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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of Annealing-Induced Phase Segregation in Composite Silicon Anodes for Li-ion Batteries

Silicon (Si) anodes present a promising alternative to graphite anodes for lithium-ion batteries (LIBs), as Si has a greater specific capacity. However, one major constraint is the volumetric change of Si during lithiation that results in an unstable solid-electrolyte interphase (SEI), so Si-containing electrodes require the use of various strategies to mitigate these effects and improve performance. One such solution is the use of Si nanoparticles (NPs) that maximize the surface area to volume ratio. Here, we discuss electrodes made with Si NPs treated with polyethylene oxide (PEO) (for improving dispersion during processing), conductive carbon NPs, and P84 polyimide binder which shows significant impacts of annealing treatment on improvements of active material utilization, first cycle efficiency, and capacity retention with extensive cycling . We used air-free argon ion polishing to create electrode cross sections and imaged through the electrode thickness using atomic force microscopy (AFM)-based nano-electrical characterization of scanning spreading resistance microscopy (SSRM), nano-mechanical characterizations of contact resonance and force volume (CR-FV), and scanning electron microscopy-based energy dispersive x-ray spectroscopy (SEM-EDS). Results show that the Si and conductive carbon segregate into phases with a distinctive carbon-rich banded morphology that surrounds the Si-rich phase during annealing. In pristine electrodes, the carbon- and SEI-rich bands exhibit a higher electronic conductivity and a lower elastic modulus than the Si active material phase. These structures, as well as distinct electronic and mechanical properties, remain during cycling, suggesting an improvement of electrical conduction pathways and a mechanical strain buffer for active Si material expansion during cycling. This phase separation may be a major factor in the improvement seen in electrochemical performance due to annealing. Additionally, our nm-scale and multi-mode characterizations provide a novel route for understanding and improving energy storage devices, which is advantageous due to the highly inhomogeneous of composite electrodes in nm-mm scales.

ENERGY STORAGE↗
DOE OSTI2023

The Deimos Experiment: Advanced Reactor Testbed

Advanced reactor initiatives are growing significantly through programs nationwide. This research area includes small modular reactors, microreactors, and space reactors. Many of the reactors being designed are untested concepts. They include unique moderators, varying fuel types, high temperatures, and compact configurations. The shift in fuel type, from highly enriched uranium (HEU) to high assay, low enriched uranium (HALEU), is particularly important as it has driven many of the other changes. For example, lower enrichment requires advanced moderators, which in turn require different reflectors to make the systems compact. The change in materials including the transition from HEU to HALEU affects the temperature feedback of the systems. Additionally, these advanced reactor concepts generally have a thermal neutron spectrum in contrast to earlier fast spectrum advanced reactor. With the extensive changes from previous reactor designs, validation experiments are needed. The National Criticality Experiments Research Center (NCERC) is uniquely equipped to perform such experiments. The Deimos experiment, designed for execution at NCERC, will serve as a testbed for advanced reactor concepts. It will use HALEU fuel in a graphite matrix, provide the ability to use advanced moderators, and allow measurements of temperature reactivity coefficients (TRCs).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
DOE OSTI2023

AGR-5/6/7 Capsule 1 Temperature Re-analysis_Grant Hawkes_Final

Slides for Gas-Cooled Reactor AGR-5/6/7 Thermal Model with Non-Uniform Gas Gaps. Includes investigation of offset through modeling and experimental results, ABAQUS finite element model created for the entire test train, Capsule 1 information, Three irradiation dates to compare the thermal model with the working thermocouples (TCs), Offset distances and direction of offset for the top and bottom of the graphite holder with computer runs, determination of offset by the root mean square error (RMSE) between the TCs and model predtictions, and the results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

AGR 34 Analysis Progress Presentation

A review of analysis work done on the AGR 3/4 experiment transport profiles July 2022-July 2023. Graphite ring modeling and transport barriers discussed. Powerpoint presentation

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Gas-Phase Composition as a Predictive Metric for Calendar Life Behavior of Next-Generation Silicon Anodes

