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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 OSTI2020

CRADA Final Report: Open Microgrid Platform

Current microgrid control technology is mostly proprietary, expensive, and inflexible. An open-source, vendor-neutral, publish-subscribe controller will significantly lower the barrier to entry for new technologies and market entrants: inverter, battery, and load control manufacturers; cloud and services providers; and energy aggregators. An OpenFMB controller can significantly reduce the acquisition, integration, and ongoing operating and maintenance costs and increase revenue opportunities for energy producers and consumers. It allows distributed energy resource (DER) owners to replace individual DER components as richer-function, lower-cost devices become available or to incorporate new forecast and optimization algorithms as they are developed. A standard, secure, full-function DER field controller will also provide a low-cost means for grid operators to effectively manage the variability and uncertainty of solar photovoltaics (PV). Under the terms of this CRADA, ORNL has worked with Open Energy Solutions (OES) to advance the transition of ORNL’s existing open-source microgrid controller into a platform geared toward mainstream implementation. Using a more prevalent and memory-safe programming language, Rust, OES has converted ORNL’s current generation on-grid optimization into an application more suitable for industry use. The results of the ported optimization were analyzed by inputting the same arguments in both the current-generation and next-generation optimizers and verifying that the results were equivalent.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2025

IM3 Projected U.S. Western Interconnection Grid Stress Dataset

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.

Capacity Expansion Model↗
DOE OSTI2026

Second-order wave excitation forces in WEC-Sim/MOST: Implementation, experimental validation, and code-to-code comparison

Accurate prediction of second-order hydrodynamic loads is essential for floating bodies, including floating offshore wind turbines, wave energy converters, and hybrid wind–wave platforms. These nonlinear effects, arising from both sum- and difference-frequency forcing, are critical for capturing key response characteristics but remain challenging to model efficiently. In this work, we extend the open-source Wave Energy Converter Simulator / MATLAB for Offshore Simulation Tool by implementing second-order wave excitation forces, supporting both the full Quadratic Transfer Function formulation and the Newman approximation. The full Quadratic Transfer Function method is used for all code-to-code comparisons and experimental validation, while the Newman approximation is provided as a computationally lighter alternative. To benchmark the new capability, we perform a code-to-code comparison with OpenFAST and OrcaFlex. We then validate the enhanced model using wave-tank measurements of a 1:96 scale DeepCwind semi-submersible, showing that second-order effects are required to reproduce platform motions. The implementation employs a computationally efficient pre-computation strategy for second-order wave excitation forces, reducing simulation cost while maintaining engineering accuracy. Overall, this work advances the tool as an open-source and versatile tool for modelling floating offshore renewable-energy systems requiring second-order hydrodynamic fidelity.

17 WIND ENERGY↗
DOE OSTI2020

Fluidization of Group A Glass Particles: Experiments and Preliminary Validation

This work is aimed at providing reliable high-quality data from fluidization experiments. Geldart Group A glass beads are used as bed material. The fluidizing medium is air at atmospheric pressure which enters the system at 40% relative humidity. The air inlet velocity is varied over a broad range from 2 U mf to 26 U mf , where the minimum fluidization velocity, U mf , is 0.608 cm/s. There is not a significant change in the mean values of differential pressure; however, there are noticeable trends in their standard deviation values. Preliminary validation studies are performed using the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). The results from Eulerian-Eulerian (MFiX-TFM, two-fluid model) and Eulerian-Lagrangian (MFiX-PIC, particle-in-cell) formulations are compared. The work presented in this report highlights the applicability of MFiX-PIC for large-scale applications, and reveals that PIC simulation offers a suitable alternative to TFM simulation when effects due to multiple components or polydispersity are significant. PIC modeling provides a readily extendible framework for such systems. However, PIC models do rely on an empirical closure for inter-particle stress. Some solution discrepancies were evident in MFiX-PIC predictions which require further analysis. As such, the study underlines the need for a systematic approach to quantify the sources of numerical uncertainty within the PIC model. However, even with known empiricism, MFiX-PIC has demonstrated considerable potential in analyzing gas-solid systems. The results obtained are encouraging and warrant further investigation to improve the predictive capability of MFiX-PIC.

36 MATERIALS SCIENCE↗
DOE OSTI2021

EMS and DMS Integration of the Coordinative Real-time Sub-Transmission Volt-Var Control Tool under High DER Penetration

This paper proposes an applicable approach to deploy the Coordinative Real-time Sub-Transmission Volt-Var Control Tool (CReST-VCT), and a holistic system integration framework considering both the energy management system (EMS) and distribution system management system (DMS). This provides an architectural basis and can serve as the implementation guideline of CReST-VCT and other advanced grid support tools, to co-optimize the operation benefits of DERs and assets in both transmission and distribution networks. Potential communication protocols for different physical domains of a real application is included. Performance and security issues are also discussed, along with specific considerations for field deployment. Finally, the paper presents a viable pathway for CReST-VCT and other advanced grid support tools, which could be integrated in an open-source standardized-based platform that supports distribution utilities.

