis tracking systems, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of uncertainties in generation modeling.

Geosciences↗
DOE OSTI2020

A Study on Modeled Wind Speed Errors Using the U.S. Department of Energy Buoys

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies Office. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of wind energy in the U.S. waters. The first deployment for one buoy was off the coast of Virginia from December 2014 to May 2016, and the first deployment for the other buoy was off the coast of New Jersey from November 2015 until February 2017. This report describes recent analysis of data collected during these first two deployments. Specifically, we compare hub-height wind speed estimates using Monin-Obukhov Similarity Theory (MOST) to the lidar measurements, and examine how those errors are affected by wind direction, atmospheric stability, wind-wave direction differences, and various measures of the wave-state. The comparisons are done using standard similarity functions based on MOST; including the Businger - Dyer, the Beljaars & Holtslag and the Vickers & Mahrt similarity functions. All models produce large errors over the range of atmospheric stabilities that were observed, with the largest errors occurring for stable flows. The Vickers & Mahrt function resulted in the largest overall bias and standard deviation, while Beljaars & Holtslag function gave the smallest bias and standard deviation due to its better performance under stable conditions. The models perform best under unstable conditions, but even in this regime there is a consistent overestimation of the wind speed of between roughly 0 to 1 ms -1 compared to the lidar measurements. We identify specific metocean conditions (i.e. stability and wind and wave directions) at each of the deployment locations that lead to large errors in MOST predictions. Finally, a coupled ocean-atmosphere model framework was investigated to simulate large errors in weather research forecasting (WRF).

17 WIND ENERGY↗
DOE OSTI2021

Higher Throughput, Lower Cost Processing of Flexible Perovskite Solar Cells by Photonic Curing

The objectives of this SETO project are to explore whether photonic curing can be used to perform annealing in perovskite solar cell (PSC) fabrication, and if so, whether it can enable high-throughput PSC manufacturing. The successful outcome of the project will reduce manufacturing cost towards realizing the SETO goal of the Levelized cost of electricity (LCOE) $0.03/kWh by 2030. The project is a collaboration between Hsu’s group at the University of Texas at Dallas (UTD) and NovaCentrix, an Austin TX based company that pioneered the PulseForge® photonic curing tools and has expertise in applying this technology to printed electronics. Thin-film synthesis typically involves a thermal annealing step to convert from precursors to final material phases, to obtain the desired crystalline phase, or to improve materials structural or electrical properties. However, thermal annealing typically requires high temperatures that are incompatible with inexpensive plastic substrates and annealing times lasting tens-to-hundreds of minutes, making it incompatible with roll-to-roll (R2R) manufacturing at a desirable web speed. Photonic curing uses a flash Xe lamp to deliver short (20 µs to 100 ms) but high intensity (up to 50 kW/cm 2 ) pulses of broadband light (200 – 1500 nm) to the sample. It has been successfully applied to sinter printed metal nanoparticle inks into conductive patterns. This project aimed to extend the applications of photonic curing to convert halide perovskite films and transparent metal oxide transport layers. Despite the interruption by COVID-19, we have achieved significant accomplishments to establish photonic curing as a viable technology for the high-throughput manufacturing of PSCs. (1) We demonstrated that photonic curing can convert both metal oxide transport layer and halide perovskite absorber in the PSCs and successfully made flexible PSCs on Corning Willow glass® (WG)/ indium tin oxide (ITO) substrates without any thermal annealing steps. These photonically cured devices achieve performance comparable to conventional thermally annealed devices but were fabricated with a total processing time reduced by six orders of magnitude. We also show that photonic curing can achieve desirable web speed (up to 26 m/min) and large-area uniformity. (2) By establishing photonic curing processing phase spaces for the perovskite, we show that the longer pulses are more forgiving in small variation in the photonic curing conditions, making it more suitable for R2R manufacturing. (3) Our results clearly illustrated the importance of improving mechanical and optical properties of transparent electrodes on flexible substrates. (4) We also performed fundamental studies that elucidate the interactions between perovskite absorber and metal oxide transport layer that can undermine the PSC stability. Five refereed journal papers in top journals (ACS Energy Letters, npj Flexible Electronics, ACS Applied Energy Materials, Materials Advances, and Frontiers in Energy Research), one conference proceeding (PVSC-47), six conference presentations (PVSC and Materials Research Society meetings), and a Ph.D. dissertation (Trey B. Daunis) were produced under this project. Energy Materials Corporation (EMC) has forecast that with a 1.5 m wide web moving at a web speed of 30 m/min, R2R processing of PSC could provide up to 4 GW/year for a single R2R assembly line. Replacing thermal annealing with photonic curing, as demonstrated by the accomplishments of this project, is necessary to realize such an energy production goal.