The expansion of renewable technologies and electrification of the transportation sector is driving increased demand for next-generation battery materials that provide higher power and energy density with superior cycling and calendar life stability. Silicon (Si) has a theoretical capacity nearly 10x that of graphite, and is therefore a promising anode material candidate to meet these rigorous performance demands. While leading Si anode battery demonstrations are approaching target metrics for cycle life, a series of complex and interrelated modes of reactivity lead to reduced calendar life and therefore challenge practical adoption of these materials. Deconvoluting the degradation processes that impact Si calendar life is critical to informing the rational and accelerated design of improved Si materials. In the present work, we employ novel sampling techniques and GC-MS-FID characterization to measure gas-phase composition during initial Si cycling, which we tie to selective mechanisms of Si passivation. We utilize a tiered analysis approach to identify and quantify the gas-phase reaction products associated with three advanced Si material candidates under practical operating conditions. Ex situ analysis of Si powders (pure chemical reactivity) is coupled with nondestructive in situ sampling of Si electrodes in a practical pouch-cell format (coupled chemical and electrochemical reactivity). We link the observed gas-phase species evolution to electrochemical behavior and measured calendar life of the three Si materials. Further, we evaluate the voltage-resolved evolution of gas-phase species for one such Si nanomaterial, where nonmonotonic gas generation implies competition between passivating reaction pathways. The measured gas-phase compositional data serves as a critical input for our advanced electrochemical SEI models to identify favorable vs unfavorable reaction pathways to stabilize Si. In addition to bolstering a fundamental understanding of Si reactivity, the present approach informs specific and quantifiable gas-phase metrics tied to calendar life improvements in Si, which can streamline and accelerate the process of next-generation material development.

DIRECT ENERGY CONVERSION,ENERGY STORAGE↗
DOE OSTI2023

Building Environmental Justice and Equity into the Development of Critical Mineral Industries

The global transition toward low-carbon energy not only means an increase in demand for clean electricity and renewable resources, but also an increase in demand for the critical minerals (CMs) and rare earth elements (REEs) that these technologies rely upon. Indeed, low-carbon energy technologies, such as those used for wind turbines and electric vehicle motors, require significantly more lithium, nickel, cobalt, manganese, graphite, and other CMs than fossil-based energy technologies. The growing demand for new forms of low- carbon energy will necessitate a proportional scale-up of CM and REE extraction and processing. Presently, the majority of extraction and processing activities for CMs and REEs are concentrated in very few countries, primarily in China and the Global South. CM and REE supply chain activities conducted in or controlled by these countries are commonly associated with widespread and well- documented human rights abuses and environmental degradation. As a result of concerns related to supply chain security, worker welfare, and the overall need for additional sources of CMs and REEs, governments worldwide have begun to explore policy pathways for the development of new supply chains. The development of new supply chains for CMs and REEs re s a multifold opportunity to accelerate the global deployment of low-carbon energy fleets, reduce global carbon emissions, build political resilience—and also to enhance policy objectives of environmental justice and equity in the clean energy transition. Fulfilling these policy objectives requires energy producers to navigate a maze of global supply chain policies designed to shape and accelerate the growth of new markets. In the United States, for instance, President Biden recently implemented fiscal and trade policies that incentivize both domestic and global reliance on U.S.-produced CMs and REEs, leveraging key relationships in Asia and Europe to ensure the accelerated buildout of the U.S. supply chain. The Biden Administration’s framework also emphasizes that new supply chains for CMs, REEs, and other low-carbon energy pathways must generate benefits for marginalized and disadvantaged communities, build energy equity, and contribute to the Biden Administration’s vision of environmental justice. present CM and REE supply chains include several stages, including mining, processing, transport, utilization, and disposal, each of which involves different environmental justice considerations and potential injustices. This study explores how technical innovation, paired with responsible community engagement and empowerment, can help inform the development of energy equity and environmental justice at all stages of new supply chains. The study highlights these opportunities on a broad scale. We also lend a specific focus to research at the University of Wyoming School of Energy Resources that aims to guide the development of CM and REE industries in communities with high economic dependence on coal and other fossil fuel industries, identifying pathways to grow a U.S. domestic supply chain by producing CMs from coal, coal by-products, and coal waste streams. These production pathways represent a potential new supply for CMs across the U.S. and elsewhere, thereby enhancing national security and accelerating the widespread deployment of low-carbon energy technologies, while also generating alternative applications for remaining coal reserves and aiding in a just transition for rural energy-producing communities.

