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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

NRAP-open-IAM: A flexible open-source integrated-assessment-model for geologic carbon storage risk assessment and management

Large-scale implementation of geologic carbon storage (GCS) to help reduce atmospheric greenhouse gas emissions requires stakeholder confidence that injected CO2 will remain contained and that potential subsurface environmental risks are acceptably small and manageable. The U.S. Department of Energy’s National Risk Assessment Partnership (NRAP) has developed an open-source integrated assessment model (NRAP-Open-IAM) to help address questions about a potential GCS site’s ability to effectively contain injected CO 2 and protect groundwater and other overlying environmentally sensitive receptors. NRAP-Open-IAM allows a user to: (1) incorporate relevant site geologic and injection scenario data; (2) characterize important site features and events;(3) couple fast prediction models of various system components of the engineered geologic system; and (4) execute stochastic, dynamic simulation of whole GCS system performance, leakage risk assessment, and uncertainty quantification. NRAP-Open-IAM is available on GitLab (https://gitlab.com/NRAP/OpenIAM), and is accompanied by multiple application examples and detailed user and developer guides.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Insights from a coupled thermo-hydro-mechanical analysis of a layered high-temperature thermal energy storage reservoir

Coupled thermal-hydraulic-mechanical (THM) modeling is applied to investigate the performance of a seasonal high-temperature aquifer thermal energy storage operation based on data and conditions from current site investigations at the Geostorage Forsthaus pilot project in Bern (Switzerland). The model includes subhorizontal sand lenses of various lengths and dips that are embedded in a low permeability clay matrix. Thermal energy storage is simulated by seasonal injection and withdrawal of hot (up to 90 °C) water from a main well, with reservoir pressure regulated by two auxiliary wells at a distance of about 70 m from the main well. The results show how targeted injection into deeper permeable storage formations, along with active deep well pressure control, can effectively minimize geomechanical impact and the potential risk of damaging subsurface storage and sealing formations, or even surface facilities. With such pressure control, the subsurface mechanical responses are dominated by thermal strain and stress, which can be monitored with subsurface fiber optics. The study demonstrates how coupled THM modeling can be applied for the design of a safe and efficient thermal energy storage operation, and how subsurface fiber optic monitoring can be applied for performance confirmation, allowing for more confident operational forecasting.

Rutqvist, Jonny↗
DOE OSTI2025

Risk of Mortality in Family Members of Men Seeking Fertility Assessment

Objective: To assess mortality in family members of men seeking fertility assessment. Subfertility serves as a biomarker for overall somatic health, and poor semen quality is associated with increased risk of hospitalization and mortality from chronic conditions. However, it is unclear if these risks extend to family members of men with low sperm count. Design: Retrospective cohort study. Subjects: Family members, up to third-degree relatives, of men in the Subfertility, Health and Assisted Reproduction and the Environment cohort who underwent a semen analysis as part of a fertility assessment 1996–2017. Relatives of men with a recorded total sperm count who lived in Utah for ≥1 year 1904–2017 were included in the analysis (N = 22,280 families). Exposure: Individuals were classified by family membership. Families were classified as relatives of azoospermic (0M), oligozoospermic (<39M), or normozoospermic (≥39M) men. The average total sperm count of the proband (male relative) with fertility assessment was also included as a continuous exposure measure. Main Outcome Measures: The main outcomes were all-cause and cause-specific mortality risk by sex, age, and degree of relation: first-, second-, and third-degree. Cox proportional hazard models were used to test the association between fertility classification and mortality, controlling for sex, race/ethnicity, and birth year. Results: A total of 666,437 relatives of men with fertility assessment (N deaths = 183,974) were included in the analysis. Relative to normozoospermia families, all-cause mortality risk increased in oligozoospermia families (hazard ratio [HR] oligozoospermia , 1.03; 95% confidence interval [CI], 1.01–1.05). Close relatives, first- (HR oligozoospermia , 1.17; 95% CI, 1.07–1.28) and second-degree relatives (HR azoospermia , 1.11; 95% CI, 1.04–1.20; HR oligozoospermia , 1.05; 95% CI,1.01–1.09), of azoospermic and oligozoospermic men had the highest all-cause and cause-specific mortality risk, including death attributed to cardiovascular disease or congenital birth conditions. Conclusion: Our results suggest that familial all-cause and cause-specific mortality risk differ by fertility phenotype. Families of azoospermic and oligozoospermic men showed significantly increased risk, particularly for close relatives. This study provides further evidence that shared genetic and/or environmental factors could influence both fertility and somatic health.

