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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Heterogeneity, pore pressure, and injectate chemistry: Control measures for geologic carbon storage

Desirable outcomes for geologic carbon storage include maximizing storage efficiency, preserving injectivity, and avoiding unwanted consequences such as caprock or wellbore leakage or induced seismicity during and post injection. Here, to achieve these outcomes, three control measures are evident including pore pressure, injectate chemistry, and knowledge and prudent use of geologic heterogeneity. Field, experimental, and modeling examples are presented that demonstrate controllable GCS via these three measures. Observed changes in reservoir response accompanying CO 2 injection at the Cranfield (Mississippi, USA) site, along with lab testing, show potential for use of injectate chemistry as a means to alter fracture permeability (with concomitant improvements for sweep and storage efficiency). Further control of reservoir sweep attends brine extraction from reservoirs, with benefit for pressure control, mitigation of reservoir and wellbore damage, and water use. State-of-the-art validated models predict the extent of damage and deformation associated with pore pressure hazards in reservoirs, timing and location of networks of fractures, and development of localized leakage pathways. Experimentally validated geomechanics models show where wellbore failure is likely to occur during injection, and efficiency of repair methods. Use of heterogeneity as a control measure includes where best to inject, and where to avoid attempts at storage. Lastly, an example is use of waste zones or leaky seals to both reduce pore pressure hazards and enhance residual CO 2 trapping.

58 GEOSCIENCES↗
DOE OSTI2023

Environmental controls on observed spatial variability of soil pore water geochemistry in small headwater catchments underlain with permafrost

Abstract. Soil pore water (SPW) chemistry can vary substantially across multiple scales in Arctic permafrost landscapes. The magnitude of these variations and their relationship to scale are critical considerations for understanding current controls on geochemical cycling and for predicting future changes. These aspects are especially important for Arctic change modeling where accurate representation of sub-grid variability may be necessary to predict watershed-scale behaviors. Our research goal is to characterize intra- and inter-watershed soil water geochemical variations at two contrasting locations in the Seward Peninsula of Alaska, USA. We then attempt to identify the key factors controlling concentrations of important pore water solutes in these systems. The SPW geochemistry of 18 locations spanning two small Arctic catchments was examined for spatial variability and its dominant environmental controls. The primary environmental controls considered were vegetation, soil moisture and/or redox condition, water–soil interactions and hydrologic transport, and mineral solubility. The sampling locations varied in terms of vegetation type and canopy height, presence or absence of near-surface permafrost, soil moisture, and hillslope position. Vegetation was found to have a significant impact on SPW NO3- concentrations, associated with the localized presence of nitrogen-fixing alders and mineralization and nitrification of leaf litter from tall willow shrubs. The elevated NO3- concentrations were, however, frequently equipoised by increased microbial denitrification in regions with sufficient moisture to support it. Vegetation also had an observable impact on soil-moisture-sensitive constituents, but the effect was less significant. The redox conditions in both catchments were generally limited by Fe reduction, seemingly well-buffered by a cache of amorphous Fe hydroxides, with the most reducing conditions found at sampling locations with the highest soil moisture content. Non-redox-sensitive cations were affected by a wide variety of water–soil interactions that affect mineral solubility and transport. Identification of the dominant controls on current SPW hydrogeochemistry allows for qualitative prediction of future geochemical trends in small Arctic catchments that are likely to experience warming and permafrost thaw. As source areas for geochemical fluxes to the broader Arctic hydrologic system, geochemical processes occurring in these environments are particularly important to understand and predict with regards to such environmental changes.

Conroy, Nathan Alec (ORCID:0000000305973373)↗
DOE OSTI2025

Reinforcement Learning for In-Spill Optimization of the Mu2e Resonant Extraction: Compensating Non-Stationarity

