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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 OSTI2023

CoolPINNs: A physics-informed neural network modeling of active cooling in vascular systems

Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during the design and operational phases of these engineered systems. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. Here, this paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation—called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs since the approach is meshless. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the most significant utility of the current framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Techno-economic Analysis of Novel PV Plant Designs for Extreme Cost Reductions

A techno-economic analysis is underway examining the cost and performance of future large-scale photovoltaic (PV) plant components, including bifacial modules, tandem modules, increased plant voltage architectures, and module-level power electronics. Integration of these components into PV plant designs is compared with current PV technologies based on levelized cost of electricity (LCOE). Baseline models are developed and validated against recorded PV plant performance data. Expected cost and performance data of future PV technologies are incorporated into the baseline models. An evolutionary algorithm is utilized to optimize PV plant configuration, technology combination, and LCOE. This paper focuses on the bifacial module analysis.

14 SOLAR ENERGY↗
DOE OSTI2021

Airport Risk Assessment Model Stakeholder Symposium Event Report

On June 22-23, 2021, the Pacific Northwest National Laboratory hosted a two-day virtual stakeholder symposium to share how the Airport Risk Assessment Model (ARAM) is putting security resource allocation planning into the hands of our airport’s front lines of defense. This report highlights the key takeaways from the discussions.

42 ENGINEERING↗
DOE OSTI2025

Lessons Learned on Prize Design in the Perovskite Startup Prize

In 2018, the U.S. Department of Energy's Solar Energy Technologies Office and the National Renewable Energy Laboratory set out to develop a repeatable, predictable prize model with the American-Made Challenges. A new model launched by the Solar Energy Technologies Office in March 2021 was the American-Made Perovskite Startup Prize. This new prize aimed to accelerate the growth of the domestic perovskite industry and support the rapid development of solar cells and modules that use perovskite materials. By sharing the outputs of the prize, we hope that our lessons learned will help continue to build the clean tech entrepreneurship support ecosystem and influence how a successful prize design can be achieved.

14 SOLAR ENERGY↗
DOE OSTI2023

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

13 HYDRO ENERGY↗
DOE OSTI2025

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗
DOE OSTI2023

Innovation in complementary energy technologies from renewable energy policies

Complementary renewable technologies support renewable energy use as they can help balance the intermittency of solar and wind generation. Previous research has shown that environmental policies directly targeting renewable energy can drive innovation in solar PV and wind technology. This study explores the impact of renewable policies on innovation in complementary renewable technology that assists with integration, including combustion with mitigation potential, efficient power transmission or distribution, and enabling technologies. We use negative binomial models and a panel of 25 OECD countries from 1997 to 2015 to evaluate these relationships. We are not able to find evidence that renewable policies incentivize innovation in complementary combustion or transmission and distribution technologies, and only limited evidence that demand-pull instruments are associated with increased innovation in smart grid technology. Technology-push policies, such as public research and development funds for renewable energy, have had limited impact on grid-supporting energy storage innovation. Our findings suggest that policymakers wishing to accelerate these complementary technologies need to go beyond policies targeting renewable energy in order to drive innovation in complementary fields.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

OVERVIEW OF PARTICLE DEPOSITION MODELS FOR SPENT NUCLEAR FUEL STORAGE SYSTEMS

Deposition models were built to evaluate contaminant deposition on spent nuclear fuel (SNF) canisters. The primary contaminant of concern is chloride, which is dispersed in the atmosphere and then deposits onto the canisters. During dry storage, the primary degradation process is likely to be Chloride Induced Stress Corrosion Cracking (CISCC) at the heat-affected zones of the canister welds. It is known that stainless steel canisters are susceptible to CISCC; however, the rate of chloride deposition onto the canisters is poorly known, based on sparse field data from a small number of sites. The models presented in this study could be useful for determining the rate of deposition on the canisters and the likelihood of CISCC to help with SNF canister ageing management. The deposition models were developed with the commercial computational fluid dynamics (CFD) code STAR-CCM+. Various deposition mechanisms were considered and incorporated into the models, and a sensitivity study was conducted to determine the most important mechanisms for deposition within a SNF storage system. The models included both a vertical and horizontal configuration storage system: NAC International’s Modular, Advanced Generation, Nuclear All-purpose STORage System (MAGNASTOR®) and a NUHOMS® horizontal storage module respectively. The resulting canister deposition on the horizontal canister is visually compared with inspection data taken onsite at the Calvert Cliffs Nuclear Power Plant. These models are preliminary, and development of the models will continue. Future validation exercises are currently being planned, including the Canister Deposition Field Demonstration (CDFD) effort funded by U.S. Department of Energy office of Nuclear Energy office of Spent Fuel Waste Science and Technology. The goal of the modeling presented is to demonstrate a potential modeling technique that could be used to plan and inform SNF canister ageing management programs with predictive models for the timing and occurrence of canister CISCC.

