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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effects of transient fission gas release on rod balloon burst behavior during a loss-of-coolant accident

The US nuclear energy industry is investigating strategies to increase the reactor operating cycle to 24 months, which would result in rod average burnups exceeding the current limit of 62 GWd/tU. To support this goal, multiphysics simulations tools and methodologies are being developed to predict the effects of this change during transient events such as loss-of-coolant accidents (LOCAs). In this work, two cladding burst correlations were used to predict cladding failure. Here, these were coupled with three transient fission gas release (tFGR) models and were implemented in the BISON fuel performance code to quantify any changes in cladding burst behavior during a large-break LOCA. First, a simple linear model was used to perform a sensitivity analysis of a single fuel rod. Second, a tFGR model available in BISON was applied to 281 fuel rods throughout the core. The third model examined was an empirical correlation based on data from the literature.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Improved Bayesian regularization of inverse problems in vibrations and acoustics using noise-only measurements

Here, this paper studies Tikhonov regularization (ridge regression) parameter selection for problems in vibrations and acoustics. The selection method is based on a popular Bayesian method, but it incorporates measurements of sensor noise. The regularization parameter is closely related to the ratio of system input energy to noise energy, so noise measurements inform the inference procedure and improve parameter identification. In cases where standard Bayesian regularization identifies zero as the optimal regularization parameter, noise measurements guarantee a unique nonzero optimum. Sufficient theoretical criteria are developed for this guarantee. The method is verified in even-determined and under-determined configurations in an acoustic source localization simulation and a vibration load identification experiment. It is shown to yield significant improvements over existing empirical Bayesian regularization. Improvements are larger in the even-determined case and smaller in the under-determined case, wherein the inverse solution is less sensitive to the regularization parameter.

42 ENGINEERING↗
DOE OSTI2020

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Assessing concurrent effects of climate change on hydropower supply, electricity demand, and greenhouse gas emissions in the Upper Yangtze River Basin of China

Hydropower importantly provides flexible low-carbon electricity, however, climate change will affect the hydropower system through altering hydrologic regimes while also affecting electricity demands for heating and cooling that hydropower resources serve. This study assesses the effect of climate change on hydropower and electricity demand in the Upper Yangtze River Basin (UYRB) in China on the regional net electric load and greenhouse gas (GHG) emissions. This is accomplished by using climate projections from five global climate models (GCMs) to simultaneously force (1) a physically-based hydrological model and a statistically-based hydropower model to estimate the future generating capacity of 21 large hydropower plants in the UYRB and (2) an empirical electricity demand model accounting for socioeconomic and climatic factors. Under climate change, the projected hydropower generation in the UYRB tends to increase in the 21st century but is far less than the increase in electricity demand, increasing the gap between demand and supply. Future increases in overall electricity demand are driven by GDP growth, but climate change will alter the distribution of the seasonal electricity demand. Climate warming decreases electricity demand for heating in winter and increases electricity demand for cooling in summer, but ultimately increases demand. Meanwhile, there is an increasing mismatch between electricity demand and hydropower supply associated with inter- and intra-annual variations, owing to the temporal climate change and increase in compound climate extremes (droughts and heatwaves). Finally, meeting the gap between supply and demand due to climate change is estimated to contribute 79.0–184.6 and 50.6–316.2 MMT CO 2e /yr of additional GHG emissions by the mid and end of 21st century, respectively.

13 HYDRO ENERGY↗
DOE OSTI2022

Data-Driven Simulation-Based Planning for Electric Airport Shuttle Systems: A Real-World Case Study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event-driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are optimized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Aerodynamic Rotor Design for a 25 MW Offshore Downwind Turbine