Nguyen, Quan H.↗
DOE OSTI2022

Review of WEC-Sim development and applications

WEC-Sim (Wave Energy Converter Simulator) is an open-source code for simulating wave energy converters, which has been actively developed and applied to simulate a wide variety of device archetypes, and has become a popular tool since its release. This paper reviewed the development efforts and the usage of WEC-Sim. The publications considered in this study have been broken down into six topic areas, namely feature development, experimental validation, device modeling, control modeling, PTO and grid modeling, and novel applications, which even includes some non-wave energy applications. This review paper has also attempted to recognize the contributions of the broader WEC-Sim development effort, meaning not only the internal WEC-Sim development team but also the external efforts from the academia researchers and technology developers around the world. The growing trend of external applications of WEC-Sim has demonstrated the broader acceptance of the open-source code, and how WEC-Sim has been used in a certain topic area also highlights the potential future development needs.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Simple, Secure, Internet Delivery of MOOSE-based Applications

Application packaging and distribution are the final steps for delivering software to end-users; both are frequently neglected when creating scientific software. Commercial businesses rely on electronic distribution systems that have rendered disk drives obsolete. Still, national laboratories continue to rely heavily on removable media to distribute and limit access to controlled applications. With increasing concerns of unauthorized copying of sensitive applications, a modern distribution system that utilizes cryptographically secure communication and authentication protocols has been developed. This new distribution system will secure the chain of custody for nuclear software while simultaneously simplifying access to these tools. This report summarizes four primary advancements made toward the secure distribution of Nuclear Energy Advanced Modeling and Simulation (NEAMS)-developed, Multiphysics Object Oriented Simulation Environment (MOOSE)-based applications: application installation, package distribution, automated package building, and distribution of documentation. NEAMS is currently developing more than ten separate applications based on the open-source MOOSE Framework. Distribution of these applications has primarily been accomplished by distributing source code, with end-users compiling the applications themselves. This work created a mechanism where MOOSE applications can be installed in a similar way to any other software. This allows both administrators and end-users simplified access to runnable executables. With this new installation capability, it was then possible to rethink distribution. A new, secure capability for delivering MOOSE-based applications over the internet has been created. This system requires unique cryptographic tokens for authentication, greatly securing the custody chain for software. Once granted access, installation of any NEAMS code can be accomplished with these terminal commands: "conda install ncrc" "ncrc install ncrc-bison." After these two commands (and authenticating) the BISON application will be securely down- loaded from Idaho National Laboratory (INL)’s servers, installed, and ready to use. To enable this new distribution capability to be successful, the open-source Continuous Integration, Verification, Enhancement, and Testing (CIVET) Continuous Integration (CI) capability was augmented to add Continuous Delivery (CD). CD enables the automated building and packaging of MOOSE-based applications as they are modified by development teams, ensuring that our customers can obtain up-to-date versions of the software at any time. The need for instruction on how to use these applications was addressed through modifications to the MOOSE documentation system. The MooseDocs capability, which enables robust documentation of MOOSE-based applications, has been extended to allow both for the installation of documentation and the packaging of documentation with installed applications. Together, these enhancements form the core of a new, secure distribution mechanism for nuclear simulation tools. In concert with the Nuclear Computational Resource Center (NCRC), NEAMS- developed applications will now be straightforward to obtain securely.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Catching Rays: How Bifacial_Radiance Sheds Light on the Future of Solar PV

The challenge of energy transition is immediate and immense, with current projections targeting 75 TW of photovoltaics (PV) capacity globally by 2050. Alongside the rapid deployment is the "solar-coaster" ride the PV industry experiences with evolving technologies and novel installation methods. In 2016, NREL developed bifacial_radiance, a python open-source modeling tool for bifacial PV. This tool is a wrapper of the raytracing engine Radiance, which you all know better than us at this workshop. Bifacial_radiance integrates the many characteristics of common PV systems to model irradiance on both the front and rear sides of bifacial PV technology - a technology that now represents 75% of utility-scale deployment in the US. Bifacial_radiance has been pivotal for understanding bifacial system performance, shading, and edge effects, and now agrivoltaics research. It has also helped develop simplified models used in PV due diligence tools for optimizing new deployments or evaluating the performance of existing projects. Now, it's the go-to comparison tool for many university, and industry-developed systems modeling tools, and a pivotal tool for further research in photovoltaics. This talk will cover the needs bifacial_radiance addresses as an open-source tool, its development path, and the opportunity for any raytracer to shine light on the solar industry through research and practical application of modeling in regular site installations and novel setups like agrivoltaics and vertical panels at high latitudes (and even the South Pole!).

