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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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DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The spatial and temporal evolution of the Portland and Tualatin forearc basins, Oregon, USA

The Portland and Tualatin basins are part of the Salish-Puget-Willamette Lowland, a 900-km-long, forearc depression lying between the volcanic arc and the Coast Ranges of the Cascadia convergent margin. Such inland seaways are characteristic of warm, young slab subduction. We analyzed the basins to better understand their evolution and relation to Coast Range history and to provide an improved tectonic framework for the Portland metropolitan area. We model three key horizons in the basins: (1) the top of the Columbia River Basalt Group (CRBG), (2) the bottom of the CRBG, and (3) the top of Eocene basement. Isochore maps constrain basin depocenters during (1) Pleistocene to mid-Miocene time (0–15 Ma), (2) CRBG (15.5–16.5 Ma), and (3) early Miocene to late Eocene (ca. 17–35 Ma) time. Results show that the Portland and Tualatin basins have distinct mid-Miocene to Quaternary depocenters but were one continuous basin from the Eocene until mid-Miocene time. A NW-striking gravity low coincident with the NW-striking, fault-bounded Portland Hills anticline is interpreted as an older graben coincident with observed thickening of CRBG flows and underlying sedimentary rocks. Neogene transpression in the forearc structurally inverted the Sylvan-Oatfield and Portland Hills normal faults as high-angle dextral-reverse faults, separating the Portland and Tualatin basins. An eastward shift of the forearc basin depocenter and ten-fold decrease in accommodation space provide temporal constraints on the emergence of the Coast Range to the west. Clockwise rotation and northward transport of the forearc is deforming the basins and producing local earthquakes beneath the metropolitan area.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Advancing Geophysical Techniques to Image a Stratigraphic Hydrothermal Resource

Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this resource and analogous ones. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data products, in conjunction with new and preexisting geoscientific measurements and observations, are being integrated and evaluated for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings, and whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs, will also be evaluated.

Geothermal, Sedimentary Heat, Geophysics, Seismic,↗

DOE OSTI2023

Complex concentrated alloys and chemical short-range ordering

Abstract Complex concentrated alloys (CCAs) are materials comprising three or more elements in similar proportions and possessing structural but no chemical long-range order. Fascination with CCAs has grown over the last 20 years and to date, CCAs have opened a new materials design paradigm and horizon for discovery of materials to meet the demands of applications in aggressive environments. Understanding the fundamental mechanisms controlling their response, however, is challenging due to the chemical and structural variations that wildly fluctuate over fine atomic and nanoscales. This issue focuses on the experimental, computational, and theoretical investigations that aim to uncover phenomena and processes determining the structure, kinetics, mechanics, or deformation mechanisms in CCAs at the atomic scale. At the atomic scale at which they operate, chemical short-range ordering can be influential. This issue further addresses the capabilities, as well as the debatable need, to characterize, predict, and relate short-range ordering to material performance. Collectively, the articles in this issue highlight the insights, understanding, and experimental and computational tools that attempt to create property-tunable CCAs “from the atom up” by treating short-range ordering and engineering atomic-scale mechanisms. Graphical abstract

36 MATERIALS SCIENCE↗

DOE OSTI2022

Frictional properties of Opalinus Clay: influence of humidity, normal stress and grain size on frictional stability

SUMMARY The Opalinus Clay (OPA) is a clay-rich formation considered as a potential host rock for radioactive waste repositories and as a caprock for carbon storage in Switzerland. Its very low permeability (10−19 to 10−21 m2) makes it a potential sealing horizon, however the presence of faults that may be activated during the lifetime of a repository project can compromise the long-term hydrological confinement, and lead to mechanical instability. Here, we have performed laboratory experiments to test the effect of relative humidity (RH), grain size (g.s.) and normal stress on rate-and-state frictional properties and stability of fault laboratory analogues corresponding to powders of OPA shaly facies. The sifted host rock powders at different grain size fractions (<63 μm and 63 < g.s. < 125 μm), at room (∼25 per cent) and 100 per cent humidity, were slid in double-direct shear configuration, under different normal stresses (5–70 MPa). We observe that peak friction, μpeak and steady-state friction, μss, depend on water vapour content and applied normal stress. Increasing relative humidity from ∼25 per cent RH (room humidity) to 100 per cent RH causes a decrease of frictional coefficient from 0.41 to 0.35. The analysis of velocity-steps in the light of rate-and-state friction framework shows that the stability parameter (a–b) is always positive (velocity-strengthening), and it increases with increasing sliding velocity and humidity. The dependence of (a–b) on slip rate is lost as normal stress increases, for each humidity condition. By monitoring the variations of the layer thickness during the velocity steps, we observe that dilation (Δh) is directly proportional to the sliding velocity, decreases with normal stress and is unaffected by humidity. Microstructural analysis shows that most of the deformation is accommodated within B-shear zones, and the increase of normal stress (σn) promotes the transition from strain localization and grain size reduction to distributed deformation on a well-developed phyllosilicate network. These results suggest that: (1) the progressive loss of velocity dependence of frictional stability parameter (a–b) at σn > 35 MPa is dictated by a transition from localized to distributed deformation and (2) water vapour content does not affect the deformation mechanisms and dilation, whereas it decreases steady-state friction (μss), and enhances fault stability.

58 GEOSCIENCES↗

DOE OSTI2022

Hydropower operation in future power grid with various renewable power integration

Hydropower generation may play an increasingly important role in the power grid under increasing contribution of variable renewable sources such as wind and solar. An improved understanding of the changes to hydropower dispatch under future higher VRE grid conditions reveals research gap that should be informed power grid planning and reservoir water releases policies considering multiple other water uses and varying hydrologic condition. Here this study aims to understand the role of hydropower in a changing power grid by employing a production cost model, PLEXOS, across future power system scenarios, planning horizons, and regions. We explore optimized hydropower dispatch to understand its potential role in minimizing the system cost and renewable curtailment. We also examine the sensitivity of hydropower revenue under various grid scenarios of the Eastern U.S. and hydrology conditions. Results indicate hydropower generation follows net load and compensates for the variability of solar and wind generation. Although energy prices are lower during some periods in the future grid scenarios, there is a potential for higher revenue for hydropower by providing both energy and ancillary services during times of stress. Additionally, hydropower revenue is sensitive to hydrology in the SERC region, which we considered as an example. The feasibility of hydropower dispatching with higher ramps between low and high hourly-capacity factors, as indicated in the optimization model, requires further study to consider other water use and ecology constraints.

13 HYDRO ENERGY↗

DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