Male fertility↗
DOE OSTI2020

Predicting the potential for mineral scale precipitation in unconventional reservoirs due to fluid-rock and fluid mixing geochemical reactions

Mineral precipitation within hydraulically fractured shale may affect fluid flow pathways and impact longterm hydrocarbon production. The ability to predict geochemical reactions that lead to problematic mineral precipitation will lead to active reservoir management strategies to improve overall production. Using the Marcellus Shale as a case study, a combination of laboratory experiments and reaction path modeling was applied to determine which reactions are likely to occur upon introduction of hydraulic fracturing fluid into the shale reservoir. Experimental results indicate that contact between fracturing fluid and shale will result in dissolution of primary minerals (quartz, feldspars, kaolinite, chlorite, pyrite) and secondary mineral precipitation over time periods of less than one week. Precipitation of barite, Fe-oxides, feldspars, amorphous silica and clay is likely to occur within the reservoir during shut in and early flowback due to mixing between fracturing fluid and reservoir brine as based on modeling saturation indices using experimental fluid data. Reaction path modeling of the experimental scenarios corroborates the specific dissolution and precipitation reactions observed experimentally. Comparison of the results to injected and produced waters from a Marcellus Shale well pad in Greene County, PA, USA, shows that the mineral reactions occur during the hydraulic fracturing, shut in, and early flowback periods. The results presented in this paper demonstrate the value in applying experimental approaches to identify mineral precipitation/dissolution reactions that may significantly impact reservoir performance. The good agreement between geochemical models and experimental results provides confidence that numerical models can be applied to screen the potential fluid-mineral and fluid-mixing reactions in unconventional reservoirs that result in undesired mineral scale precipitation.

58 GEOSCIENCES↗
DOE OSTI2021

Gas hydrate deposit formation in transient flowloop tests and mitigation with a surface treatment

Thermodynamic and thermal mitigation strategies are traditionally utilized to prevent gas hydrate formation but may be rendered ineffective for certain unplanned, extended shut-in scenarios. Unfortunately, the current knowledge regarding transient gas hydrate formation has been extremely limited to only a few studies and unable to address this complex issue adequately. This work, conducted in a lab-scale flowloop, aims to add to the understanding of this area by delving into the implications of transient conditions on gas hydrate formation mechanisms, while also exploring a possible mitigation strategy of a surface treatment. Specifically, the effects of phase separation on gas hydrate formation locations, transportability, and plugging are presented for an oil-dominated system. An omniphobic surface treatment is shown to affect initial gas hydrate deposition and its wall adherence strength, as well as the overall plugging outcome. Overall, this work combines observational information with process data measurements to provide a better understanding of phase separated gas hydrate systems and passive deposition mitigation strategies. This can further aid advanced gas hydrate modeling efforts, which currently do not adequately address transient flow effects on phase separation/dispersion. Here, it offers representative gas hydrate testing for coatings, which have exclusively been limited to non-flowing and/or bench-scale analysis, to give confidence in their potential application as advanced deposition prevention tools.

03 NATURAL GAS↗
DOE OSTI2021

Inter-well connectivity detection in CO 2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. This study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

42 ENGINEERING↗
DOE OSTI2022

Synthetic aromatic kerosene property prediction improvements with isomer specific characterization via GCxGC and vacuum ultraviolet spectroscopy

This research explores an advanced method of fuel composition determination and builds upon typical hydrocarbon group type analyses performed with two-dimensional gas chromatography (GCxGC). In this study, structural information of individual species within Virent’s Synthetic Aromatic Kerosene (SAK) is identified by vacuum ultraviolet (VUV) spectroscopy. By mass, 71.3% of the components elute within six peaks of the chromatogram, from which 12 unique species are identified through a novel deconvolution method. Overall, the identification of 93.6%m across 26 structural isomers is made by the methods described in this work. With 93.6%m ascribed to specific isomers, the precision of fuel property predictions improves dramatically. For example, the absolute error of the viscosity prediction is reduced by 90% because of this advancement in diagnostic capability, and its 95-percentile confidence interval (precision only) is reduced by 93%. Additionally, the properties of SAK, blended with hydro processed esters fatty acids (HEFA), are demonstrated to have blended properties consistent with conventional jet fuel.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Position-specific carbon isotopes of Murchison amino acids elucidate extraterrestrial abiotic organic synthesis networks

