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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 91 records · Page 5

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗

A Machine Learning based Approach of Estimating Equivalent Circuit Model Parameters at Different SoCs of Li-ion Batteries from Voltage Relaxation

Abstract: In this study, an approach of estimating the equivalent circuit model (ECM) parameters for Li-ion batteries (LIBs) is proposed based on the voltage value at different intervals while relaxing the LIB after discharge. The typical approach for estimating ECM parameters of a LIB is to conduct electrochemical impedance spectroscopy (EIS) measurements at different frequencies and fit them to a predefined circuit model, which requires additional measuring arrangements and specialized devices. The proposed methodology utilizes four different voltages at 0s, 60s, 360s, and 1800s alongside the specific state of charge (SoC) value for a specific constant discharge current value of ~1C until the relaxation stage to train and evaluate three regression-based machine learning models— Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR)—for estimating the ECM parameters of the selected model. Bayesian optimization is employed for hyperparameter tuning to achieve optimal performance for all the regressor models, among which, the GPR provided the best performance with the root-mean-squared error (RMSE) of less than 4x10-4 on average for the resistive components and less than 0.27 for capacitive components with excellent R2 scores. The simplicity of the approach enables it to eliminate the need for sophisticated measuring equipment and computation power.

Sagar, Md. Samiul [The University of Alabama (UA)]↗

Data and scripts associated with the manuscript "A Hydrogeophysical Framework to Assess Infiltration During a Simulated Ecosystem-Scale Flooding Experiment”

This package contains the data and scripts used in "A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment" (Adebayo et al., 2023). The Geophysics folder contains two subfolders containing Ground Penetrating Radar (GPR) and Electrical Resistivity Imaging (ERI) datasets acquired between August 23 and August 26, 2021. The GPR subfolder contains raw data (Tempest_Project.GPZ) and processed results using the Sensors and Software EKKO Project software. The ERI subfolder contains two additional subfolders named Static and Timelapse. The Static subfolder contains background resistivity datasets acquired before infiltration, both raw and inverted results using the ResIpy code. The Timelapse subfolder contains repeated resistivity datasets acquired during infiltration on two profiles, along with raw and inverted results using the ResIpy code. The Flow model folder contains ATS model simulation files. The Geophysics-informed subfolder contains simulation datasets for two profiles generated using information from the geophysics data. The National_database-informed subfolder contains simulation datasets for two profiles generated using information from a national soil database. Each subfolder contains initial simulation results, multiple ensembles, and final simulation files. The Sensitivity_analysis folder contains the observation files exported from the flow model, notebooks used in running the code, and sensitivity analysis results showing the sensitivity of different parameters over a 48-hour period. We used mutual information-based global sensitivity analysis for the sensitivity analysis, following Jiang et al., 2022. This study shows a novel approach that integrates geophysical data with hydrological modeling to understand subsurface flow during coastal flooding.

54 ENVIRONMENTAL SCIENCES↗

Determination of Ground Subsidence Around Snow Fences in the Arctic Region

In this study, we analyzed the effects of snow cover changes caused by snow fences (SFs) installed in 2017 in the Alaskan tundra to examine ground subsidence. Digital surface model data obtained through LiDAR-based remote sensing in 2019 and 2022, combined with a field survey in 2021, revealed approximately 0.2 m of ground subsidence around the SF. To investigate the relationship between SF-induced snow cover changes and ground subsidence, geophysical methods, electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), were applied in 2023 to analyze subsurface characteristics. The increased snow cover due to the SF-enhanced insulation, delaying the penetration of winter cold into the subsurface. This delay caused subsurface temperatures to decrease more slowly, melting the upper permafrost and increasing the thickness of the active layer. ERT and GPR surveys well delineated the boundary between the active layer and permafrost, confirming that the increased snow cover thickened the active layer. This thickening led to the melting of pore ice, causing water runoff and ground compaction, which resulted in subsidence. The runoff also formed channels flowing eastward over the SF. This study highlights how changes in snow cover can influence active layer properties, leading to localized environmental changes and ground subsidence.

