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All sources NASA NTRS DOE OSTI

DOE OSTI2022

CMB-S4: Forecasting Constraints on Primordial Gravitational Waves

CMB-S4—the next-generation ground-based cosmic microwave background (CMB) experiment—is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the universe. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semianalytic projection tool, targeted explicitly toward optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2–3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments, given a desired scientific goal. To form a closed-loop process, we couple this semianalytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r > 0.003 at greater than 5σ, or in the absence of a detection, of reaching an upper limit of r < 0.001 at 95% CL.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Evaluation of Engineered Barrier Systems FY20 Report

This report describes research and development (R&D) activities conducted during fiscal year 2020 (FY20) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Energy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Generic Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc. The FY20 EBS activities involved not only modeling and analysis work, but experimental work as well. Despite delays to some planned activities due to COVID-19 precautions, progress was made during FY20 in multiple research areas and documented in this report as follows: (1) EBS Task Force: Task 9/FEBEX Modeling Final Report: Thermo-Hydrological Modeling with PFLOTRAN, (2) preliminary sensitivity analysis for the FEBEX in-situ heater test, (3) cement-carbonate rock interaction under saturated conditions: from laboratory to modeling, (4) hydrothermal experiments, (5) progress on investigating the high temperature behavior of the uranyl-carbonate complexes, (6) in-situ and electrochemical work for model validation, (7) investigation of the impact of high temperature on EBS bentonite with THMC modeling, (8) sorption and diffusion experiments on bentonite, (9) chemical controls on montmorillonite structure and swelling pressure, (10) microscopic origins of coupled transport processes in bentonite, (11) understanding the THMC evolution of bentonite in FEBEX-DP—coupled THMC modeling, (12) modeling in support of HotBENT, an experiment studying the effects of high temperatures on clay buffers/near-field, and (13) high temperature heating and hydration column test on bentonite.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1993

Development and evaluation of a profile negotiation process for integrating aircraft and air traffic control automation

The development and evaluation of the profile negotiation process (PNP), an interactive process between an aircraft and air traffic control (ATC) that integrates airborne and ground-based automation capabilities to determine conflict-free trajectories that are as close to an aircraft's preference as possible, are described. The PNP was evaluated in a real-time simulation experiment conducted jointly by NASA's Ames and Langley Research Centers. The Ames Center/TRACON Automation System (CTAS) was used to support the ATC environment, and the Langley Transport Systems Research Vehicle (TSRV) piloted cab was used to simulate a 4D Flight Management System (FMS) capable aircraft. Both systems were connected in real time by way of voice and data lines; digital datalink communications capability was developed and evaluated as a means of supporting the air/ground exchange of trajectory data. The controllers were able to consistently and effectively negotiate nominally conflict-free vertical profiles with the 4D-equipped aircraft. The actual profiles flown were substantially closer to the aircraft's preference than would have been possible without the PNP. However, there was a strong consensus among the pilots and controllers that the level of automation of the PNP should be increased to make the process more transparent. The experiment demonstrated the importance of an aircraft's ability to accurately execute a negotiated profile as well as the need for digital datalink to support advanced air/ground data communications. The concept of trajectory space is proposed as a comprehensive approach for coupling the processes of trajectory planning and tracking to allow maximum pilot discretion in meeting ATC constraints.

Green, Steven M.↗

DOE OSTI2021

Spinning no-scale $${\mathcal {F}}$$-SU(5) in the right direction

Abstract The Fermi National Accelerator Laboratory (FNAL) recently announced confirmation of the Brookhaven National Lab (BNL) measurements of the $$g-2$$ g - 2 of the muon that uncovered a discrepancy with the theoretically calculated Standard Model value. We suggest an explanation for the combined BNL+FNAL 4.2 $$\sigma $$ σ deviation within the supersymmetric grand unification theory (GUT) model No-Scale $${\mathcal {F}}$$ F - $$SU(5)$$ S U ( 5 ) supplemented with a string derived TeV-scale extra $$10+\overline{10}$$ 10 + 10 ¯ vector-like multiplet and charged vector-like singlet $$(XE,XE^c)$$ ( X E , X E c ) , dubbed flippons. We introduced these vector-like particles into No-Scale Flipped SU (5) many years ago, and as a result, the renormalization group equation (RGE) running was immediately shaped to produce a distinctive and rather beneficial two-stage gauge coupling unification process to avoid the Landau pole and lift unification to the string scale, in addition to contributing through 1-loop to the light Higgs boson mass. The flippons have long stood ready to tackle another challenge, and now do so yet again, where the charged vector-like “lepton”/singlet couples with the muon, the supersymmetric down-type Higgs $$H_d$$ H d , and a singlet S , using a chirality flip to easily accommodate the muonic $$g-2$$ g - 2 discrepancy in No-Scale $${\mathcal {F}}$$ F - $$SU(5)$$ S U ( 5 ) . Considering the phenomenological success of this string derived model over the prior 11 years that remains accommodative of all presently available LHC limits plus all other experimental constraints, including no fine-tuning, and the fact that for the first time a Starobinsky-like inflationary model consistent with all cosmological data was derived from superstring theory in No-Scale Flipped SU (5), we believe it is imperative to reconcile the BNL+FNAL developments within the model space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Phase instability-coupled fracture behavior in garnet LLZO solid electrolytes: a machine learning-enabled atomistic study