Continuously increasing offshore wind turbine scales require rotor designs that maximize power and performance. Downwind rotors offer advantages in lower mass due to reduced potential for tower strike, and is especially true at large scales, e.g., for a 25 MW turbine. In this study, three 25 MW downwind rotors, each with different prescribed lift coefficient distributions were designed (chord, geometry, and twist) and compared to maximize power production at unprecedented scales and Reynolds numbers, including a new approach to optimize rotor tilt and coning based on aeroelastic effects. To achieve this objective the design process was focused on achieving high power coefficients, while maximizing swept area and minimizing blade mass. Maximizing swept area was achieved by prescribing pre-cone and shaft tilt angles to ensure the aeroelastic orientation when the blades point upwards was nearly vertical at nearly rated conditions. Maximizing the power coefficient was achieved by prescribing axial induction factor and lift coefficient distributions which were then used as inputs for an inverse rotor design tool. The resulting rotors were then simulated to compare performance and subsequently optimized for minimum rotor mass. To achieve these goals, a high Reynolds number design space was developed using computational predictions as well as new empirical correlations for flatback airfoil drag and maximum lift. Within this design space, three rotors of small, medium and large chords were considered for clean airfoil conditions (effects of premature transition were also considered but did not significantly modify the design space). The results indicated that the medium chord design provided the best performance, producing the highest power in Region 2 from simulations while resulting in the lowest rotor mass, both of which support minimum LCOE. The methodology developed herein can be used for the design of other extreme-scale (upwind and downwind) turbines.

downwind rotors↗
DOE OSTI2023

Film condensation with high heat fluxes and scaled experiments using pure steam for reactor containment cooling

Condensation tests were performed using a newly developed test facility for scaling the passive containment cooling system (PCCS) to a small modular reactor (SMR). The PCCS of the SMR plays a pivotal role in ensuring greater safety, reliability, and compactness than what is afforded by traditional reactors. Therefore, a well-designed PCCS is essential to SMRs. However, previous studies and test data were unsuitable for scaling, due to high variation in the test geometry and operating conditions. This study intends to close this research gap by using a novel designed scaled test facility consisting of vertical condensing test sections featuring 1-, 2-, and 4-inch-diameter condensing tubes with annular water cooling, and by applying superheated and saturated steam with different steam mass flow ranges of 5–25 g/s. Further, the primary test data, including axial temperatures, mass flow rates, and pressures, were used in conjunction with a standard data reduction method to estimate critical parameters such as heat fluxes, heat transfer coefficients, and condensation rates. These scaled test data would support improving empirical correlations and validating condensation models to identify scaling distortion for SMR PCCSs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Scaling considerations for supercritical carbon dioxide cycles including turbomachinery loss models

A modeling framework for the supercritical carbon dioxide recompressed closed Brayton cycle was developed. Unlike typical models, this effort incorporated generalized empirical turbomachinery loss models. Aerodynamic, windage, and leakage losses were considered in order to address the limitations of conventional constant-efficiency turbomachinery assumptions without relying on machine-specific or computationally expensive simulations. The model enables system-level exploration of optimal cycle design across a range of power scales, including smaller scales that are relevant to microreactors and extraterrestrial power applications. Parametric studies and multi-objective optimizations are used to evaluate the trade-offs between thermal efficiency and system compactness based on an analytical heat exchanger scaling model, yielding Pareto-optimal fronts across a range of operating pressures. Results reveal that at small power scales, the Pareto-optimal compressor inlet pressure becomes subcritical due to the increasing influence of density-dependent turbomachinery losses. Here, the relative contributions of each loss mechanism are quantified, and design recommendations are provided for key parameters such as recompression split ratio and generator cavity pressure across varying power scales.

