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DOE OSTI2025

American-Made Solar Prize: Edgeli Enables DER Integration (CRADA 615) (Final Report)

The purpose of this project was to demonstrate how granular time series data and automated data transformation, and impact assessment tools could speed interconnection approvals for distributed energy resource projects of various types and sizes. Types included community solar, rooftop solar, and EV charging projects. Using software routines to automate the transformation of data (e.g. GIS) to a network database and power flow model then applying scenarios to create hourly (8760) hosting capacity values and voltage and thermal impacts for specific projects, we were able to demonstrate the feasibility of quickly assembling and analyzing key utility data sets for interconnection purposes. The outcomes of this effort will become the foundation for future work that will enhance and encapsulate the software components developed as part of this project, into web services (e.g. APIs) that can be integrated into queue management systems and automate interconnection screening processes.

14 SOLAR ENERGY↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Mars Polar Processes: Land and Sky

The titles in this section include: 1) Morphological Evidence for the Large-scale Evolution of Martian North Polar Troughs? 2) New Views of the Martian Polar Regions: The Latest Results from the Mars Odyssey THEMIS Investigation; 3) Observations at the Chasma Australe Re-Entrant, South Polar Region, Mars; 4) Stratigraphic Details of Uppermost Units within South Polar Layered Deposits on Mars; 5) Detailed Geologic Analysis of Part of the South Polar Layered Deposits, Planum Australe, Mars: Part II; 6) SHARAD: Radar Volume Scattering and the Polar Layered Terrains on Mars; 7) Arabia and Memnonia Equatorial Regions with High Content of Water: Data from HEND/Odyssey; 8) Alaskan Permafrost Analogs of Martian Small Valley Networks, Thermokarst, Terrain Softening, Terraces, and Volcanic Craters; 9) Periglacial Processes in the Southern Hemisphere of Mars; 10) Thermal Modeling of Possible Surface Water Ice Deposits in Juventae Chasma; 11) Modeling Depth to Ground Ice on Mars; 12) Ground Ice at the Phoenix Landing Site: A Preflight Assessment; 13) Possible Temperature-related Differences in Slope Angle between the North and South Walls of Coprates Chasma, Mars; 14) Seasonal Variations within Korolev Crater, Mars; 15) South Polar Ar Enhancement as a Tracer for Southern Winter Horizontal Meridional Mixing; 16) An Investigation of the Correlation of Water-Ice and Dust Retrievals via the MGS TES Data Set; 17) Observations of Plasma Waves near Mars and Their Implications for Atmospheric Loss.

Source record↗

DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2024

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible-Near-Infrared to Thermal Infrared

Super-coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super-coarse dust accounts for less dust extinction in the visible-to-near-infrared (VIS-NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS-NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET-retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super-coarse dust in the AERONET retrievals due to the limited VIS-NIR sensitivity. An extra super-coarse mode included in the AERONET-retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS-NIR.

thermal infrared (TIR)↗

DOE OSTI2023

Measurements of He-Ar, He-N 2 , and He-air thermal creep slip and accommodation coefficients at high temperatures

Understanding gas flows in capillaries has many applications in modeling the transport of gases in nano-structured, porous, or fractured media. A network of capillaries can often approximate these media, and also information on gas-surface interactions obtained from capillary experiments can be used for modeling flows in these media. Experimental data on flows of different mixtures of He-Ar, He-N 2 , and He-air in the slip regime and in stainless steel capillaries at high temperatures were obtained by using a two-bulb apparatus. An accurate expression for the thermal creep slip coefficients with the Lennard-Jones potential parameters and diffuse-specular reflection gas-surface interaction conditions were then used to obtain the accommodation coefficients for the different gases. The experimental data are best described with values of accommodation coefficients in the range of 0.1–0.3 for He and 0.5–1.0 for Ar, N 2 , and air. Further, the use of values in this range is suggested for modeling gaseous flows in capillaries and nano-structured, porous, or fractured media if other direct measured values for a particular medium are unavailable.