Suffield, Sarah R.↗
DOE OSTI2020

Developing of Quaternary Pumped Storage Hydropower for Dynamic Studies

Quaternary pumped storage hydropower (Q-PSH) technology, as one of the new advanced-PSH technology, has been developed by taking advantage of Conventional-PSH (C-PSH) and Adjustable Speed-PSH (AS-PSH). By combining adjustable-speed pump unit and conventional hydropower turbine unit in the quaternary configuration, Q-PSH has the more competitive capability of providing fast power support in the future high renewable penetrated power system. Acting as energy storage (ES), Q-PSH provides promising power supply to deal with the uncertainty and variability from renewable energy generation. This paper focuses on the dynamic modeling of Q-PSH technology employing full-converter machine and the impact of Q-PSH on the frequency response in a system. The detailed model of Q-PSH is developed and implemented in the IEEE 14-bus system based on GE Positive Sequence Load Flow (PSLF) platform, which captures the dynamic of multiple operation modes, especially hydraulic short-circuit (HSC) operation mode. Several cases are set up to reveal the advantages of Q-PSH technology when power electronic based renewable energy generation is deployed in the system. Sensitivity studies of the controller in pump governor show the impact of parameters in pump response performance. The comparison case illustrates the impact of frequency response provided by the Q-PSH in the system.

dynamic modeling↗
DOE OSTI2020

Health co-benefits and mitigation costs as per the Paris Agreement under different technological pathways for energy supply

This study assesses the reductions in air pollution emissions and subsequent beneficial health effects from different global mitigation pathways consistent with the 2 °C stabilization objective of the Paris Agreement. We use an integrated modelling framework, demonstrating the need for models with an appropriate level of technology detail for an accurate co-benefit assessment. The framework combines an integrated assessment model (GCAM) with an air quality model (TM5-FASST) to obtain estimates of premature mortality and then assesses their economic cost. The results show that significant co-benefits can be found for a range of technological options, such as introducing a limitation on bioenergy, carbon capture and storage (CCS) or nuclear power. Cumulative premature mortality may be reduced by 17–23% by 2020–2050 compared to the baseline, depending on the scenarios. However, the ratio of health co-benefits to mitigation costs varies substantially, ranging from 1.45 when a bioenergy limitation is set to 2.19 when all technologies are available. As for regional disaggregation, some regions, such as India and China, obtain far greater co-benefits than others.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reservoir Thermal Energy Storage Benchmarking (Rev. 3)