Fracture in the garnet-type solid electrolyte Li 7 La 3 Zr 2 O 12 (LLZO) poses a critical threat to both the performance and safety of solid-state batteries. To unravel the coupled chemomechanical processes that govern fracture in LLZO under external loading, we carry out large-scale molecular-dynamics simulations with a validated machine-learning force field. Our results demonstrate that triaxial stresses at crack flanks trigger a localized cubic-to-tetragonal phase transformation, which is accompanied by Li-ion rearrangement. The emergent tetragonal domains feature lattice contraction normal to the fracture plane, imposing coherent misfit strains that provide an additional driving force for further crack propagation. Crucially, introducing Li deficiencies stabilizes the cubic phase, postponing the phase transition and thereby delaying fracture initiation. These findings highlight the role of intrinsic phase instability in dictating LLZO's fracture resistance and its critical connection to local Li concentration. This chemomechanical coupling points toward targeted strategies to enhance the mechanical robustness of garnet electrolytes, including tuning Li content, ensuring dopant homogeneity, and refining processing protocols.

Materials science↗

DOE OSTI2022

Low Regeneration Temperature Sorbents for Direct Air Capture of CO 2

Susteon Inc., in partnership with University of Wyoming and SoCalGas, successfully met all major technical objectives to (1) scale up the ionic liquid catalyst for amine-based sorbents for improved desorption and absorption kinetics, (2) evaluate the catalyzed amine-based sorbents for direct CO 2 capture process to determine CO 2 adsorption and desorption rates and energy requirements, and (3) based on the experimental results, develop a conceptual process design to perform a preliminary economic assessment to evaluate the potential for DAC process cost reduction using the catalyzed sorbents. Amine doped solid sorbents are effective for DAC applications and can be regenerated by heat or by a combination of heat, steam, and vacuum. The best sorbent composition identified was polyethyleneimine (PEI) on fumed silica with 200 ppm ionic liquid catalyst. This sorbent formulation was shown to have a CO 2 breakthrough capacity twice that of the non-catalyzed sorbent, in laboratory tests with air at 75% relative humidity (RH). The CO 2 adsorption rate was also 40% higher than that of the non-catalyzed sorbent. This type of sorbents has the attributes required for lowering the overall cost of DAC with high CO 2 capacity and high rate of adsorption. The combination of an industrially utilized amine-based sorbent with a highly active catalyst to form a new class of materials for DAC provides a technically viable pathway for reducing the cost of DAC to <$100/tonne of CO 2 . Laboratory measurements show that the silica/PEI (polyethyleneimine) sorbents with 100 ppm of ionic liquid catalyst have almost 100% higher CO 2 cyclic capacity and 40% higher adsorption rate. Generally, CO 2 desorption occurred at higher temperatures with a rate of desorption 10 times faster than adsorption (which occurred at ambient conditions). Therefore, adsorption rate is a much more important factor in the cost of DAC because it is directly linked to the CAPEX of the total system and the cycle time (i.e., sorbent productivity in ton/day of CO 2 captured per unit volume of the air contactor). An initial process design, coupled with techno-economic analysis, based on optimal experimental results and preliminary resulting from structured sorbent testing, showed a path to lower the DAC cost from the current cost of over $200/tonne CO 2 to less than $100/tonne with a scale-up, mature state of the technology, with projected material and process improvements. These results demonstrate the effectiveness of the catalyst in silica/PEI sorbents in enhancing sorbents’ CO 2 working capacity, in (a) increasing the rate of adsorption and desorption, and (b) in lowering the CAPEX and OPEX of the DAC system employing the ionic liquid catalyzed sorbents.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Innovations in underground hydrogen storage with multiphysics simulations, optimization, and monitoring: A review

