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

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Open-Source Virtual Testbed for a Real Net-Zero Energy Community

Net zero energy communities (NZECs) are critical to ensure sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present the modeling work based on a real NZEC. Two sets of models are developed: higher-fidelity physics-based models considering the interaction between subsystems of the studied NZEC and capturing fast-dynamics; and lower-fidelity data-driven models requiring less resource to establish and/or run. All models are validated against measurements from this real NZEC. In addition, we create a simulation framework which streamlines the processes for simulation and thus allows using developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and doesn’t sacrifice the thermal comfort to a great extent.

Huang, Sen↗
DOE OSTI2024

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Stability Analysis Tool for Bulk Power Systems Using Black-Box Models of Inverter-based Resources

This paper presents a small-signal stability analysis tool for large-scale power systems with high penetration of inverter-based resources (IBRs). Firstly, a network transfer function matrix (NTFM), which represents the information of the system topology, transmission lines, loads, IBRs locations, etc., is derived to model the entire power system network. Secondly, small-signal perturbation method is applied to obtain the sequence impedance/admittance responses of IBRs considering the frequency cross-coupling effects. With the obtained NTFM as well as IBRs' models, a multi-input, multi-output (MIMO) feedback system is constructed, and the generalized Nyquist criterion (GNC)-based stability method is employed to analyze the stability of the entire power system. Different testing cases based on a modified IEEE-14 bus system are leveraged to verify the proposed stability analysis tool.

Sun, Dongsen↗
DOE OSTI2022

Modeling Methods for Capturing System Interactions of Combined Technologies: A Study of PV + Battery

The costs of solar photovoltaics (PV) have been dropping in recent years, leading to increasing installations of solar PV systems and growing interest in how the technology will impact the electric grid at higher penetrations. Additionally, as battery costs decline it becomes important to understand the benefits and limitations of battery storage for the grid as well as potential benefits of co-locating battery storage and PV, particularly within the realm of future system planning. However, it is non-trivial to capture these potential benefits within a linearized capacity expansion model (CEM). This paper presents methodological developments to more fully represent the value and limitations of coupled PV and battery systems (PV + Battery) in CEMs using the Resource Planning Model (RPM), which co-optimizes capacity investments, transmission investments, and reduced-order dispatch in the Western Interconnection of North America through 2045. We use the model to simulate the evolution of the generation and transmission system under two core scenarios - a baseline scenario and a high renewable penetration scenario - coupled with sensitivities assuming low and midline PV and battery cost projections. When incorporating PV + battery, we find that it is important for CEMs to capture the ability of the coupled technology to provide firm capacity and reduce expected curtailment, compared to independent systems. These interactions can have even more dramatic impacts at higher solar penetrations.

14 SOLAR ENERGY↗
DOE OSTI2023

City and Borough of Sitka, Alaska: Modeling and Controls Assistance and Renewable Energy Resource Assessment [Slides]

This project develops the dynamic modeling of hydro plants and wind generation in Sitka, Alaska. A dynamic penetration of renewables was evaluated under various conditions. The impact of altering hydro generation control to synchronous condenser control was evaluated. While the existing hydro generations are sufficient to address voltage instability, a detailed study of the active-reactive capability limits of hydro generation operation should be conducted to uptake at various wind penetration levels.

14 SOLAR ENERGY↗
DOE OSTI2021

CSR in the Probabilistic Resource Adequacy Suite (PRAS): Fast Interregional Transmission Models for Renewable Integration Studies

NREL's Probabilistic Resource Adequacy Suite (PRAS) was developed for use in renewable integration studies, to assess the resource adequacy of large interconnected power systems with potentially significant interregional transfers of power. This presentation provides an overview of the tool and the methods it uses to solve multi-regional transmission and storage dispatch problems in a computationally-efficient manner.

composite system reliability↗
DOE OSTI2025

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗
DOE OSTI2025

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗
DOE OSTI2020

Recovery of High Value Anode Materials for a Closed Loop Li-ion Battery Recycling Process (Final Report)

Battery Resourcers’ goal with this Small Business Innovative Research Phase 1 project was to produce high quality and high purity graphite generated from its Li-ion battery recycling technology. Battery Resourcers’ technology recovers cathode material from a broad spectrum of Li-ion battery regardless of size, shape or chemistry. In Battery Resourcers existing model, the graphite is recovered to be used as a reductant and therefore the battery materials cost or recycling cost is not lowered by the recovery of the graphite. Currently, Battery Resourcers is piloting its lithium ion battery recycling process and the recovered LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) or LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC111) cathode materials have shown that they can outperform existing commercial NMC111 cathode materials while being produced at a projected lower cost. Battery Resourcers and its subcontractor WPI have demonstrated the same success with the recovered graphite for anode materials. The success of this award will enable Battery Resourcers offer lowest cost anode materials while reducing the carbon footprint of the battery industry. This will potentially lower the cost of EV by lowering the upfront cost of the battery and the end of life disposal costs. The technical objectives of this Phase 1 research project are to purify the recovered graphite from Battery Resourcers leaching process and demonstrate it is viable to be used in new electric vehicle lithium ion batteries. This project demonstrated that Battery Resourcers can recover graphite with less than 2% impurities. To separate the graphite from other materials Battery Resourcers will employed new filtering techniques and chemical processing methods.