A benchmarking analysis of RTES research funded by GTO through the Beyond Batteries projects was conducted against the ESGC to see where they fit within the identified ESGC Use Cases. The projects were found to advance knowledge in multiple ESGC use cases, either directly or in some cases, indirectly as enabling technologies. This analysis is helpful to understand where RTES and associated research fits into the larger discussion around energy storage technologies. Also, a retrospective analysis of the Beyond Batteries projects was conducted to evaluate what the projects learned and how the results can be applied to advance the value of RTES. Major results of each of the studies are summarized in Table 2. Additionally, a comparative metrics analysis for RTES was completed to understand where RTES lies within the energy storage industry. Metrics for evaluation of RTES and its comparison to other storage technologies were selected and ranges of their values compiled. The selected metrics – LCOE (levelized cost of energy), capital costs, roundtrip efficiency, energy storage capacity, and storage time – were chosen based on data availability and have a particularly strong influence on the potential deployment of a storage technology. Charts which compare the metrics are presented in section 4.3 and show ranges for each of the 10 selected technologies. However, due to a lack of domestic operational facilities, values for RTES and for portions of the remaining technologies are based on theoretical modeling and studies of best-case scenarios. LCOE estimates for RTES fall within the lower reaches of Figure 15, but nevertheless amount to 2 – 5 times the ESGC Roadmap goal for LCOE, for example in the Facilitating and Evolving Grid Use Case. Capital costs for RTES sit on the higher end (Figure 16) but are expected to decrease as new projects are developed and the technology is refined. The theoretical roundtrip efficiency reported for RTES varies from mid to high percentages (Figure 17) with efficiencies upwards of 93% in modeled scenarios in the Portland Basin (Bershaw et al.,2020). RTES is also expected to have the largest energy storage capacities and longest storage times, likely matched only by lower efficiency hydrogen storage. To better assess the role that RTES could play in energy storage we examined it’s potential in the U.S. The potential depends on many factors. Recently, many researchers have started looking at deep sedimentary basins, depleted oil and gas fields, and basalt formations as potential targets for RTES development. The United States Geological Survey (USGS) has analyzed various cities and shown substantial RTES potential in the cooling sector (Pepin et al., 2021). By modeling RTES in low-quality groundwater (e.g., brackish), it is shown to be favorable across the U.S. with particular suitability in the Illinois Basin, Coastal Plains, and Basin and Range regions. Seasonal RTES operations have also been modeled in the Portland Basin by those at the USGS and Portland State University to simulate an RTES system supplying heating loads needed for the Oregon Health and Science University. Simulations suggest that high conductive heat loss in the initial years exists but tends to decrease with increasing time and development of the resource due to self-insulating nature of the basalts (Burns et al., 2020). Other national laboratory efforts are taking a close look at many of the technical issues involved with RTES (McLing et al., 2019, McLing et al., 2022). These include difficulties in understanding geochemical, hydrogeological, mechanical, and microbiological changes at such elevated temperatures and operational scenarios. Major gaps in research are identified and suggested for future work. With this increased focus to understand how to make RTES successful in the U.S., this technology could be a potential solution to many of the nation’s energy storage problems. For the energy independence of this country, the DOE should prioritize de-risking this technology by making future investments in pilot-scale demonstrations to attract potential investors.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy–funded projects for advancing HPC Software via AI: Ellora and Durban.

Teranishi, Keita [ORNL] (ORCID:0000000166472690)↗
DOE OSTI2022

Performance of Additively Manufactured Internally Cooled Airfoils for Small Industrial Gas Turbines

The goal of this effort is to screen several internally cooled airfoil designs that could be fabricated when additive manufacturing becomes a viable alternative for industrial gas turbine applications. The objectives of the current paper are as follows: 1) Describe two advanced additively manufactured cooling schemes, 2) Describe the analytical tools and results that guided the design. 3) Identify key additive manufacturing issues and steps taken to mitigate these issues. 4) Compare experimentally measured cooling technology curves to 1-D cooling models, and 5) Compare the cooling performance of three different internally cooled airfoils using experimentally measured cooling technology curves.

Straub, Douglas↗
DOE OSTI2022

Becoming a 10: A Closer Look at the U.S. Department of Energy Home Energy Score's Updates, Improvements, and Expansion

The U.S. Department of Energy (DOE)'s Home Energy Score provides homeowners, buyers, and renters directly comparable and credible information about a home's estimated energy use and costs. Certified Qualified Assessors conduct low-cost assessments to provide each home a 1-10 score alongside a set of cost-effective upgrades to improve the score. As of February 2022, hundreds of assessors have delivered over 175,000 scores to homes across the country. Originally released in 2012 using DOE2.1e as the modeling backend, after years of effort, a new version of Home Energy Score was released in 2021 utilizing DOE's flagship energy modeling software, EnergyPlus. The updated architecture leverages modeling advancements and enables new building technologies to be added to the Scoring Tool. The new release represents a leap forward in harmonizing modeling assumptions across DOE and industry programs. In this paper we discuss the rigorous approach to model comparison with DOE2 undertaken prior to the update, utilizing test homes and real homes from the Home Energy Score database to strike a balance between consistency and more accurate energy predictions. We also discuss additional new capabilities, including improvements made to the upgrade recommendations methodology, the inclusion of an energy cost estimate metric based on ResStock analysis for use in home appraisals, and improved data analysis for quality assurance. Finally, we look at the impact Home Energy Score has had over the last decade and its future potential as its uptake in state energy plans, local ordinances, utility programs, and real estate data continues to grow.