14 SOLAR ENERGY↗
DOE OSTI2020

Collaborative Research: Advancing Arctic Climate Projection Capability at Seasonal to Decadal Scales (Final Technical Report)

The Regional Arctic System Model (RASM) at process resolving configurations has been used to (i) advance understanding of physical processes and feedbacks involved in Arctic amplification and (ii) understand and potentially reduce uncertainty in prediction of arctic climate change at seasonal to decadal scales. RASM consists the atmosphere (Weather and Research Forecasting model, WRF), ocean (Parallel Ocean Program, POP), sea ice (CICE), land hydrology (Variable Infiltration Capacity model, VIC), river routing scheme (RVIC), marine biogeochemistry components and the coupling framework (CPL7). Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid and four configurations of the ocean and sea ice components: 1/12°(~9.3km) or 1/48°(~2.4km) and 45 or 60 vertical layers. These RASM configurations have been motivated by the emerging exascale capability for high performance computing to improve model fidelity. The dynamical downscaling of reanalysis allows comparison of RASM results with observations in place and time to: (i) advance system level understanding of physical processes and coupling involved in an event, (ii) optimize model parameter space, (iii) diagnose and reduce model biases and (iv) produce realistic and consistent across all the components initial conditions for predictions and predictability studies, which are all unique capabilities not available in global Earth System Models (ESMs). An evaluation of RASM 1.0 (Cassano et al. 2017) revealed that it had a cold bias over the oceans and a warm bias over land areas due largely to cloud and radiation biases in the model, with too little cloud cover simulated over land and too much cloud cover simulated over sub-polar oceans. This study has motivated an upgrade to WRF version 3.7.1 in RASM and allowed for the inclusion of the radiative impact of convective clouds. A variety of atmospheric physics parameterizations were evaluated against observations (e.g. data from the Arctic Clouds in Summer Experiment (ACSE); Sedlar et al. 2020) to identify an optimal suite of WRF physics options in RASM. The RASM with the optimized WRF physics were used to study the impact of strong mesoscale winds over the ocean around the southern tip of Greenland (DuVivier and Cassano 2016) and their impact on oceanic convection (DuVivier et al. 2017a). Data from the PolarWinds field campaign were used to evaluate WRF boundary layer physics and resolution impacts on the simulation of a Greenland barrier wind event (DuVivier et al. 2017b). The RVIC streamflow routing model has been implemented in RASM to realistically represent high-resolution streamflow processes (Hamman et al. 2017) and to couple the land buoyancy fluxes to the ocean. The RASM-RVIC high-resolution data set of all coastal freshwater fluxes for the Arctic drainage basin and surrounding areas for 1979-2014 was published as a separate product (https://doi.org/10.5281/zenodo.293037). The fidelity of atmospheric momentum transfer to and the response of polar marine Ekman layer in RASM and Community Earth System Model (CESM) was investigated by Roberts et al. (2015). The increased frequency of oceanic flux exchange in CESM, following the RASM guidance, caused a considerable increase in the median inertial ice speed across the Southern Ocean and parts of the Arctic. A comprehensive evaluation of the RASM1.0 atmosphere-ocean-sea ice-land interface was completed by Brunke et al. (2018). RASM was also demonstrated for its capability to simulate extreme events in agreement with observations in space and time (Lee et al. submitted). In particular, the development of three open water events, known as polynyas, have been simulated north of Greenland in February of 2011, 2017 and 2018, in agreement with satellite observations for the past four decades. The optimized RASM sea ice results have been favorably evaluated against satellite observations and a subset of eleven CMIP6 models (Watts et al. submitted). In a complementary project, Jin et al. (2018) have shown that RASM with higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice conditions, concentrations of nutrients and ice algae, in comparison to results from the coarse-resolution (1°) CESM. In two other complementary studies, RASM results were used (i) to explain areas of concentrated use by bowhead whales, the seasonal progression in the use, and the physical environment within those areas (Citta et al. 2015) and (ii) for a synthesis of fall bowhead whales distribution and migration in the Bering-Chukchi-Beaufort (BCB) Sea to investigate whale movements and feeding to the local ocean hydrography and currents (Citta et al. 2018). However, the multi-decadal output from the CESM Large Ensemble yielded unrealistic forcing. Instead, the shorter NCEP CFSv2 9-month forecasts were successfully tested and afforded an increased ensemble size (~30) to demonstrate gains of dynamical downscaling at sub-seasonal to intra-annual time scales. The improved model physics and coupling among RASM model components have yielded more realistic representation of the sea ice cover and consistent across all model components initial conditions. Consequently, RASM demonstrates significant gains compared to simulation of sea ice in the NCEP reanalysis. In addition, RASM 6-month ensemble forecasts yield very realistic sea ice distribution, which demonstrates both significant gains of dynamical downscaling and the continued impact of the initial conditions on forecasts out to 6 months (https://nps.edu/web/rasm/predictions). A follow up study using RASM for dynamical downscaling of the more realistic CESM initialized Decadal Prediction Large Ensemble output is currently ongoing as part of the DOE RGMA HiLAT-RASM project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Automated and highly parallelized Bayesian optimization scheme for direct drive fusion experiments on OMEGA