Gerace, Selena↗
DOE OSTI2023

AGR TRISO Fuel Fission Product Release Data Summary

Knowledge of fission product retention in and release from TRISO fuel under normal and off-normal conditions is needed for reactor safety analyses. This is important for coated-particle fuel used in high-temperature reactors relying on the functional containment strategy. Data on the release and retention of key fission products (e.g., Ag-110m, Cs-134, Eu-154, and Sr-90) in AGR UCO TRISO fuels have been summarized in this report, and empirical relationships with respect to time and temperature were developed. This included fission product accumulation in the OPyC and compact graphitic matrix during irradiation, release from compacts during irradiation, and release during post-irradiation safety testing at temperatures from 1600-1800°C. The frequencies of SiC failure and TRISO coating failure from irradiation and post-irradiation safety testing were also summarized as they have bearing on the quantities of Cs release from the fuel. In a forthcoming publication, a framework for combining and using these empirical relationships as part of a source term analysis will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

AGR-5/6/7 Thermal Model with Non-uniform Gas Gaps

Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tri-structural isotropic fuel particles. In the absence of direct measurement, fuel compact temperatures were calculated using a three-dimensional finite element thermal model, which is subject to simulation uncertainty. The most dominant factor in the uncertainty of calculated fuel temperatures is the gas gap uncertainty due to the nub-to-shell clearance caused by a design error of AGR-5/6/7 capsules. The thermal model was revised to examine the most probable graphite offset position for six different days during the irradiation for Capsules 1 and 2. The analysis varied the offset distance and azimuthal direction at both the top and bottom of the holder. The best-fit offset was estimated based on the minimum root mean square error of the residuals (measured minus calculated) for the operational thermocouples (TCs). From these results, the following conclusions were made: (1) The holder offsets led to slightly lower average temperatures but wider temperature variations (lower minimum and higher peak fuel temperatures) for both Capsule 1 and Capsule 2. (2) During earlier cycles (162A–164B), when numerous TCs were still operational, the best-fit offset distance varied over a specific range for both the top and bottom ([0.002–0.0035 in.] for Capsule 1 and [0.003-0.004 in] for Capsule 2). In contrast, the offset azimuthal direction varied widely, especially for the offset at the bottom of the Capsule 1 holder. This is because holder movement was somewhat constrained at the top of Capsule 1 by the TC leads running through the capsule head and into the holder and by the through tubes in Capsule 2, but the Capsule 1 bottom did not have this type of constraint. (3) During later cycles, when all TCs failed, applying the maximum possible offset of 0.006 in. to the northwest direction for both the top and bottom resulted in a calculated peak fuel temperature of 1557? in Capsule 1 (i.e., a 135? increase from 1422? with zero offset on September 20, 2019 (166A)); the maximum offset of 0.0068 in. to the south for both top and bottom resulted in a calculated peak fuel temperature of 1110°C in Capsule 2 on April 20, 2020 (i.e., a 116? increase from 994? with zero offset (168A)). High peak fuel temperatures in Capsule 1 during Cycle 166A could be the cause of massive particle failure near the end of this cycle. (4) Even though the highest temperature at the tip of Type-N TCs, such as TC-1-7, slightly exceeded 1000?, the temperature along the TC wire reached as high as 1335? assuming an offset of 0.006 in. in the northwest of Capsule 1 holder near the end of Cycle 166A. This temperature significantly exceeds the temperature threshold at which TC degradation is expected to occur, ultimately contributing to considerable particle failures in Capsule 1. For eight Type-N TCs in Capsule 2, the peak TC line temperature was much lower (i.e., 1029°C for TC-2-5), assuming maximum offset of 00068 in. to the south during cycle 168A.

advanced gas reactors↗
DOE OSTI2023

Modeling The DLOFC Accident Scenario of HTR-PM Equilibrium Core Using NEAMS Tools

High-Temperature Gas-cooled Reactors (HTGRs) have excellent characteristics in terms of safety and high thermal efficiency, and they are gaining a large interest from the industry as a candidate of Gen-IV reactors for a wide range of applications. The High Temperature gas-cooled Reactor Pebble-bed Module project (HTR-PM) is one of these designs and where helium gas is used to cool the pebble-bed region that consists of spherical fuel elements moderated with graphite. The HTR-PM design is based on the combined experience from the German pebble-bed reactor program from the 1960s through the 1990s and the HTR-10 experience in China during the 2000s. Idaho National Laboratory has a long experience in modeling of HTGRs working in developing neutronics and thermal hydraulics tools for the proper modeling of these reactors. The neutronics code Griffin has the capability to model pebble depletion . While the thermal hydraulics code Pronghorn was developed mainly to model the pebble bed reactors with the porous media assumption. In this work, an equilibrium core Multiphysics model was developed for the HTR-PM reactor to analyze the depressurized loss of forced cooling accident scenario (DLOFC). This paper is organized as follows: First, a brief description of the reactor and model specifications are provided. Then, the developed Multiphysics model is discussed. Finally, verification results of the steady-state equilibrium core and DLOFC accident are presented followed by a summary of the conclusions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