42 ENGINEERING↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗

DOE OSTI2024

Simulation-Guided Decision-Making for Enhancing Energy Resilience in a Remote Alaskan Community

The increasing frequency and severity of extreme weather events underscore the need to bolster the resilience of energy infrastructure in remote coastal communities exposed to climate hazards. In recent years, considerable effort has been made to harden the grid infrastructure of remote communities through investment in energy storage, advanced metering, renewable generation and energy-efficient loads. However, simulation-based studies are still needed to identify appropriate locations for investment and determine the adequacy of existing infrastructure in supporting new resources. Preparing the required models may often be challenging due to insufficient metering, disparate data sources and workforce limitations. This paper describes modeling efforts undertaken to represent, within a unified co-simulation platform: (a) the electric distribution network and (b) the thermal behavior of a community medical center, in the remote community of Cordova, Alaska. With the help of case-studies, it is shown how the developed simulation platform can help the local utility in making decisions regarding capital investment aimed at enhancing community resilience.

Microgrid, energy storage, resilience↗

DOE OSTI2022

Scaling Building Energy Audits through Machine Learning Methods on Novel Drone Image Data

Building energy audits are time-consuming and labor-intensive. This paper describes a new method using machine learning (ML) techniques on novel data sources (drone images) to improve the identification of building characteristics and retrofit opportunities, and thereby reduce the effort for audits. The new ML method includes: (1) Building footprint extraction using line extraction, polygonization, and polygon-merging, (2) Building envelope extraction using PIX4d modeling software to reconstruct a building 3D model, (3) Visualization tool for viewing images from the 3D model, (4) Window-to-wall ratio (WWR) using state-of-art deep neural network semantic segmentation, (5) Envelope thermal anomaly detection using an unsupervised machine learning clustering algorithm, and (6) Rooftop energy equipment detection based on an object detection algorithm. The testing of this method involved a comparison of additional ML-generated information overlaid on current ‘state-of-practice’ audit and remote assessment baselines using evaluation metrics: labor time and associated cost, marginal benefits of using ML-generated information in workflows for audits and remote assessments, integration potential with existing processes and tools, and replicability/scalability of the method. In two test buildings in California that had comprehensive drawings and meter data available, the ML method effectively generated a building footprint, envelope, rooftop equipment, WWR, and locations of envelope thermal anomalies. Projected target segments of the ML method are sites with minimal drawings and energy data, and underserved sectors such as multistoried housing, disadvantaged communities, and schools for which the ML method can enable identification of building asset characteristics and prioritization of envelope retrofits and decentralized energy equipment retrofits.

Singh, Reshma↗

DOE OSTI2023

Performance Evaluation of Gray-box and Machine Learning Models of a Thermal Energy Storage System with Active Insulation

An interior partition wall integrated with active thermal storage and a dynamic insulation system was built and then installed in an office building in Oak Ridge, Tennessee, TN. This smart wall, termed the Empower Wall, was equipped with embedded pipes in the building envelope core component and an additional pipe network enclosing rigid insulation to switch on and off the active insulation dynamically. The performance of the wall's contribution to cooling load reduction under different parameters has been investigated in previous publications. Aiming to be deployed into model predictive control and other optimization methods, simplified and reliable models for the developed wall and the room accommodating it are required. They are needed to characterize the properties and thermal response of both Empower Wall and building envelope, which form an essential component for accurate indoor temperature or cooling/heating demand prediction. In this study, simplified gray-box and regression models as well as machine learning model were developed and the performance of them were compared and analyzed.

Cui, Borui↗

DOE OSTI2022

A Machine Learning-Assisted Framework to Control Thermally Anisotropic Building Envelopes in Residential Buildings

To curb the energy consumption of buildings and their related CO2 emissions, Oak Ridge National Laboratory (ORNL) has developed the thermally anisotropic building envelope (TABE) —a multi-layer design comprising insulation materials and metal foils connected to thermal loops. In this study, a machine learning-assisted framework was developed to control the TABE in residential buildings to reduce the computation load for future optimal rule-based control and application. First, a 2D finite element model was established in COMSOL to calculate the hourly heat flux through exterior walls installed with the TABE. Then, TABE wall heat fluxes were simulated for various given indoor and outdoor boundary conditions, as well as thermal loops fluid temperatures and flow rates. Since the finite element simulations are computationally expensive, an artificial neural network (ANN) was then trained to use as a proxy of the finite element (COMSOL) modeling. Finally, the trained ANN model was coupled with the EnergyPlus model to predict the energy consumption of a US Department of Energy prototype single-family house installed with the TABE. An optimal simple rule-based control was determined from predefined rules for a case study. The results demonstrate that the developed machine learning–assisted framework can reduce 99.9% of the computation time while efficiently managing residential building energy for installed TABE walls.