Finding the optimal implosion design on existing experimental facilities for inertial confinement fusion requires an exhaustive search of the vast design parameter space. This is infeasible both with experiments and with simulations. Consequently, a large fraction of the experimentally realizable design space remains unexplored, and new design schemes are challenging to optimize in a reasonable time frame. On the OMEGA laser facility, predictive machine learning models have been developed to accurately forecast the result of an experiment using only inexpensive simulations and the large dataset of prior experimental data. However, the full design space remains vast enough to be unassailable with simple optimization techniques. Here we develop an automated and optimally parallel Bayesian optimization algorithm that can entirely optimize the target and pulse shape of a direct-drive ICF implosion under a given design paradigm. We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Potential Formation Damage: An Integrated Reservoir Characterization Study of the Naturally Fractured Carbonate Middle Duperow Formation at the Kevin Dome, Montana

In this study, we integrate geologic and engineering data of a naturally fractured carbonate reservoir at the Kevin Dome, Montana. Well test data are correlated with core description, geochemical and lithology study to determine the flow behavior and communication within the injection test interval and to the surrounding area. Based on a dual-continuum geologic model, numerical brine injection simulations are carried out to validate the interpretation results from our well test analytical models and forecast the probability of CO 2 injection success using current reservoir properties. As a result, our well test analytical models as well as lithology/core description suggest that fluid flow may be mainly restricted to the injection interval and the assumption of radial (horizontal) flow may be appropriate. The well test models also indicate that there is potentially formation damage with a positive skin factor although prior to brine injection well tests, well stimulation through acid treatment was performed. Our numerical simulation results appear to confirm this formation damage by showing additional pressure buildup in the injection data during later test periods. To explain this, acid may have dissolved dolomite then dolomite or calcite may have been formed again further into the matrix/fracture system. Another possible explanation is mechanical clogging of the fractures due to acid dissolving dolomite and dislodging fine grains. Our work also predicts that if no additional well stimulation is performed, the project will have a lower probability of successfully injecting 1 million tons of CO 2 into the Middle Duperow formation over 4 years.