We present design considerations and challenges for the fast machine learning component of a third-order resonant beam extraction regulation system being commissioned to deliver steady beam rates to the mu2e experiment at Fermilab. Dedicated quadrupoles drive the tune toward the 29/3 resonance each spill, extracting beam at kV multiwire septa. The overall Spill Regulation System consists of (1) a “slow” process using ~100-spill averages to adjust the base quad ramp infrequently, (2) a feedforward harmonic content compensator, and (3) the “fast” ML agent reacting during each ongoing spill with on-the-fly additive corrections to the sum of (1) and (2). We have demonstrated improved beam-rate steadying for a fast ML agent compared to a PID controller using a quasi-physical spill simulation, and demonstrated distillation of that simulation into a predictive surrogate model. Current work includes a data-and-training pipeline to generate data-aware surrogates with real-world dynamics, even as the dynamics shift unpredictably. The surrogates are to act as RL environments against which to train our fast ML control agents before deploying them on FPGA in the live system. Further current efforts focus on modeling and controlling beam loss around the storage ring, understanding additional available hardware inputs to the model, and the interplay of these with beam-steadying performance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Thermal Transport and Fracture Behavior of Sintered Fuel Pellets: Experimental Validation of NEAMS Tool MARMOT

The project targets at experimental validation of NEAMS tool MARMOT for predicting thermal transport and fuel fracture. The key components of the project include: (1) sintering UO 2 samples with well-controlled microstructures (grain size, pore, fission bubble, porosity and pore distribution either in intra- or inter-granular pores); (2) thermal conductivity measurements of these wellcharacterized polycrystalline samples; (3) indentation testing of sintered UO 2 with various grain size, porosity and stoichiometry to obtain fracture and crack propagation mechanisms (inter or intragranular fracture); and (4) validation and uncertainty quantification of the MARMOT thermal transport and fracture models. The high quality experimental data will be used to validate the predictions of MARMOT for thermal transport and fracture, using average data and local microstructure information. In this work, UO 2 fuels with controlled microstructure (grain size, pore size/structure and fuel chemistry) were successfully prepared by high energy ball milling of the hyper-stoichiometric UO 2+x powders purchased commercially, and consolidated by spark plasma sintering. Dense nano-sized UO 2 fuel pellets with controlled stoichiometry were sintered by SPS. The effects of different SPS sintering parameters on the pore size and distribution were investigated, and the correlation among the SPS parameter – microstructure control – stoichiometry/defect structure was established. To further control the pore structure and fuel chemistry, systematic thermal annealing of the sintered fuels with the controlled pore structure was performed in a reduced. The controlled microstructure and fuel chemistry enable the investigation of the separate effects of pore structure and fuel chemistry on thermal transport behavior. The mechanical properties of the sintered UO 2 fuels were tested by nanoindentation and microindentation testing at different temperatures. The fracture behavior of the sintered fuels as functions of grain structure and temperature was investigated. Nanocrystalline UO 2 display higher hardness than microcrystalline counterpart, consistent with the Hall-Petch strengthening mechanism. Greater Young’s modulus and fracture toughness are also identified for the nanocrystalline UO 2 , and hardness and Young’s modulus decrease with temperature, suggesting better ductility of oxide fuels at greater temperature at small length scale. Hyper-stoichiometric UO 2 specimen displays higher hardness and fracture toughness than stoichiometric UO 2 , due to the impediment of the crack propagation by the oxygen interstitial atoms. These results are useful in understanding the mechanical properties of the high burn-up structure (HBS) formed in nuclear fuels during reactor operation, and also provide critical experimental data as the input for the development and validation of the MARMOT fracture model of nuclear fuels. The thermal properties of the sintered UO 2 fuel pellets with controlled grain structure, fuel chemistry and pore structures are measured by laser flash and the impact of the microstructure characteristics on the thermal transport properties is elucidated. Through a close collaboration with the MARMOT team at Idaho National Laboratory, the thermal properties of the sintered oxide fuel pellets with controlled microstructures are measured experimentally, and modeled using the developed MARMOT thermal transport models for a joint effort of demonstrating high spatial resolution measurements at the microstructural level on as-fabricated nuclear fuels and materials and obtain critical experimental data for model validation and properties predictions. In this work, we also conducted sensitivity analysis and uncertainty quantification (UQ) on a mesoscale simulation that has been applied to understand the effective thermal conductivity of UO 2 reactor fuel using the MARMOT tool. We also conduced UQ on mechanistic macroscale models that have been developed to predict the effective thermal conductivity. The most sensitive parameter is the thermal conductivity of bulk UO 2 for the thermal conductivity models. In the future, the predicted simulation distributions need to be compared to experimental data for validation of the models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

The physics of desiccation cracks 2: Modeling and prediction of the crack patterns

