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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2025

Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Thioester synthesis by a designed nickel enzyme models prebiotic energy conversion

The formation of carbon–carbon bonds from prebiotic precursors such as carbon dioxide represents the foundation of all primordial life processes. In extant organisms, this reaction is carried out by the carbon monoxide dehydrogenase (CODH)/acetyl coenzyme A synthase (ACS) enzyme, which performs the cornerstone reaction in the ancient Wood–Ljungdahl metabolic pathway to synthesize the key biological metabolite, acetyl-CoA. Despite its significance, a fundamental understanding of this transformation is lacking, hampering efforts to harness analogous chemistry. To address these knowledge gaps, we have designed an artificial metalloenzyme within the azurin protein scaffold as a structural, functional, and mechanistic model of ACS. We demonstrate the intermediacy of the Ni I species and requirement for ordered substrate binding in the bioorganometallic carbon–carbon bond-forming reaction from the one-carbon ACS substrates. The electronic and geometric structures of the nickel-acetyl intermediate have been characterized using time-resolved optical, electron paramagnetic resonance, and X-ray absorption spectroscopy in conjunction with quantum chemical calculations. Moreover, we demonstrate that the nickel-acetyl species is chemically competent for selective acyl transfer upon thiol addition to biosynthesize an activated thioester. Drawing an analogy to the native enzyme, a mechanism for thioester generation by this ACS model has been proposed. The fundamental insight into the enzymatic process provided by this rudimentary ACS model has implications for the evolution of primitive ACS-like proteins. Ultimately, these findings offer strategies for development of highly active catalysts for sustainable generation of liquid fuels from one-carbon substrates, with potential for broad applications across diverse fields ranging from energy storage to environmental remediation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

On the effect of scalar flux weighing of linearly anisotropic scattering matrices in few-group transport calculations

The majority of the codes available for homogenized group constant generation for deterministic transport calculations apply the approximation of scalar flux weighting during energy group condensation of higher-order anisotropic scattering matrices. In this paper, we discuss the effect of scalar flux weighting of the linearly anisotropic scattering matrices in the frame of S P{sub 3} and S{sub 12} calculations performed for a two-dimensional VVER-440 reactor benchmark. To compare group constants generated for two neutron energy groups, an infinite pin cell was homogenized with Serpent 2 and ERANOS ECCO. Serpent 2 applies scalar flux, while ERANOS ECCO performs current weighting of the linearly anisotropic scattering matrices during energy group condensation. For analyzing the effect of the various weighting options, three simple reactor models were built assuming different core sizes using standard rectangular assemblies with 15*15 fuel pins. Diffusion, SP{sub 3} and S{sub 12} calculations were performed for the 3 models using group constants generated with Serpent 2 and ERANOS ECCO. The effect of scalar flux weighting of linearly anisotropic scattering matrices is shown by comparing the decrease in reactivity due to the decreased reactor size, as well as the assembly power distribution to reference results obtained with Serpent 2 Monte Carlo calculations. Neglecting higher than linearly anisotropic scattering and indirect application of diffusion coefficients in higher-order transport calculations is advised if angular flux-moment spectra weighted higher-order scattering matrices cannot be generated. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Dynamic Building Load Control to Facilitate High Penetration of Solar Photovoltaic Generation (Final Technical Report)

Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.

14 SOLAR ENERGY↗
DOE OSTI2024

ANS Winter 2024 Summary: MCCAFE: The Monte Carlo Constructor for ATR Fuel Elements

The Irradiation Experiment Neutronics Analysis Department at Idaho National Laboratory (INL) has implemented a new analysis workflow for experiments in the Advanced Test Reactor (ATR). One key piece of this workflow is the Monte Carlo Constructor for ATR Fuel Elements, or MCCAFE. For each ATR operating cycle, the Reactor and Nuclear Safety Engineering (RNSE) Department first solves the core in eigenvalue mode and depletes the driver fuel materials. In a separate calculation, neutronics analysts model and deplete the materials of one or more irradiation experiments, usually in a series of fixed-source Monte Carlo N-Particle (MCNP) models of the ATR for neutron transport calculations. It was desirable to use the results of the former calculations to inform the models of the latter. MCCAFE is a Python program developed using American Society of Mechanical Engineers Nuclear Quality Assurance-1 procedures at INL. Its purpose is to take the calculated results from the RNSE depletion solutions and the measured or projected operating parameters from the Nuclear Data Management and Analysis System (NDMAS) to generate fixed-source models of the ATR core at given points in time across one or more cycles.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Method and apparatus for shape-based energy analysis of solids

A computer-readable medium stores instructions including storing a model of a physical structure and defining a mesh for the model. The mesh includes finite elements, each defined by a respective set of edges. The instructions include, for each finite element, identifying a governing differential equation and a set of complementary functions that exactly satisfy the governing differential equation. The instructions include determining an applied physical stimulus for the physical structure. The instructions include generating an energy optimization model that minimizes a difference between internal energy and external energy of the finite elements in response to the applied physical stimulus. The model includes a matrix of respective scalar multipliers for the complementary functions for each finite element. The instructions include transforming the matrix and calculating a physical parameter of interest. The instructions include, in response to the physical parameter not satisfying a design parameter, updating the model of the physical structure.