agrivoltaics↗
DOE OSTI2025

EMT-TS Hybrid Simulation for Large Power Grids Considering IBR-Driven Dynamics

The escalating integration of inverter-based resources (IBRs) poses new challenges to power systems by introducing fast dynamics with higher frequencies, which may need to be simulated by an electromagnetic transient (EMT) program. As an alternative to conducting EMT simulations for the entire system, which is typically time consuming, hybrid simulation between EMT and phasor-domain transient stability (TS) can greatly reduce the computational burden while preserving the detailed fast dynamics in the EMT zone. This paper establishes an EMT-TS hybrid simulation platform using open-source tools, specifically ParaEMT, GridPACK, and HELICS, which are the EMT simulator, TS simulator, and interface framework, respectively. Case studies on the 240-bus Western Electricity Coordinating Council (WECC) system demonstrate that the developed ParaEMT-HELICS-GridPACK hybrid simulator can accurately capture both slow electromechanical and fast IBR-driven dynamics with a 2.4x speedup.

electromagnetic transient↗
DOE OSTI2023

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Implementation and evaluation of multi-dual mode counter-current chromatography in the CUP Modeler software

Counter-current chromatography (CCC) is a separation technique that utilizes immiscible solvent pairs as stationary and mobile phases, which imparts numerous benefits compared to solid-liquid chromatography including the ability to treat either the more-dense or less-dense solvent layer as the mobile phase. Multi-dual mode (MDM) is a CCC elution mode capable of improving the separation of closely eluting compounds by alternating upper- and lower-layer solvent flows in opposing directions within the same separation. While some effort has been made to model MDM, implementation of these models in experimental design has yet to be widely adopted. Accordingly, we further developed our previously published cell utilized partitioning (CUP) model to include MDM predictions with CCC and packaged the full suite of CUP modeling capabilities into a user-friendly, open-source tool called the CUP Modeler. The mathematical model for MDM CCC was derived and validated with experimental separation of ethyl guaiacol (EG) and ethyl phenol (EP), two compounds that co-elute in our previously demonstrated reductive catalytic fractionation (RCF) lignin monomer isolation method. The developed MDM model provided insights into the effect of multiple operating parameters - including stationary phase retention, flow rate, column efficiency, feed concentration ratio, selectivity factor, and solute distribution ratios - on the separation yields, productivity, and purities. Our model agreed with prevailing understanding of MDM but also revealed new insights including that the ideal distribution ratios for co-eluting solutes to be separated by MDM is between 1.1 and 1.5, with the lower value ideally close to 1.25. Overall, this work provides fundamental insights for MDM process design and enables broader adoption of general liquid-liquid chromatography with a new, open-source user-friendly interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Introducing the Video In Situ Snowfall Sensor (VISSS)

The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings

Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.

Zanetti, Ettore↗
DOE OSTI2021

Development of Integrated Mechanical Pods

This presentation highlights early wins, updated progress, and upcoming developments on ‘national-scale shared development platform’ for rapid prototyping, testing and validation of various integrated Mechanical Pod solutions and form factors. Such pod solutions consist of a set of all-electric heat pump mechanical equipment that have integrated functionalities through built-in controls, with heating, cooling, hot water, ventilation (including energy recovery), electrical management, and battery storage within a single package. The presentation draws inspiration from the success of bathroom pods in the US modular construction industry, UK’s efforts with unitizing mechanical systems as ‘utility cupboards’, and VEIC’s early wins in design-build of all-electric Mechanical Pod solutions in Vermont. The presentation includes researchers and partners involved with NREL in Design for Manufacturing and Assembly (DfMA), Virtual Design and Construction (VDC), and digital twin based process optimization modeling of integrated Mechanical Pod solutions. The presentation aims to highlight early wins from such a platform and how various physical and virtual tools are currently being employed as part of NREL’s ongoing multi-year project funded by US DOE. Streamlined procurement, coordination, installation, and O&M of Mechanical Pods such that the majority of work is delegated to the off-site modular factory implies monetary savings. Such a seemingly basic shift in location of the construction process leads to great reduction in complexity, first cost, lead time, and waste, and greater opportunities for innovative compartmentalization and integration of mechanical systems appropriately sized for each apartment or hotel guest room. However, past studies on unitized combination systems show that high installation costs, maintenance issues, challenges with system integration, limitations in existing electrical infrastructure, and lack of architecturally appealing solutions are key barriers. NREL and partners aim to address key barriers through DfMA approach, rapid prototyping and testing, and digital twin process optimization modeling. The presentation is also a call for interested entities to partner with NREL as part of the national-scale development platform, help drive both product and process innovation, and encourage open source sharing of learnings. Learning objectives include (1) learn about the vision of national-scale shared development platform for process-product innovation on integrated mechanical pod solutions and how to get involved, (2) gain an understanding of the components of an all-electric, high performance home, design characteristics and equipment included in an all-electric mechanical pod, integration of mechanical systems within a modular factories’ assembly line, and the system’s commissioning, operation and maintenance. The pre-planning and coordination with the factory and sub-contractors are also highlighted, (3) gain an understanding of using process modeling tools to quantify resource-constrained performance of operations (such as integration of energy efficiency strategies) to manufacture modules of varying design, (4) gain insights on virtual design, rapid prototyping, and emulated testing of various form factors across different climatic conditions. The need for such preliminary testing with open source sharing of learnings will also be highlighted.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

machine learning (ML), geothermal energy↗
DOE OSTI2022

An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling

This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.

36 MATERIALS SCIENCE↗