Here, this paper extends the experimental results of our companion paper by modeling and predicting the onset and pattern formation of desiccation cracks in geomaterials (Ruoyu et al., 2023). Thin-layer silt samples in controlled atmospheric conditions were tested to obtain the surface strain maps with the digital image correlation (DIC) method during dehydration. Support experiments, including consolidation and displacement-controlled triaxial tests, were conducted for the properties of geomaterials. These experimental results were used to validate a viscoplastic theoretical model by comparing cracks and singularities locations that distribute following the Cnoidal wave pattern. A critical value exists in the viscoplastic model that determines the number of singularities, which accurately predicts the number of cracks in the experiment. The critical values contain two crucial parameters: rate sensitivity and λ. Rate sensitivity describes the rate-dependent stress–strain relation, while λ shows the ability of pore pressure redistribution under the external mechanical loading rate. These results provide a new view to analyze the desiccation cracks considering the rate-dependent viscoplasticity.

42 ENGINEERING↗
DOE OSTI2010

A thermodynamic analysis of native point defect and dopant solubilities in zinc-blende III-V semiconductors

A thermodynamic model is used to analyze available experimental data relevant to point defects in the binary zinc-blende III-V compounds (Ga,In)-(P,As,Sb). The important point defects and their complexes in each of the materials are identified and included in the model. Essentially all of the available experimental data on dopant solubility, crystal density, and lattice parameter of melt and solution grown crystals and epilayers are reproduced by the model. It extends an earlier study [Hurle, J. Appl. Phys. 85, 6957 (1999)] devoted solely to GaAs. Values for the enthalpy and entropy of formation of both native and dopant related point defects are obtained by fitting to experimental data. In undoped material, vacancies, and interstitials on the Group V sublattice dominate in the vicinity of the melting point (MP) in both the phosphides and arsenides, whereas, in the antimonides, vacancies on both sublattices dominate. The calculated concentrations of the native point defects are used to construct the solidus curves of all the compounds. The charged native point defect concentrations at the MP in four of the six materials are significantly higher than their intrinsic carrier concentrations. Thus the usually assumed high temperature 'intrinsic' electroneutrality condition for undoped material (n=p) is not valid for these materials. In GaSb, the Ga{sub Sb} antisite defect appears to be grown-in from the melt. This contrasts with the As{sub Ga} defect in GaAs for which the concentration grown-in at the MP is negligibly small. Compensation of donor-doped material by donor-Group III vacancy complexes is shown to exist in all the compounds except InP where Group VI doped crystals are uncompensated and in InSb where there is a lack of experimental data. The annealing effects in n{sup +} GaAs, including lattice superdilation, which were shown in the earlier paper to be due to Group III vacancy undersaturation during cooling, are found to be present also in GaSb and InAs. Results for native point defects are compared with reported ''first principles'' calculations for GaAs. It is seen that, while there is some accord with experimental findings for low temperature molecular beam epitaxial (MBE) growth, they fail totally to predict the behavior under high temperature growth conditions. The analysis of data on liquid phase epitaxy (LPE) growth of GaAs from Bi solution in the earlier paper has been re-calculated in the light of experimental data that showed that the model used in that paper to represent the Ga-As-Bi phase equilibria was inadequate. An improved model reveals that Ga vacancies exert a greater effect in controlling the extent of the linear range of donor dopant solubility than previously predicted. It has also led to a re-evaluation of the equilibrium EL2 and Ga vacancy concentrations in GaAs during MBE growth under As-rich conditions at low temperatures ({approx}500 K). The amended model predicts that the very high concentrations of EL2 and of Ga vacancies observed experimentally are near equilibrium values. The predicted increase in the equilibrium concentrations of these defects at low temperatures results from coulombic attraction between the two defects. At temperatures somewhat lower than 500 K the rate of increase becomes catastrophic.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Nanoparticle Catalyst Formation and Subsequent Sintering: Size-Distribution Fundamental Understanding and Catalyst Control via Mechanism-Enabled Population Balance Modeling (Final Report)

The challenge of elucidating the chemical mechanisms by which nano- and other particles form, and then using those mechanisms to understand, analyze, and ideally predict particle-size and particle-size distributions, was undertaken. Thanks to multi-year, longer-term DOE support each of these challenges was successfully met. The community is now able to both predict, and control in cases where the underlying mechanism of particle formation has been determined, the key PS and PSD that are, in turn, critical properties that strongly influence the catalytic and other physical properties of nano- and larger particles across nature.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Phase and Power Control in the RF Magnetron Power Stations of Superconducting Accelerators