The Murchison meteorite is a well-studied carbonaceous chondrite with relatively high concentrations of amino acids thought to be endogenous to the meteorite, in part because they are characterized by carbon isotope (δ 13 C) values higher than those typical of terrestrial amino acids. Past studies have proposed that extraterrestrial amino acids in the Murchison meteorite could have formed by Strecker synthesis (for α-amino acids), Michael addition (for β-amino acids), or reductive amination, but a lack of constraints have prevented confident discrimination among these possibilities, or assignment of specific formation pathways to each of several specific amino acids. Position-specific carbon isotope analysis differentiates amongst these mechanisms by relating molecular sites to isotopically distinct carbon sources and by constraining isotope effects associated with elementary chemical reactions. Prior measurements of the position-specific carbon isotopic composition of α-alanine from the Murchison CM chondrite demonstrated that alanine’s high δ 13 C VPDB value is attributable to the amine carbon (δ 13 C VPDB = +142 ± 20‰), consistent with Strecker synthesis drawing on 13 C-rich carbonyl groups in precursors (Chimiak et al., 2021). Here, we measured the δ 13 C composition of fragment ions generated by electron impact ionization of derivatized α-alanine, β-alanine, and aspartic acid from Murchison via gas chromatography-Fourier transform mass spectrometry. α-Alanine’s amine carbon yielded δ 13 C VPDB = +109 ± 21‰, which is consistent with the previously measured value and with formation from 13 C-rich precursors. β-Alanine’s amine carbon presents a lower δ 13 C VPDB = +33 ± 24‰, which supports formation from 13 C-rich precursors but potentially via a Michael addition mechanism rather than Strecker synthesis. Aspartic acid’s amine carbon has δ 13 C VPDB = -14 ± 5‰, suggesting synthesis from precursors distinct from those that generated the alanine isomers. Further, these measurements indicate that Murchison amino acids are a mixture of compounds made from different synthesis mechanisms, though some subsets likely drew on the same substrates; this conclusion highlights the complexity of extraterrestrial organic synthesis networks and the potential of emerging methods of isotope ratio analysis to elucidate the details of those networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Temporal relationships among lunar crustal rocks

Temporal relationships among the three most common suites of lunar crustal rocks have been investigated by obtaining new high precision ages on Felsic/Alkali-suite Quartz monzodiorite Clast B from breccia 15405 and Magnesian-suite norite 78235/6/8/55/56 and comparing them to previously dated ferroan anorthosite sample 60025. The weighted average age of 4337.19 ± 0.49 Ma of 15405 Clast B is defined by zircon U-Pb and Pb-Pb ages as well as mineral isochron Sm-Nd and Nd-Nd ages. It is identical to the weighted average age for Apollo 17 norite 78235/6/8/55/56 of 4334.1 ± 3.5 Ma which is defined by Pb-Pb ages measured on baddeleyites in this investigation and less precise Pb-Pb and Sm-Nd ages reported in the literature. Both ages are ∼ 25 Ma younger than the weighted average of Sm-Nd and Pb-Pb ages reported in the literature on ferroan anorthosite 60025 of 4359.3 ± 2.3 Ma. The fact that ages of all three samples are defined by multiple U-Pb, Pb-Pb, Sm-Nd, and 142 Nd- 143 Nd chronometers provide confidence that they record the igneous crystallization history of the samples and do not represent disturbances or mixing lines with no temporal significance. Here, the extent to which these three ages represent broader scale magmatism is difficult to evaluate. Nevertheless, the age defined for 15405 Clast B, 78235/6/8/55/56, and 60025 are contemporaneous with the peak of ages observed in detrital zircons from the Apollo 12, 14, 15, and 17 landing sites (4340 ± 20 Ma), a Mg-suite Sm-Nd whole rock isochron defined by samples from Apollo 14, 15, 16, and 17 landing sites (4348 ± 25 Ma), and a Ferroan Anorthosite-suite Sm-Nd whole rock isochron defined by samples from the Apollo 15 and 16 landing sites (4354 ± 29 Ma). This implies that Ferroan Anorthosite-suite magmatism is temporally distinct and earlier than magmatism associated with the Mg-suite and the Felsic/Alkali-suite, as predicted by the lunar magma ocean model of lunar differentiation. The short 35 ± 10 Ma interval between primary ferroan anorthosite magmatism and secondary magmatism suggests that the lunar crust formed over a limited period of time. Although heat from decay of long-lived isotopes, large impacts, tidal heating associated with interactions between the Earth and Moon, and density driven overturn of the magma ocean have all been invoked to explain production of ancient secondary crustal magmatism, only tidal heating and cumulate overturn are consistent with the apparent short duration of secondary crustal magmatism and the great depth of crystallization implied for some Mg-suite samples. The initial ε 143 Nd values derived from the 15405 Clast B and 78238 Mg-suite norite isochrons, as well as a Mg-suite whole rock isochron are −0.23 ± 0.11, −0.27 ± 0.74, and −0.25 ± 0.09, respectively. They are identical within uncertainty indicating that Mg-suite and Felsic/Alkali-suite magmas were derived from materials that had the same time averaged Sm/Nd ratios since the formation of the solar system. This, combined with the contemporaneous nature of 15405 Clast B and 78235/6/8/55/56 Mg-suite norite, is consistent with evolution of both samples, and likely both magma suites, from a common source through closed system fractional crystallization or partial melting processes.