42 ENGINEERING↗
DOE OSTI2026

Dynamical Downscaling of Earth System Model Data for Energy System Analysis

Assessing energy resources (e.g., solar, wind, and hydro) under future scenarios requires datasets with sufficient spatial and temporal detail to capture variability and extreme events. While global-scale Earth System Model (ESM) projections are widely used, their coarse resolution limits direct application to regional energy system analyses. Dynamical downscaling offers a robust approach to generate physically consistent, fine-scale datasets that better represent local atmospheric processes impacting energy resources. In this work, we present a two-stage approach for producing high-resolution historical and future projections over the contiguous United States (CONUS). First, we optimize the Weather Research and Forecasting (WRF) model configuration for energy-relevant variables - solar irradiance, wind speed, and precipitation - by conducting ERA5-driven simulations at 8-km and 28-km resolution. Multiple physics schemes and model configurations within the WRF are evaluated against observational datasets including the National Solar Radiation Database (NSRDB), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the Stage IV multi-radar/multi-sensor precipitation product for the CONUS domain. Using the best-performing configuration, we dynamically downscale MPI-ESM1-2-HR simulations for 2000-2060 under SSP2-4.5 and SSP5-8.5 scenarios at 4-km spatial and hourly temporal resolution. This presentation will provide a comprehensive analysis of the results from multiple numerical experiments and high-resolution ESM projections. In addition, we will discuss potential applications of our high-resolution datasets within the energy sector and outline future research avenues dedicated to evaluating how extreme weather events influence system performance and resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.

58 GEOSCIENCES↗
DOE OSTI2020

A High-resolution Regional Wave Resource Characterization For The U.S. West Coast

Objectives/Scope: Wave resource characterization is a critical step for wave energy converter deployment in the coastal ocean and relies on long-term, high-resolution wave datasets. This study presents a detailed modeling study of the wave resource along the U.S. West Coast (Washington, Oregon, and California), a coastal region that was identified with high wave energy potential in earlier studies. Methods, Procedures, Process: The wave hindcast covers a 32-year period from 1979 to 2010 and is based on a multi-resolution, unstructured-grid SWAN model framework. Model configuration closely follows and meets the requirements recommended by the International Electrotechnical Commission Technical Specification (IEC TS) for wave energy resource assessment and characterization (Class 2 - feasibility study). The model domain covers the entire U.S. Exclusive Economic Zone (EEZ) in the West Coast and has a spatial resolution varying from ~300 m in the nearshore region (20 km from the shoreline) to ~2500 m within the EEZ and ~5000 m at the open boundary, which extends beyond the EEZ. The model was forced by hourly 2-D wave spectra produced by a two-way nested WaveWatch III model, which covers the global ocean domain and the broader U.S. West Coast region domain with spatial resolutions of 0.5 degree and 10 arc-minutes, respectively. Both wave models are forced by hourly, 0.5-degree wind forcing obtained from NCEP’s Climate Forecast System Reanalysis (CFSR) product. Results, Observations, Conclusions: The standard model output for the SWAN model includes 3-hourly output for the six IEC wave resource parameters (e.g., omnidirectional wave power) at each grid point and hourly 2-D spectra at more than 50 NDBC buoys. Extensive model validation was achieved by comparing the six model-predicted IEC parameters with those derived from field observations at representative NDBC buoys. The error statistics indicated the model’s satisfactory performance. Further analyses were conducted to systematically evaluate the temporal and spatial distributions of wave energy potential and wave climate along the U.S. West Coast. Results suggest that Washington and Oregon coasts have similar nearshore wave resource, which is significantly higher than resources in Southern California. Strong seasonal variations are also observed, e.g., high wave energy tends to occur in the winter months. In summary, this study produced the first high-resolution, comprehensive dataset on wave energy distribution along the U.S. West Coast. Novel/Additive Information: The results are being used by the National Renewable Energy Laboratory to update the MHK Atlas, which was originally derived from NOAA’s 4-arc-minute WaveWatch III model output. In addition, the monthly averaged wave energy climatology dataset can be readily shared to support a variety of research and application efforts within the EEZ of the U.S. West Coast.

Wang, Taiping↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