AGR-5/6/7 Thermal Model with Non-uniform Gas Gaps

Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tri-structural isotropic fuel particles. In the absence of direct measurement, fuel compact temperatures were calculated using a three-dimensional finite element thermal model, which is subject to simulation uncertainty. The most dominant factor in the uncertainty of calculated fuel temperatures is the gas gap uncertainty due to the nub-to-shell clearance caused by a design error of AGR-5/6/7 capsules. The thermal model was revised to examine the most probable graphite offset position for six different days during the irradiation for Capsules 1 and 2. The analysis varied the offset distance and azimuthal direction at both the top and bottom of the holder. The best-fit offset was estimated based on the minimum root mean square error of the residuals (measured minus calculated) for the operational thermocouples (TCs). From these results, the following conclusions were made: (1) The holder offsets led to slightly lower average temperatures but wider temperature variations (lower minimum and higher peak fuel temperatures) for both Capsule 1 and Capsule 2. (2) During earlier cycles (162A–164B), when numerous TCs were still operational, the best-fit offset distance varied over a specific range for both the top and bottom ([0.002–0.0035 in.] for Capsule 1 and [0.003-0.004 in] for Capsule 2). In contrast, the offset azimuthal direction varied widely, especially for the offset at the bottom of the Capsule 1 holder. This is because holder movement was somewhat constrained at the top of Capsule 1 by the TC leads running through the capsule head and into the holder and by the through tubes in Capsule 2, but the Capsule 1 bottom did not have this type of constraint. (3) During later cycles, when all TCs failed, applying the maximum possible offset of 0.006 in. to the northwest direction for both the top and bottom resulted in a calculated peak fuel temperature of 1557? in Capsule 1 (i.e., a 135? increase from 1422? with zero offset on September 20, 2019 (166A)); the maximum offset of 0.0068 in. to the south for both top and bottom resulted in a calculated peak fuel temperature of 1110°C in Capsule 2 on April 20, 2020 (i.e., a 116? increase from 994? with zero offset (168A)). High peak fuel temperatures in Capsule 1 during Cycle 166A could be the cause of massive particle failure near the end of this cycle. (4) Even though the highest temperature at the tip of Type-N TCs, such as TC-1-7, slightly exceeded 1000?, the temperature along the TC wire reached as high as 1335? assuming an offset of 0.006 in. in the northwest of Capsule 1 holder near the end of Cycle 166A. This temperature significantly exceeds the temperature threshold at which TC degradation is expected to occur, ultimately contributing to considerable particle failures in Capsule 1. For eight Type-N TCs in Capsule 2, the peak TC line temperature was much lower (i.e., 1029°C for TC-2-5), assuming maximum offset of 00068 in. to the south during cycle 168A.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Stability margin analysis of the Holos-quad microreactor design

A simplified point model of the Holos-Quad microreactor is introduced. The model is based on the point kinetics equations coupled to three heat balance equations, representing the mean temperatures of the fuel particles, the graphite moderator and the helium coolant. The differential equations are converted to the frequency domain, enabling the construction of the closed-loop reactor transfer function. Using this function the stability margins of the core design is analyzed for various power levels. It is shown that the gain margins approaches infinity, demonstrating the stability of the reactor for all power levels. The phase margin at nominal power is about 60 degrees, however it shows a non-monotonous dependence on power, with minimal value obtained for about 10% of nominal power. As power level is further increased, the phase margin also increases, demonstrating the reactor becomes more stable. This behavior may be of high importance in load-follow scenarios, where the power level of the reactor changes with time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Multigroup cross section generation capability in GRIFFIN

GRIFFIN is an advanced reactor multiphysics application built on the object-oriented simulation environment (MOOSE) and is jointly developed by Idaho National Laboratory and Argonne National Laboratory. The cross section application programming interface, originally developed for the PROTEUS code, has been integrated into GRIFFIN to prepare cross sections for thermal reactor applications with heterogeneous geometries. Additional improvements have been made by implementing an on-the-fly slowing down method, a double heterogeneity treatment capability, and updating the procedure to generate the fine multigroup library. The cross section preparation capability in GRIFFIN was verified for graphite-moderated TRISO fuel-based reactor benchmark problems: unit-cell problems of VHTR and EMPIRE micro reactor and HTTR assembly problems. Eigenvalues and multigroup cross sections of GRIFFIN agreed very well with those of the continuous-energy Monte Carlo code Serpent2 within 200 pcm in eigenvalue and 2% in cross sections. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Development of the TREAT M2 experiment as a transient benchmark