Multi-objective optimization↗
DOE OSTI2026

Virtual refrigerant charge sensing algorithm for residential CO₂ heat pumps

Natural refrigerants are increasingly adopted in next-generation heat pump systems, among which CO₂ heat pumps have attracted significant attention. However, due to their high operating pressures, the leakage risk is higher, resulting in undercharge conditions and degraded heat pump performance. Thus, developing an accurate refrigerant charge level detection technique is necessary to guarantee safe and efficient operation. Although virtual refrigerant charge (VRC) level calculation algorithms for CO₂ heat pumps exist, they typically rely on empirically selected features without a systematic selection framework, leading to multicollinearity and potential overfitting, which limit their prediction accuracy and generalizability. To address these issues, this study proposes a VRC algorithm framework with a systematic feature selection method that identifies physically meaningful and statistically significant features, and is applied using a residential CO₂ heat pump as a case study. The method is extended from previous work on conventional refrigerants to account for charge behavior in CO₂ gas coolers. The selected features include gas cooler outlet density, evaporator pressure, and superheat temperature. The results demonstrate that the proposed feature selection method significantly improves prediction accuracy compared to existing VRC approaches. A relatively small training dataset (∼30 samples) is sufficient for feature identification and model development. The developed algorithm achieves less than 3% prediction error under both undercharge and overcharge conditions, representing reductions of 46.7% and 35.3% compared to two recent reference VRC algorithms for transcritical CO₂ heat pumps reported in the literature. The proposed algorithm and feature selection method enhance leakage detection capability, facilitate the deployment of CO₂ heat pump systems, and contribute to reduced energy waste and maintenance costs.

Guo, Fangzhou [Lawrence Berkeley National Laborato↗
DOE OSTI2021

Nuclear data for reactor production of 131 Ba and 133 Ba

The newest radioisotope for brachytherapy treatment of prostate cancer is 131 Cs (t 1/2 = 9.69 d, 100% EC). Generated via electron capture decay of 131 Ba (t 1/2 = 11.6 d, 100% EC), 131 Cs has been used in brachytherapy for prostate cancer since 2004. The 131 Ba parent is produced through neutron capture of enriched 130 Ba in a nuclear reactor. For large-scale production of 131 Ba, an accurate knowledge of production and burnup cross sections of 131 Ba are essential. Here, we report two group cross sections (thermal and resonance integrals) for 130 Ba and 131 Ba and a new measure of the half-life of 131 Ba. Targets consisting of milligram quantities of enriched 130Ba (~35%) were irradiated in Oak Ridge National Laboratory's High Flux Isotope Reactor at thermal and resonance neutron fluxes of (1.9–2.1) × 10 15 and (5.8–7.0) × 10 13 neutrons·cm -2 s -1 , respectively, for durations ranging from 3 to 26 days. In addition, cadmium covered samples of 130 Ba were irradiated for 1 hour at 12.6% full reactor power (10.7 MW). The yield of 131 Ba approaches a saturation value of ~60 GBq (~1.6 Ci) per mg of 130 Ba for 20 days irradiation at a thermal neutron flux of 1.8 × 10 15 n·s -1 ·cm -2 , with a thermal/epithermal ratio of ~30. Under the above experimental conditions, the two group cross sections of 130 Ba are 6.9 ± 0.5 b (thermal, σ 0 ) and 173 ± 7 b (resonance, I 0 ). These values represent the sum of cross sections to metastable and ground states of 131 Ba. For 131 Ba, the empirically measured thermal cross section is 200 ± 50 b assuming an I 0 /σ 0 of 10. This cross section is reported for the first time. Further, the half-life of 131 Ba was remeasured to be 11.657 ± 0.008 d. Lastly, this study also resulted in the co-production of 133 Ba (t 1/2 = 10.52 y, 100% EC). The experimental yield of 133 Ba is ~370 MBq (~10 mCi) per mg of 132 Ba (thin target) for one cycle irradiation in the High Flux Isotope Reactor, and measured two-group 132 Ba cross sections are 7.2 ± 0.2 b and 39.9 ± 1.3 b. These values also represent the sum of cross sections to metastable and ground states of 133 Ba.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Uranium integral fission product yields for a spectrally-shaped 14.1 MeV neutron source at the National Ignition Facility