Spears, Robert E.↗
DOE OSTI2020

Non-standard interactions in radiative neutrino mass models

Models of radiative Majorana neutrino masses require new scalars and/or fermions to induce lepton-number-violating interactions. We show that these new particles also generate observable neutrino non-standard interactions (NSI) with matter. We classify radiative models as type-I or II, with type-I models containing at least one Standard Model (SM) particle inside the loop diagram generating neutrino mass, and type-II models having no SM particle inside the loop. While type-II radiative models do not generate NSI at tree-level, popular models which fall under the type-I category are shown, somewhat surprisingly, to generate observable NSI at tree-level, while being consistent with direct and indirect constraints from colliders, electroweak precision data and charged-lepton flavor violation (cLFV). We survey such models where neutrino masses arise at one, two and three loops. In the prototypical Zee model which generates neutrino masses via one-loop diagrams involving charged scalars, we find that diagonal NSI can be as large as (8%, 3.8%, 9.3%) for (ε$_{ee}$, ε$_{μμ}$, ε$_{ττ}$), while off-diagonal NSI can be at most (10$^{–3}$%, 0.56%, 0.34%) for (ε$_{eμ}$, ε$_{eτ}$, ε$_{μτ}$). In one-loop neutrino mass models using leptoquarks (LQs), (ε$_{μμ}$, ε$_{ττ}$) can be as large as (21.6%, 51.7%), while ε$_{ee}$ and (ε$_{eμ}$, ε$_{eτ}$, ε$_{μτ}$) can at most be 0.6%. Other two- and three-loop LQ models are found to give NSI of similar strength. The most stringent constraints on the diagonal NSI are found to come from neutrino oscillation and scattering experiments, while the off-diagonal NSI are mostly constrained by low-energy processes, such as atomic parity violation and cLFV. We also comment on the future sensitivity of these radiative models in long-baseline neutrino experiments, such as DUNE. While our analysis is focused on radiative neutrino mass models, it essentially covers all NSI possibilities with heavy mediators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Computer-aided design file format for additive manufacturing and methods of file generation

A method of generating a tessellated output file comprising receiving a computer-aided design (CAD) model file defining a CAD model including a plurality of vertices, a plurality of curves, a plurality of surfaces, and at least one volume; generating base polygon data defining the CAD model for additive manufacturing, the base polygon data including a plurality of connected polygons, each polygon including a plurality of nodes, a plurality of edges, and a face; generating data for a vertex metadata container including a listing of CAD model vertices and one polygon node associated with each CAD model vertex; generating data for a curve metadata container including a listing of CAD model curves and at least one polygon edge associated with each CAD model curve; and generating data for a surface metadata container including a listing of CAD model surfaces and at least one polygon face associated with each CAD model surface.

Vernon, Gregory John↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2020

Heat Generation versus Heat Dissipation Maps for Batteries (Battery Safety Maps) [SWR-21-27]

NREL's Battery Safety Map (Heat Generation versus Heat Dissipation Maps for Batteries) generates a response surface that is grouped into regions where the battery is deemed safe from the propagation of thermal runaway, those where an engineering solution would render the battery safe, and finally instances where thermal runaway is inevitable. The demarcation for the different regions is provided by specifications or constraints imposed by the end user. NREL's Battery Safety Map is a user-defined subroutine that sweeps through a set of parameters describing the operating conditions and short-circuit scenarios for battery safety simulations. For each run, the electrochemical-thermal model implementing reaction kinetics and gas generation are run to evaluate the amount of heat generated. A thermal model then captures the amount of heat dissipation based on boundary conditions defined (e.g., forced convection, constant temperature at the surface, etc.). Based on the rate of heat transfer and the amount of heat generated, each run is classified into "safe," "mitigation possible," or "unsafe" zones. These categories are defined based on constraints or tolerance specifications provided by the end user.

Santhanagopalan, Shriram↗
DOE OSTI2023

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Towards Improved Decision Making for the Smarter Grid: Geomagnetic Disturbance Implicit Modeling Uncertainty Quantification

Geomagnetic disturbances can greatly disrupt the power grid operation. This is caused by low-frequency changes in the earth’s magnetic field, which upon interaction with the deep earth conductivity induces a changing electric field at the earth’s surface. This in turn induces quasi-direct currents which have the potential to cause protective devices to operate, widespread damage to high-voltage transformers, or voltage collapse due to induced reactive power losses. While these impacts have been known since the 1940s, only recently have standards for GMD response been implemented. Because these standards are heavily based on models, it is important to understand when it is appropriate to make certain modeling assumptions. In this work, implicit modeling uncertainty is quantified for the purpose of enhanced decision making for disturbance impact mitigation. Considering electromagnetic models, modeling assumptions of non-uniform grounding, imbalanced operation, transformer saturation, and geomagnetically induced currents, voltage estimation values are analyzed. Statistical characteristics of modeling uncertainty are obtained by analysis of synthetically generated data. Results statistically characterize geomagnetic disturbances modeling uncertainty, providing an important tool for impact mitigation.

grid analysis↗
DOE OSTI2024

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

Ahmadikia, Behnam↗
DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions

Abstract There is interest for desalination technologies powered by solar energy as arid areas are typically bestowed with good solar potential. In response to a US DOE call for solar desalination analysis tools, we developed an open-source solar energy desalination analysis tool, sedat , for techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It is expected that this software will simplify the planning, design, and valuation of solar desalination systems in the U.S. and worldwide. Sedat uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. This paper summarizes the various phases of the tool’s development, presents example results showing the potential, under multiple objectives, of solar desalination in parts of the U.S. southwest, and discusses method details that would be useful for future model development.

14 SOLAR ENERGY↗

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