Phase and power control methods that satisfy the requirements of superconducting accelerators to magnetron RF sources were considered by a simplified kinetic model of a magnetron driven by a resonant injected signal. The model predicting and explaining stable, low noise operation of the tube below the threshold of self-excitation (the Hatrree voltage in free run mode) at a highest efficiency, a wide range of power control and a wide-band phase control was well verified in experiments demonstrating capabilities of the magnetron transmitters for powering of state of the art superconducting accelerators. Descriptions of the kinetic model, the experimental verification and a conceptual scheme of the highly-efficient magnetron RF transmitter for the accelerators are presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Longitudinal coherence and short-term wind speed prediction based on a nacelle-mounted Doppler lidar

The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.

17 WIND ENERGY↗
DOE OSTI2012

Laboratory and Lysimeter Experimentation and Transport Modeling of Neptunium and Strontium in Savannah River Site Sediments

The Savannah River Site (SRS) conducts performance assessment (PA) calculations to determine the appropriate amount of low-level radiological waste that can be safely disposed on site. Parameters are included in these calculations that account for the interaction between the immobile solid phase and the mobile aqueous phase. These parameters are either the distribution coefficient (K$_{d}$ value) or the apparent solubility value (K$_{sp}$). These parameters are readily found in the literature and are used throughout the DOE complex. One shortcoming of K$_{d}$ values is that they are only applicable to a given set of solid and aqueous phase conditions. Therefore, a given radionuclide may have several K$_{d}$ values as it moves between formations and comes into contact with different solids and different aqueous phases. It is expected that the K$_{d}$ construct will be appropriate to use for a majority of the PA and for a majority of the radionuclides. However, semi-mechanistic models would be more representative in isolated cases where the chemistry is especially transitory or the radionuclide chemistry is especially complex, bringing to bear multiple species of varying sorption tendencies to the sediment. Semi-mechanistic models explicitly accommodate the dependency of K$_{d}$ values, or other sorption parameters, on contaminant concentration, competing ion concentrations, pH-dependent surface charge on the adsorbent, and solute species distribution. Incorporating semi-mechanistic concepts into geochemical models is desirable to make the models more robust and technically defensible. Furthermore, these alternative models could be used to augment or validate a K$_{d}$-based DOE Order 435.1 Performance Assessment. The objectives of this study were to: 1) develop a quantitative thermodynamically-based model for neptunium sorption to SRS sediments, and 2) determine a sorption constant from an SRS 11-year lysimeter study. The modeling studies were conducted with existing data sets. The first data set used laboratory generated Np sorption data as a function of concentration (three orders of magnitude) and as a function of pH (four orders of magnitude of proton concentration). In this modeling exercise, a very simple solution was identified by assuming that all sorption occurred only to the iron oxides in the sediment and that all the added NpO$_{4}$$^{-}$ remained in the oxidized state and was not reduced to the Np(IV) state (as occurs rapidly with Pu(V)). With rather limited input data, very good agreement between experimental and modeling results was observed. This modeling approach would be easy to add to the PA with little additional data requirements. This model would be useful in a system where pH is expected to change greatly, such as directly beneath a grout or concrete structure. The second model discussed in the report was to derive strontium K$_{d}$ values from data collected in an 11-year-old field transport study. In this controlled lysimeter study, a sensitivity analysis was conducted of hydrological and chemical processes that influence contaminant transport, including diffusion coefficients, seepage velocity, and K$_{d}$ value. The best overall K$_{d}$ derived from the model fit to the data was 32 L kg$^{-1}$, which was the same value that was previously measured in traditional laboratory batch sorption studies. This was an unexpected result given the differences in experimental conditions between the batch test and the lysimeter flow through test, in particular the differences between strontium adsorption and desorption processes occurring in the latter test and not in the former. There were some trends in the lysimeter strontium data that were not predicted by the K$_{d}$ model, which suggest that other geochemical processes are likely also controlling strontium transport. Strontium release and cation exchange are being evaluated. These results suggest that future modeling efforts (e.g., PAs) could be improved by employing a more robust semi-empirical modeling approach to transient or complex conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Improving the FLORIS wind plant model for compatibility with gradient-based optimization