Shen, Zhenglai↗

DOE OSTI2025

Atomic-Scale Tracking of Topological Defect Motion and Incommensurate Charge Order Melting

Charge order pervades the phase diagrams of quantum materials where it competes with superconducting and magnetic phases, hosts electronic phase transitions and topological defects, and couples to the lattice generating intricate structural distortions. Incommensurate charge order is readily stabilized in manganese oxides, where it is associated with anomalous electronic and magnetic properties, but its nanoscale structural inhomogeneity complicates precise characterization and understanding of its relationship with competing phases. Leveraging atomic-resolution variable-temperature cryogenic scanning transmission electron microscopy, we characterize the thermal evolution of charge order as it transforms from its ground state in a model manganite system. We find that mobile networks of discommensurations and dislocations generate phase inhomogeneity and induce global incommensurability in an otherwise lattice-locked modulation. Driving the order to melt at high temperatures, the discommensuration density grows, and regions of order locally decouple from the lattice periodicity. Published by the American Physical Society 2025

Schnitzer, Noah (ORCID:0000000210421146)↗

NASA NTRS2000

Theoretical Modeling and Computer Simulations for the Origins and Evolution of Reproducing Molecular Systems and Complex Systems with Many Interactive Parts

Our research effort has produced nine publications in peer-reviewed journals listed at the end of this report. The work reported here are in the following areas: (1) genetic network modeling; (2) autocatalytic model of pre-biotic evolution; (3) theoretical and computational studies of strongly correlated electron systems; (4) reducing thermal oscillations in atomic force microscope; (5) transcription termination mechanism in prokaryotic cells; and (6) the low glutamine usage in thennophiles obtained by studying completely sequenced genomes. We discuss the main accomplishments of these publications.

Liang, Shoudan↗

NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD API, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return (MSR) mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency (ESA) with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Hume L. Peabody↗

NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Thermal desktop↗

NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage observatory or launch models at the highest levels of assembly. However, the effort to generate reduced models, and verify against their detailed counterparts, is a challenge that has not yet been conclusively solved. Higher level organizations often place a limit on the number of nodes for delivered models with the assumption that smaller models generally result in less computation time. However, the burden of producing and verifying the accuracy of the reduced models is placed primarily on the lower-level organizations, which in turn consumes resources needed to produce these models. Limiting the total number of allowable nodes may also prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which generally require more nodes than older centroid based models. A methodology using Thermal Desktop was described in 2010 which used: (1) finite elements and edge nodes for a conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. At that time, the methodology was clear, but the implementation would have had to be done manually; however, with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and for the first time, be a viable approach for the generation of reduced models. The approach was implemented and developed at the NASA Goddard Space Flight Center (GSFC) for the Capture, Containment, and Return System (CCRS) payload on the Earth Return Orbiter (ERO) as part of the Mars Sample Return mission, resulting in the TCYEE tool. ERO features a spacecraft bus provided by Airbus through the European Space Agency with node limitations on the delivered CCRS model provided by GSFC. TCYEE was used to generate the reduced model for this delivery and the predictions compared favorably to the detailed model currently in use for the thermal performance evaluation. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements on the RST observatory model for use in launch simulation analyses. This paper describes the methodology, its implementation, and compares the performance of reduced models generated by TCYEE to their detailed counterparts.

Thermal Desktop↗

DOE OSTI2025

Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However, low flammability and a propensity for high nitrogen oxide (NO x ) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NO x emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigated in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NO x concentrations below those of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing nonadiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and postflame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors' past work, this arrangement has demonstrated the significant sensitivity of exit NO x to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and nonpremixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NO x emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have been shown to create conditions that are conducive to increased N 2 O formation in the lean stage. In conclusion, the results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia.

Combustion↗

DOE OSTI2024

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