Chronology↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Comparing modelling approaches for a generic nuclear waste repository in salt

This paper contains a comparison of five modelling approaches for a simplified nuclear waste repository in a domal salt formation. It is the result of a four-year collaboration between five international teams on Task F of the DECOVALEX-2023 project on performance assessment modelling. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. This work demonstrates how these objectives are accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Participating teams made a wide range of model assumptions, ranging from compartmentalized networks to full 3D models of the salt formation and repository. Despite differences in the modelling strategies, all models indicate that salt compaction and diffusion of radionuclides in brine are key processes in the repository. For the isothermal spent nuclear fuel and vitrified waste scenario with multiple early failures considered, all models indicate little of the disposed radionuclides will migrate beyond the repository seal over the 100,000-year simulations. In general, the model output quantities have the largest differences over the short term and near the waste. Disparities between the models are believed to be due to differing simplifications from the conceptual model.

DECOVALEX↗
DOE OSTI2025

Benchmark study of a new simplified DFN model for shearing of intersecting fractures and faults

It is challenging to quantitatively predict shearing of intersecting fractures/faults because of dynamic frictional contacts accompanied by possible nonlinear rock deformation. To address such challenges, a new conceptual model—the simplified DFN model—was proposed and validated by Hu et al. 46 to use major paths (MPs) to represent complicated DFNs for calculation of shearing. In this work, we conducted a benchmark study for three examples that involve different levels of complexity of intersecting fractures, and correspondingly different numbers of MPs. The codes and software that were used in the benchmark cover a range of continuum, discontinuum and hybrid numerical methods: NMM (LBNL), FLAC3D (LBNL), GBDEM (KIGAM), FRACOD (DynaFrax), and CASRock (CAS). The general consistency between DFN and MP cases as predicted by all the codes/software demonstrates that major paths can be used to simplify the geometry of DFNs in a wide range of software. Disagreement in results made by some software and potential future improvements are discussed. We show that (1) shearing of one or multiple major fractures can be reduced if there are multiple smaller intersecting fractures in that area, which is a useful basis for understanding and controlling induced seismicity and merits further analysis, and (2) the agreement achieved in the benchmark examples provide confidence that the simplified DFN model is a promising conceptual model that can be used for different types of numerical approaches and software for simplifying the analysis of the shearing of intersecting fractures and faults.

58 GEOSCIENCES↗
DOE OSTI2024

Modeling the impact of high thermal conductivity paper on the performance and life of power transformers

Degradation of insulation paper is a key contributor to the failure of power transformers. Insulation degradation accelerates at elevated temperatures, which highlights the potential for better thermal management to prolong life. While several studies have analyzed the benefits of high thermal conductivity oil for reducing temperatures inside a transformer, this study is an initial assessment of the benefits of high thermal conductivity paper on transformer life. Blending particulates with cellulosic fibers offers a pathway for high thermal conductivity paper (with good dielectric properties), which can reduce internal temperatures. Presently, life extensions that can be achieved by the use of such thermally conducting papers were estimated, with the thermal conductivity of the paper being the key parameter under study. The analytical-numerical thermal model used in this study was validated against experimental measurements in a distribution transformer, adding confidence to the utility of the model. This model was then used to provide estimates of hot-spot temperature reduction resulting from the use of papers with higher thermal conductivity than baseline. Transformer life was predicted conventionally by tracking the degree of polymerization of paper over time, based on an Arrhenius model. Results indicate that increasing the thermal conductivity of paper from 0.2 W/mK (baseline) to 1W/mK reduces the hot spot temperature by 10°C. While degradation significantly depends on the moisture and oxygen content, the model shows that such a temperature reduction can increase life for all conditions, by as much as a factor of three.

42 ENGINEERING↗
DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Generalized fiducial inference on differentiable manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.

97 MATHEMATICS AND COMPUTING↗