The FLORIS (FLOw Redirection and Induction in Steady-state) model, a parametric wind turbine wake model that predicts steady-state wake characteristics based on wind turbine position and yaw angle, was developed for optimization of control settings and turbine locations. This article provides details on changes made to the FLORIS model to make the model more suitable for gradient-based optimization. Changes to the FLORIS model were made to remove discontinuities and add curvature to regions of non-physical zero gradient. Exact gradients for the FLORIS model were obtained using algorithmic differentiation. A set of three case studies demonstrate that using exact gradients with gradient-based optimization reduces the number of function calls by several orders of magnitude. The case studies also show that adding curvature improves convergence behavior, allowing gradient-based optimization algorithms used with the FLORIS model to more reliably find better solutions to wind farm optimization problems.

17 WIND ENERGY↗
DOE OSTI2011

Irradiation Planning for Fully-Ceramic Micro-encsapsulated fuel in ATR at LWR-relevant conditions: year-end report on FY-2011

This report presents the estimation of required ATR irradiation levels for the DB-FCM fuel design (fueled with Pu and MAs). The fuel and assembly designs are those considered in a companion report [R. S. Sen et al., FCR&D-2011- 00037 or INL/EXT-11-23269]. These results, pertaining to the DB-FCM fuel, are definitive in as much as the design of said fuel is definitive. In addition to the work performed, as required, for DB-FCM fuel, work has started in a preliminary fashion on single-cell UO2 and UN fuels. These latter activities go beyond the original charter of this project and although the corresponding work is incomplete, significant progress has been achieved. However, in this context, all that has been achieved is only preliminary because the corresponding fuel designs are neither finalized nor optimized. In particular, the UO2 case is unlikely to result in a viable fuel design if limited to enrichment at or under 20 weight % in U-235. The UN fuel allows reasonable length cycles and is likely to make an optimal design possible. Despite being limited to preliminary designs and offering only preliminary conclusions, the irradiation planning tasks for UO2 and UN fuels that are summarized in this report are useful to the overall goal of devising and deploying FCM-LWR fuel since the methods acquired and tested in this project and the overall procedure for planning will be available for planning tests for the finalized fuel design. Indeed, once the fuel design is finalized and the expected burnup level is determined, the methodology that has been assembled will allow the prompt finalization of the neutronic planning of the irradiation experiment and would provide guidance on the expected experimental performance of the fuel. Deviations from the expected behavior will then have to be analyzed and the outcome of the analysis may be corrections or modifications for the assessment models as well as, possibly, fuel design modifications, and perhaps even variation of experimental control for future experimental phases. Besides the prediction of irradiation times, preliminary work was carried out on other aspects of irradiation planning. In particular, a method for evaluating the interplay of depletion, material performance modeling and irradiation is identified by reference to a companion report. Another area that was addressed in a preliminary fashion is the identification and selection of a strategy for the physical and mechanical design of the irradiation experiments. The principal conclusion is that the similarity between the FCM fuel and the fuel compacts of the Next Generation Nuclear Plant prismatic design are strong enough to warrant using irradiation hardware designs and instrumentation adapted from the AGR irradiation tests. Modifications, if found necessary, will probably be few and small, except as pertains to the water environment and its implications on the use of SiC cladding or SiC matrix with no additional cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Cryogenic thermal modeling of microwave high density signaling

Superconducting quantum computers require microwave control lines running from room temperature to the mixing chamber of a dilution refrigerator. Adding more lines without preliminary thermal modeling to make predictions risks overwhelming the cooling power at each thermal stage. In this paper, we investigate the thermal load of SC-086/50-SCN-CN semi-rigid coaxial cable, which is commonly used for the control and readout lines of a superconducting quantum computer, as we increase the number of lines to a quantum processor. We investigate the makeup of the coaxial cables, verify the materials and dimensions, and experimentally measure the total thermal conductivity of a single cable as a function of the temperature from cryogenic to room temperature values. We also measure the cryogenic DC electrical resistance of the inner conductor as a function of temperature, allowing for the calculation of active thermal loads due to Ohmic heating. Fitting this data produces a numerical thermal conductivity function used to calculate the static heat loads due to thermal transfer within the wires resulting from a temperature gradient. The resistivity data is used to calculate active heat loads, and we use these fits in a cryogenic model of a superconducting quantum processor in a typical Bluefors XLD1000-SL dilution refrigerator, investigating how the thermal load increases with processor sizes ranging from 100 to 225 qubits. We conclude that the theoretical upper limit of the described architecture is approximately 200 qubits. However, including an engineering margin in the cooling power and the available space for microwave readout circuitry at the mixing chamber, the practical limit is approximately 140 qubits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Directed Energy Deposition Process Modeling, Validation, and Process-Informed Optimization