Underground Hydrogen Storage (UHS) is a promising solution for large-scale energy storage and a critical component in advancing low-carbon energy system. Ensuring the safety and efficiency of UHS necessitates a comprehensive understanding of multiphysical interactions driven by cyclic pore fluid pressure fluctuations and coupled physicochemical processes. Here, this review examines the key geomechanical responses in UHS, including rock property variations under cyclic loading, fracture evolution and propagation, reservoir stress sensitivity, and fault stability. It also explores the impact of geochemical and microbial reactions on geomechanical characteristics. We provide an in-depth analysis of Thermal-Hydraulic-Mechanical-Chemical (THMC) coupled numerical simulations, highlighting their potential for future multi-scale modeling. Limitations of current machine learning (ML) approaches in addressing UHS challenges are highlighted, emphasizing the need for innovative ML-based methodologies. Operational strategies for hydrogen injection and production are reviewed, focusing on safety, efficiency, and economic viability. The necessity for multi-objective optimization (MOO) to balance storage efficiency, risk mitigation, and cost-effectiveness is also discussed. Current monitoring technologies are evaluated to ensure safe and efficient UHS operations. Finally, this review identifies critical knowledge gaps and underscores the importance of advancing geomechanical understanding under multiphysics-coupling. We highlight the need for ML-driven multiphysics theories, enhanced modeling techniques, and robust optimization strategies to improve UHS performance. This study serves as a comprehensive reference for future research and the large-scale implementation of UHS systems.

25 ENERGY STORAGE↗

DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗

NASA NTRS1996

Integrating a Genetic Algorithm Into a Knowledge-Based System for Ordering Complex Design Processes

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to be able to determine the best ordering of the processes within these subcycles to reduce design cycle time and cost. Many decomposition approaches assume the capability is available to determine what design processes and couplings exist and what order of execution will be imposed during the design cycle. Unfortunately, this is often a complex problem and beyond the capabilities of a human design manager. A new feature, a genetic algorithm, has been added to DeMAID (Design Manager's Aid for Intelligent Decomposition) to allow the design manager to rapidly examine many different combinations of ordering processes in an iterative subcycle and to optimize the ordering based on cost, time, and iteration requirements. Two sample test cases are presented to show the effects of optimizing the ordering with a genetic algorithm.

Rogers, James L.↗

DOE OSTI2026

Innovations Driven by Advanced Characterization to Strategize Critical Mineral Production and Beneficial Reuse from Fossil Energy Waste

Critical minerals (CM), such as rare earth elements (REE), cobalt, nickel, and lithium, have important uses in modern electronics and advanced manufacturing, yet are vulnerable to potential supply chain disruptions. Relatively abundant and readily available fossil energy (FE) wastes, such as coal combustion ash, acid mine drainage (AMD) and treatment solids (AMD solids), and Oil and Gas (O&G) drilling wastes (drill cuttings and produced waters) are under consideration as CM feedstocks. The National Energy Technology Laboratory (NETL) has studied CM resources for various FE wastes as part of the U.S. Department of Energy’s mission of bolstering the domestic CM supply, and makes the data available to the public on EDX at sites such as the NEWTS group. Advanced characterization utilizing synchrotron x-ray techniques coupled with laboratory extractions has been performed to identify CM hosting phases in these FE wastes to inform CM recoverability mechanisms. Novel methods to selectively recover CMs while co-producing other valuable byproducts have been developed. Successful examples discussed here include: (1) The identification of REE/Co/Ni/Sc binding and hosting phases in select FE waste (coal combustion ash and AMD solids), resulting in the development of a patented CM step-extraction process, (2) coupled production of functional sorbents from these extraction wastes and for CM recovery. A pilot-scale testing to evaluate the patent’s technical feasibility for extracting REE from coal ash on a barrel scale has been successfully performed. Additionally, (3) evaluation and measurements of brine geochemistry from U.S. O&G produced waters has informed a high Li recovery potential from Marcellus Shale produced water. NETL researchers have been developing tailored pre-treatment processes, an innovative and highly durable lithium sorbent, and geochemical model guided precipitation to accelerate Li production from the Marcellus Shale produced waters. These innovations driven by characterization are integral for maximizing and advancing the potential for CM recovery while offsetting the cost and environmental footprint for FE waste management.