25 ENERGY STORAGE↗
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sockeye Validation Support Using the SPHERE Facility

The engineering-scale heat pipe application Sockeye, being developed through the Nuclear En- ergy Advanced Modeling and Simulation program is concurrently undergoing code development and verification and validation activities. The Single Primary Heat Extraction and Removal Emu- lator (SPHERE) facility at Idaho National Laboratory has performed numerous sodium heat pipe experiments, one of which is modeled in this work to support Sockeye validation. Heat pipe data produced by the SPHERE facility was found to be very useful not only for Sockeye validation but also as a useful resource for model development. A Sockeye simulation performed using Sockeye’s conduction model modeled an experimental run at SPHERE and was found to be able to accurately reproduce the steady temperature distribution but revealed discrepancies in transient behavior after shutoff of the power. This effort also revealed a number of challenges of modeling an experimental setup such as SPHERE, for example, the importance of details in the setup such as possible gaps in insulation. Also, continuous collaboration between Sockeye developers and SPHERE experimentalists has been established, which allows future experiments to be refined in response to questions and challenges found by simulating earlier experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Carnac for Emulytics (HPC Annual Report V.1.0)

Carnac, located at Sandia's California site, is an institutional cluster for Emulytics that provides security researchers with resources to model enterprise computer networks and evaluate how resilient they are from attacks. While multiple Emulytics cluster computers have been built at Sandia, Carnac is the first system that was developed as an institutional resource that can be shared among different groups with disparate requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

WFIP3

The Wind Forecasting Improvement Project 3 (WFIP-3) is the first offshore-based wind resource characterization project within the WFIP construct, funded by the U.S. Department of Energy. WFIP-3 will provide a unique field study that will deliver the comprehensive suite of data needed to inform a series of modeling efforts that will develop and evaluate parameterization schemes suited to offshore environments and improved industry-targeted applications. The field study has two goals: (1) detailed sampling of the vertical structure of the Marine Atmospheric Boundary Layer (MABL) at key observational areas, creating a rich dataset that will be used to refine and validate parameterization schemes, and (2) wide-area sampling of the MABL to create a multi-scale array of observations informing and guiding models of resource characterization. We will deploy a multi-platform array of measurements that span the MABL and create a multi-scale observational array stretching south from Marth’s Vineyard across the wind energy areas.

17 WIND ENERGY↗
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Dynamic Validation of CNN-Based Surrogate Models for Inverter-Based Resources in Open-Source Solvers

Traditionally, distribution system planning has focused on steady-state analyses, with limited consideration of dynamic behavior. However, as large or medium-scale inverter-based resources (IBRs), particularly grid-following (GFL) inverters in commercial or industry buildings, become more prevalent, understanding their dynamic impact is essential for grid planning and operation. This article presents an innovative deep-learning (DL)-approach using convolutional neural networks technique to model the GFL inverters. Developed from real grid-tied commercial IBR transient data, these dynamic DL models overcome proprietary constraints by requiring minimal knowledge of internal converter physics while maintaining high accuracy and flexibility. To demonstrate their applicability, the models were incorporated into GridLAB-D, an open-source, three-phase distribution analysis tool. This integration enables dynamic simulations of large-scale distribution networks with high IBR penetration stability analysis. Rigorous testing and validation, aligned with industry standards, confirmed the reliability and efficiency of this approach, paving the way for enhanced planning and operational assessments of modern power systems.

Deep-learning↗
DOE OSTI2026

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗
DOE OSTI2026

Framework for Modeling 3D-Printed Concrete Construction to Assess Energy Efficiency and Backup Power Trade-Offs in a Mixed-Use, New Construction Neighborhood Development

This paper presents a framework that expands the URBANopt(TM) modeling platform to include 3D-printed concrete wall assemblies and assess energy efficiency and backup power trade-offs in new housing developments. Applied to a planned mixed-use neighborhood in Oil City, Pennsylvania, the workflow integrates building energy and distributed energy resource (DER) modeling to evaluate envelope and equipment upgrades alongside DER operations. Results show that advanced 3D-printed envelopes combined with efficient systems and onsite photovoltaics (PV) and storage reduce energy use intensity and sustain critical loads during outages. The framework supports planning for emerging construction technologies by quantifying key trade-offs between energy efficiency and backup power performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