The directed energy deposition (DED) process, one of the most popular additive manufacturing techniques in use today, involves various complex physical mechanisms that are not yet well understood. In this regard, computational tools show promise for elucidating the manufacturing process and enabling nondestructive performance evaluations of manufactured parts. To better control and optimize the DED process?thereby improving the manufactured product? the present work develops and demonstrates a novel artificial intelligence (AI)-based process control and optimization technique. Specifically, a geometry-free thermo-mechanical model with adaptive subdomain construction is developed to accurately capture the material?s thermo-mechanical response under cyclical reheating and high cooling rates [1]. The model is demonstrated and validated with experimental measurements, in light of various geometries and processing parameters. Moreover, based on this thermo-mechanical model, a physics-informed reduced-order model is developed to enable quick predictions of the temperature field at every time step. Furthermore, an AI-based controller is developed that can adapt to the ever-changing system states by automatically adjusting the manufacturing process parameters. This entire work is based on the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) [2] and its recent integration with Libtorch (the C++ frontend of PyTorch [3]). The combined development of the adaptive material deposition scheme, thermo-mechanical model, associated reduced-order model, and AI-based process controller carries great potential for improving advanced manufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dual frequency comb spectroscopy of solid fuel pyrolysis and combustion: Quantifying the influence of moisture content in Douglas fir

As the fire research community develops multi-scale models to predict the spread of wildland fires, there is a need for well-controlled, small-scale experiments which provide benchmark data and fundamental understanding of the effects of key parameters on the combustion of wildland fuels. Here, we implement a unique suite of non-intrusive, quantitative diagnostics that simultaneously measure mass loss, surface temperature, in situ gas temperature and water vapor concentration, including the first implementation of dual frequency comb spectroscopy for the study of solid fuels. As a demonstration, we examine the effect of fuel moisture content on the burning of Douglas fir wood samples that are ignited under constant radiative heat flux from a cone calorimeter to provide benchmark data for multi-scale models. Peak flame temperatures 3 mm above the sample surface measured with the dual frequency comb laser spectrometer are in excess of 1200 K. The flame temperature decreases by over 150 K as fuel moisture content increases from 0.7% to 40%. This work demonstrates a platform for simultaneous, quantitative measurements of well-controlled small-scale experiments which provide benchmark data for the development of advanced modeling and simulations capable of capturing the complex physics present in wildland fire systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Capillary pinning and blunting of immiscible gravity currents in porous media

Gravity-driven flows in the subsurface have attracted recent interest in the context of geological carbon dioxide (CO 2 ) storage, where supercritical CO 2 is captured from the flue gas of power plants and injected underground into deep saline aquifers. After injection, the CO 2 will spread and migrate as a buoyant gravity current relative to the denser, ambient brine. Although the CO 2 and the brine are immiscible, the impact of capillarity on CO 2 spreading and migration is poorly understood. We previously studied the early time evolution of an immiscible gravity current, showing that capillary pressure hysteresis pins a portion of the macroscopic fluid–fluid interface and that this can eventually stop the flow. Here we study the full lifetime of such a gravity current. Using tabletop experiments in packings of glass beads, we show that the horizontal extent of the pinned region grows with time and that this is ultimately responsible for limiting the migration of the current to a finite distance. We also find that capillarity blunts the leading edge of the current, which contributes to further limiting the migration distance. Using experiments in etched micromodels, we show that the thickness of the blunted nose is controlled by the distribution of pore–throat sizes and the strength of capillarity relative to buoyancy. We develop a theoretical model that captures the evolution of immiscible gravity currents and predicts the maximum migration distance. By applying this model to representative aquifers, we show that capillary pinning and blunting can exert an important control on gravity currents in the context of geological CO 2 storage.

58 GEOSCIENCES↗