building energy modeling↗
DOE OSTI2025

Fluoride-Cooled High-Temperature Pebble-Bed Reactor Reference Plant Model Updates

This work presents the latest improvements to, and investigations performed with, the Fluoride-Cooled High-Temperature Pebble-Bed Reactor reference plant models for the United States Nuclear Regulatory Commission. These models, developed with the Comprehensive Reactor Analysis Bundle, or BlueCRAB, serve as the foundation for the future development of detailed design evaluation models based on license applications. BlueCRAB is the code suite proposed for non-light-water reactor systems safety analyses, and it incorporates various simulation tools developed by the Nuclear Energy Advance Modeling and Simulation program, including the Griffin code for reactor physics, the Pronghorn and SAM codes for core thermal fluids, the BISON code for solid conduction and fuel performance, and the SAM code for system analysis. The primary objective of this work is to assess the level of readiness of BlueCRAB for modeling fluoride-cooled high-temperature pebble-bed reactors. We first developed numerical models in BlueCRAB that include the key physics for this technology, ensuring an adequate level of fidelity for modeling the core performance during accident scenarios. This was followed by simulation of transient scenarios, two loss-of-forced-cooling events (one protected and one unprotected), and two control rod withdrawal events (one delayed and one prompt supercritical reactivity insertion). The analysis includes comparisons between the 2-D thermal fluid porous media models in Pronghorn and SAM, comparisons between coupled Pronghorn-Griffin and coupled SAM-Griffin models for two loss-of-forced cooling events and one control rod withdrawal event, and comparisons between SAM single-solve and domain-overlapping approaches for multi-scale thermal fluid coupling. In addition, we performed comparisons between 3-D, 2-D, and 0-D neutronic models for the two control rod withdrawal scenarios with Pronghorn-Griffin. The results show that the BlueCRAB models led to physically intuitive solutions for the scenarios examined. The changes in the various scalar and vector fields such as the neutron flux, power, temperatures, densities, pressures, and velocities are all within the expected ranges, and their distributions can be explained from the system response of the transients and the geometric and material variations. Several comparisons suggest that the porous media models in Pronghorn and SAM can lead to similar solutions, even though they are based on different methodologies. The simulations demonstrate that there are differences between the various levels of fidelity, and it is advisable to have flexible tools that can cover the breadth and depth of needs that may arise in future technical evaluations. We believe that BlueCRAB’s capabilities represent a significant asset for confirmatory analyses aimed at resolving important safety questions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Pathways to a Sustainable Aviation Ecosystem: Life Cycle Analysis with Blockchain Carbon Accounting

When linked with the National Renewable Energy Laboratory's (NREL's) rigorous life cycle assessment methodologies, blockchain technology can enable aviation stakeholders to quantify and transact on the impacts and benefits of sustainable aviation fuels and technologies. NREL can develop high-fidelity carbon modeling tools that enable apples-to-apples comparisons across technologies, fuels, companies, and jurisdictions.

aviation↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Atomic Electron Tomography of Thin Films

In the past decade, the development of atomic electron tomography (AET) [1] has allowed for the 3D characterization of atomic positions in nanoparticle [2], monolayer [3], and needle [4] geometries. Furthermore, many technologically important systems are synthesized and utilized as thin films. Several challenges in sample preparation and data analysis have been overcome to expand the scope of AET to these systems. Polycrystalline yttrium-doped hafnium dioxide (HfO 2 ) was chosen as a model system, because it is technologically relevant as a high capacitance dielectric and ferroelectric.

74 ATOMIC AND MOLECULAR PHYSICS↗