A transient benchmark based on the Transient Reactor Test Facility (TREAT) M2 Calibration experiment (M2-CAL) is under development. TREAT, at Idaho National Laboratory, is a graphite moderated air-cooled research reactor which has been used extensively for fuel material testing under extreme and accident conditions. Accurate benchmark models are a beneficial component in the operations, experimental planning, and development of TREAT. In this work, we present a transient benchmark model for the M2-CAL experiment core loading. The benchmark model incorporates the coupling between the Monte Carlo Code SERPENT and the computational fluid dynamics code OpenFOAM to capture the temperature feedback mechanism. The M2-CAL transient 2580 was simulated in this work. A pre-transient analysis was performed to determine the optimum core composition and conditions before the beginning of the transient. The analysis was validated against historic TREAT kinetic measurements and the worth of the transient rod T-2. The axial power distribution in the flux wire for the M2-CAL experiment was determined and contrasted with the experiment. The model was then used to simulate the M2-CAL transient 2580 experiment based on the reported pre-transient and transient conditions. Several transient observables were calculated and compared to the experiment. The model shows good agreement with the experimental power traces, period, and average increase of power at the power ramp (time > 7.8 s). Inverse point kinetics analysis was performed during the period of the power ramp for the model and the experiment based on that, the temperature feedback component was isolated and contrasted with the experimental feedback. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Investigating the AGN-201M research reactor's unique dominance ratio

The AGN-201M reactor is a small, thermal spectrum reactor located at the University of New Mexico (UNM). It is moderated by polyethylene, reflected by graphite, with fuel comprised of uranium microspheres embedded in polyethylene plates that are separated by an aluminum baffle. The dominance ratio of a multiplying system is the ratio of the first higher-mode eigenvalue to the fundamental, k{sub 1}/k{sub 0}. It is a key parameter in characterizing the rate of convergence, analyzing system stability, and determining how tightly coupled the system's neutron fission is. The purpose of this paper is to investigate the system's unusually low dominance ratio of 0.632. This investigation varied the reactor's physical parameters, such as the fuel geometry, fuel density, and reflector thickness, and examined their impact on the reactor's dominance ratio. Additionally, this study examined neutronically similar systems in hopes of identifying common causes for systems with low dominance ratios. The reason why the dominance ratio of the reactor is small compared to larger thermal reactors was determined to be because of its significantly smaller geometry and the composition of the fuel plates. The reflector's effect on the dominance ratio is small in comparison to the other factors, but was found to have a non-zero effect. Furthermore, the AGN-201M was found to have a significantly lower dominance ratio than systems in which it shares a very high (c{sub k} >95%) degree of neutronic similarity.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Evaluating Heat Extraction Performance of Closed-Loop Geothermal Systems with Thermally Conductive Enhancements in Conduction-Only Reservoirs: Preprint

We investigated the impact of disc-shaped and linear thermal enhancements on performance of closed-loop geothermal systems using steady-state and transient COMSOL numerical simulations. Thermal enhancements refer to thermally conductive material introduced in the rocks surrounding a wellbore to compensate for the relatively low rock thermal conductivity and increase heat extraction. Materials proposed include composite cements with thermally conductive additives such as metals or graphite to obtain overall cement thermal conductivities of 10 to 100x the rock thermal conductivity. Our simulation approach was modeling a subsegment (e.g., 100 m long) of a wellbore with idealized (e.g., perfect disc-shaped) thermal enhancements, and we report results as relative increase in heat extraction with respect to systems without thermal enhancements. We did not evaluate technical feasibility or cost of installing thermal enhancements. Simulations indicate that for 5-mm thick disc-shaped thermal enhancements with 5-m radius, repeated every 1 m along the wellbore and with thermal conductivity of 100x the rock thermal conductivity, the thermal output increases roughly 20% with respect to a closed-loop system without thermal enhancements. For fishbone structure thermal enhancements, we estimate a roughly 7% increase in thermal output for 5-m long radially outward pipes with half the radius of the main wellbore, repeated every 2.5 m, with thermal conductivity of 100x the rock thermal conductivity.

advanced geothermal systems↗
DOE OSTI2024

Demonstrate and De-Risk an Internally Lined Tank Design for Molten-Salt Thermal Energy Storage Benefitting CSP and Nuclear-Energy Sectors

The LIMOSA project extends the Gen3 CSP "Liquid Pathway" project to evaluate 720 degrees C thermal energy storage based on molten chloride salts. While SETO selected the Particle Pathway for Gen3 demonstration, LIMOSA was created to resolve a major risk factor with molten chloride salts - the design of the salt storage tank. The project team designed and assembled a test system leveraging experience from Oak Ridge National Lab and the Liquid Pathway Phases 1 and 2. The key innovation in the project is development of a graphite-foil barrier to protect insulation from salt intrusion and wetting.

chloride salt↗