critical mineral processing↗

NASA NTRS2001

Microwave Remote Sensing and the Cold Land Processes Field Experiment

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Kim, Edward J.↗

DOE OSTI2022

Digital Twin of an ANPC inverter with integrated Design-For-Trust

Reducing costs and increasing manufacturing output while creating reliable products are three essential components needed to keep a business competitive. In the past, having an asset that could address these three challenges at the same time would be challenging. However, recent advances in computer processing capabilities coupled with increases to network speeds allowed such a system to become possible; and this is known as a Digital Twin (DT), which represents a virtual copy of a production system or a product. Here, this work exhibits a DT application of an Active Neutral Point Clamped (ANPC) inverter using a controller. The controller emulates the ANPC responses of a new Digital Signal Processing (DSP) firmware and validates this new firmware by using Design-For-Trust (DFTr) characteristics. If the new firmware is malicious, then it should be rejected, thus adding preemptive protection measures for inverter based distributed energy resources.

cybersecurity↗

NASA NTRS1993

Immiscible phase incorporation during directional solidification of hypermonotectics

Solidification processes in immiscible samples were investigated by directly observing the events taking place at the solid-liquid interface during directional solidification. Visualization of these events was made possible through the use of a transparent metal analog system and a temperature gradient stage assembly fitted to an optical microscope. The immiscible transparent analog system utilized was the succinonitrile-glycerol system. This system has been shown to exhibit the same morphological transitions as observed in metallic alloys of monotectic composition. Both monotectic and hypermonotectic composition samples were directionally solidified in order to gain an improved understanding of the manner in which the excess hypermonotectic liquid is incorporated into the solidifying structure. The processing conditions utilized prevented sedimentation of the excess hypermonotectic liquid by directionally solidifying the samples in very thin (13 microns), horizontally oriented cells. High thermal gradient to growth rate ratios (G/R) were used in an effort to prevent constitutional supercooling and the subsequent formation of L(sub 2) droplets in advance of the solidification front during the growth of fibrous composite structures. Results demonstrated that hypermonotectic composites could be produced in samples up to two weight percent off of the monotectic composition by using a G/R ratio greater than or equal to 4.6 x 10(exp 4) C(s)/mm(sup 2) to avoid constitutional supercooling. For hypermonotectic samples processed with G/R ratios below 4.6 x 10(exp 4) C(s)/mm(sup 2), constitutional supercooling occurred and resulted in slight interfacial instability. For these samples, two methods of incorporation of the hypermonotectic liquid were observed and are reported. The correlation between the phase spacing, lambda, and the growth rate, R, was examined and was found to obey a relationship generally associated with a diffusion controlled coupled growth process. For samples with compositions ranging from the monotectic composition up to 2 percent off of the monotectic composition, data indicated that the square of the phase spacing (lambda) varied linearly with the inverse of the growth rate (R).

Andrews, J. Barry↗

DOE OSTI2024

Development of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) Value-Added Product Based on the lagtraj Framework

The Atmospheric Radiation Measurement (ARM) large-scale forcing data developed based on the constrained variational analysis (VARANAL) value-added product (VAP) (Zhang and Lin 1997, Zhang et al. 2001, Xie et al. 2004, Tang et al. 2019) has been widely used for single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LESs) to understand and improve physical processes in models. Recently, the U.S. Department of Energy (DOE) ARM user facility conducted several major field campaigns using ship-based moving observational platforms. For example, the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign focused on the role of subtropical marine-boundary layer (MBL) clouds, and the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign aimed to improve understanding of the coupled climate systems in the Arctic. Observations from moving platforms are critical to provide a comprehensive characterization of coupled-system processes associated with all stages of the cloud and/or sea-ice life cycle. Traditional ARM large-scale forcing data have been developed at fixed locations. They need to be extended to include these moving platforms to address data needs for ship-based field campaigns or to support LES modeling in a Lagrangian framework. With these considerations in mind, we develop ARM-type Lagrangian large-scale forcing data sets based on the lagtraj framework (Boeing et al. 2020) with notable enhancements in generating forcings that are more suitable for ARM field campaigns. The lagtraj is a novel tool that generates forcings for LES and SCM simulation in both Lagrangian and Eulerian perspective. This technical report focuses on the major changes we performed on the lagtraj algorithm and provides an overview of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) value-added products.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 1. Characterizing Thermal–Hydrological–Mechanical Processes