This paper describes the experimental results for an energy tuning assembly created to modify the National Ignition Facility deuterium–tritium fusion neutron source into a notional thermonuclear and prompt fission neutron spectrum, which has applications in integral measurements, nuclear data benchmarks, and radiation effects on microelectronics. The Monte Carlo neutron transport utilized MCNP5 to estimate the ETA-modified fluence using the ENDF-B/VIII.0 and IRDFF-II continuous energy nuclear data libraries, and SCALE Sampler was used to estimate the systematic nuclear data covariance using ENDF-B/VII.1 and IRDFF-II in a 252-group structure. The experiment fielded eight activation foils and a highly enriched uranium sample. This provided fifteen reaction channels that were used in a forward-fit comparison to the modeled results and to unfold the neutron spectrum using STAYSL. Gamma-ray spectrometry was performed on the activation and highly enriched uranium foils, and the reduced Χ 2 between the modeled and experimental values was 1.21. The results from the STAYSL unfold, reduced Χ 2 = 1.62, indicated that the modeled neutron spectrum was achieved and the systematic nuclear data uncertainty associated with the neutron transport and activation product cross sections was representative of the experiment. Integral cumulative fission product yield data were collected for 37 mass chains with a combination of gamma-ray spectrometry and radiochemical analysis. Fission product analysis was generally in agreement with two models using a semi-empirical fit and the General Observables of Fission code, with the exception of mass chains 88, 109, 111, 112, 113, 129, 139, 142, 144, 151, and 156.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Nuclear excitation functions for medical isotope production: Targeted radionuclide therapy via nat IR$(d, x)$ 193m Pt

193m Pt is an Auger emitting radionuclide which may have therapeutic potential, particularly when labeled to the chemotherapeutic drug cisplatin. One challenge to broader explorations of its clinical potential is the need for production routes with high specific activity. As part of a larger campaign to address gaps in reaction data for emerging medical radionuclides, this work seeks to characterize the nat Ir(d,x) reactions as a potential production pathway for 193m Pt. A stacked target irradiation, consisting of natural iridium, iron, nickel, and copper foils, was performed using a 33 MeV deuteron beam at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron. This measurement, along with previous experimental data, suggests an energy window between 11 to 18 MeV to maximize the production and radiopurity of 193m Pt. This experiment has yielded cross sections for 43 channels of deuteron-induced reactions from threshold to 30 MeV, including the first experimental results of nat Ir(d,x) 188m1+g,190m1+g Ir (cumulative), nat Ni(d,x) 56,57,58 m,58g Co (independent), nat Cu(d,x) 61 Co (cumulative) and nat Fe(d,x) 53 Fe, 48 V (cumulative). The results were compared with literature data, the TENDL-2023 database, and default theoretical calculations from the TALYS-2.04, CoH-3.6.0, EMPIRE 3.2.3, and ALICE-2020 reaction modeling codes. Here, this work presents another example of the lack of predictive capabilities for this set of modern nuclear-reaction modeling codes, and highlights the unsatisfactory modeling of experimental cross sections. Experimental data are important to improve the codes in general, and new experimental results can be used to improve the models. Finally, this measurement has revealed the need for an updated evaluation of the nat Cu(d,x) 63 Zn deuteron monitor reaction.

193mPt↗
DOE OSTI2024

Neural architecture search via similarity adaptive guidance

Evolutionary neural network architecture search (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures based on the target task. The current search space for ENAS is not to design a fully structured network, but to search for smaller cell architectures to reduce search costs. However, blind search strategies do not effectively utilize the potential experience of the population. In order to utilize the potential experience learned by the current population to guide the evolutionary search of the population, we propose a similarity guided neural network architecture search algorithm based on cell architecture, which utilizes the similarity between pairwise architectures in the population as empirical knowledge learned by the population. Our proposed algorithm provides a novel method for calculating architecture similarity, which calculates architecture similarity separately from the cell and macro-structure. Then we decouple the connections and operations in the cell and calculate connection and operation similarity separately. In addition, we propose adaptive similarity selection and binary tournament selection strategies to enhance the algorithm’s global and local search capabilities and effectively explore the search space. Finally, we design an improved single-point crossover operator to enhance the local search ability of the evolutionary operator. The experimental results show that SAGNAS is a competitive algorithm that achieves 97.44% and 81.60% in CIFAR10 and CIFAR100 with only 1.9 GPU-days spent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m 2 ), 10.0 W/m 2 , 11.1 W/m 2 , 7.1 W/m 2 , and 4.7 W/m 2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m 2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. Here, the comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