Abstract Bentonite clay is widely used in engineered barrier systems for the permanent disposal of high‐level radioactive waste due to its low permeability, high swelling capacity, and thermal stability. However, the complex thermal‐hydrological‐mechanical (THM) processes induced by heating from decaying radioactive waste and hydration from surrounding rock can lead to heterogeneous changes that are difficult to measure and predict. This study develops an Electrical Resistivity Tomography (ERT)‐based framework for monitoring THM processes, progressing from sample‐scale to bench‐scale tests, to inform field‐scale applications. Sample‐scale tests analyzed small bentonite samples under controlled variations in water content, temperature, and porosity to establish fundamental resistivity relationships. Bench‐scale tests involved larger bentonite columns subjected to heating (up to 200°C) and hydration under controlled pressure, simulating repository conditions. ERT measurements, complemented by X‐ray CT imaging, temperature monitoring, and tracing sensors, revealed coupled THM processes, such as hydration‐induced compression, swelling, and thermal gradients, leading to complex resistivity patterns. The results demonstrate the potential of ERT for capturing THM‐induced resistivity changes, though challenges remain in upscaling and quantitative analysis. This study evaluates laboratory test capabilities and proposes future improvements for understanding THM‐induced resistivity responses. A conceptual framework for ERT implementation in field‐scale monitoring is presented, synthesizing findings from both scales and exploring how ERT data can inform long‐term modeling and reduce prediction uncertainties. Overall, this ERT‐based framework offers a robust method for monitoring bentonite buffers, aiding in early issue detection and supporting the safe long‐term disposal of radioactive waste in geological repositories, while highlighting the need for future development. Plain Language Summary Bentonite clay is crucial in engineered barrier systems (EBS) for containing high‐level radioactive waste due to its ability to absorb water, swell, seal and remain stable under high temperatures. When bentonite absorbs water and heats up from radioactive decay, it experiences complex changes in its physical and mechanical properties. Understanding these changes is important for ensuring the long‐term safety and effectiveness of EBS. This study used Electrical Resistivity Tomography (ERT), a non‐invasive method that measures electrical conductivity to monitor these changes during laboratory experiments. The ERT data revealed significant variations in resistivity corresponding to changes in water content, temperature, and density, providing detailed spatial and temporal insights into the behavior of bentonite. These findings enhance our ability to predict the long‐term performance of bentonite barriers, ensuring the safe containment of radioactive waste. By improving our understanding of bentonite's behavior, this research supports the development of more reliable and effective barrier systems for radioactive waste disposal, protecting the environment and public health. Key Points ERT monitoring was employed to capture resistivity changes in bentonite during controlled heating and hydration experiments, providing insights into THM processes ERT data reveal significant resistivity changes correlated with water content, temperature, and mechanical effects, enhancing the understanding of THM dynamics in bentonite This study explores the potential of the framework for application in field‐scale EBS monitoring, emphasizing the need for integrating additional geophysical methods for comprehensive subsurface imaging

Chen, Hang↗

NASA NTRS2002

FSW Implementation on the Space Shuttle's External Tank

The Friction Stir Welding process developed by The Welding Institute (TWI) has found application throughout the transportation industry. This technique has proven to be a viable joining process for aluminum alloys, producing virtually defect free welds with improved mechanical properties as compared to conventional fusion welding. Lockheed Martin Space Systems - Michoud Operations has been developing this technology for application on aluminum 2219 and 2195 cryogenic tankage since 1995. This effort will come to fruition with implementation on the longitudinal welds of the External Fuel Tank (ET) of the Shuttle. To this end, sub-and full-scale demonstration programs coupled with process development and optimization have been complete. Full size ET tool has been designed, fabricated and are being installed at the Michoud Assembly Facility. Upon completion of the tooling certification and materials allowables testing, production will commence. This implementation will result in increased reliability and safety of flight for the Shuttle.

Hartley, Paula J.↗

NASA NTRSDate not supplied

Optimization of the 4-Bed CO2 Scrubber Performance Based on Ground Tests

Testing of ground hardware closely matching the flight design for the 4-Bed CO2Scrubber (4BCO2) has provided a wealth of information about the complex and highly coupled batch process. The 4BCO2 process is a Thermal and Vacuum Swing Adsorption (TVSA) cycle with recirculating flows. The ability to measure CO2concentration at intermediate points in this architecture is new to this ground test system and has revealed a better understanding of system performance and its dependence on system design, operational parameters, and boundary conditions. This paper will discuss a statistical analysis performed to identify significant correlations between performance factors and operational parameters. Using these new insights on the highly coupled system physics, means for improvements to system performance are proposed.

CO2 Removal↗

DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