54 ENVIRONMENTAL SCIENCES↗

Orbiting Lunar Ground Penetrating Radar SAR Feasibility

This report aims to answer the question: “Will ground penetrating radar (GPR) be able to measure lunar subsurface resources from a 10km or above orbit?” Similarly, “Could a reasonable satellite-based system achieve the radar parameters required for lunar orbital GPR?” This report is not proposing a system but exploring if one is possible.

47 OTHER INSTRUMENTATION↗

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗

Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints

Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology

Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlinear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)-based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial regression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

42 ENGINEERING↗

Radar Cuts Subsoil Survey Costs

Soil features located with minimum time and labor. Ground-penetrating radar (GPR) system supplements manual and mechanical methods in performing subsurface soil survey. Mobile system obtains graphic profile of soil discontinuities and interfaces as function of depth. One or two test borings necessary to substantiate soil profile. GPR proves useful as reconnaissance tool.

Johnson, R.↗

Peat analyses in the Hudson Bay Lowlands using ground penetrating radar

The use of ground penetrating radar (GPR) as a means to determine peak thickness and estimate peat volume in the Hudson Bay Lowlands of Canada is examined. Ground-based and airborne GPR data were acquired so as to extrapolate measurements to larger scales. While the ground-based measurements did an excellent job in determining peat depth, the airborne techniques did a fair job a low altitudes and demonstrated great promise with additional system engineering changes.

Pelletier, R. E.↗

Development of a Rover Deployed Ground Penetrating Radar

Development of a rover deployable Ground Penetrating Radar (GPR) involves: the nearly finished design and testing of a transducer array with high frequency (bistatic) and low frequency (monostatic) components; and design and development of a complete impulse GPR system.

Grant, J. A.↗

Orbital SAR and Ground-Penetrating Radar for Mars: Complementary Tools in the Search for Water

The physical structure and compositional variability of the upper martian crust is poorly understood. Optical and infrared measurements probe at most the top few cm of the surface layer and indicate the presence of layered volcanics and sediments, but it is likely that permafrost, hydrothermal deposits, and transient liquid water pockets occur at depths of meters to kilometers within the crust. An orbital synthetic aperture radar (SAR) can provide constraints on surface roughness, the depth of fine-grained aeolian or volcanic deposits, and the presence of strongly absorbing near-surface deposits such as carbonates. This information is crucial to the successful landing and operation of any rover designed to search for subsurface water. A rover-based ground-penetrating radar (GPR) can reveal layering in the upper crust, the presence of erosional or other subsurface horizons, depth to a permafrost layer, and direct detection of near-surface transient liquid water. We detail here the radar design parameters likely to provide the best information for Mars, based on experience with SAR and GPR in analogous terrestrial or planetary environments.

Campbell, B. A.↗

Drilling on the Moon and Mars: Developing the Science Approach for Subsurface Exploration with Human Crews

DOMEX (Drilling on the Moon and Mars in Human Exploration) is using analog missions to develop the approach for using human crews to perform science activities on the Moon and Mars involving exploration and sampling of the subsurface. Subsurface science is an important activity that may be uniquely enabled by human crews. DOMEX provides an opportunity to plan and execute planetary mission science activities without the expense and overhead of a planetary mission. Objectives: The objective of this first in a series of DOMEX missions were to 1) explore the regional area to understand the geologic context and determine stratigraphy and geologic history of various geologic units in the area. 2) Explore for and characterize sites for deploying a deep (10 m depth) drilling system in a subsequent field season. 3) Perform GPR on candidate drill sites. 4) Select sites that represent different geological units deposited in different epochs and collect soil cores using sterile procedures for mineralogical, organic and biological analysis. 5) Operate the MUM in 3 different sites representing different geological units and soil characteristics. 6) Collect rock and soil samples of sites visited and analyze them at the habitat. Results: At mission start the crew performed a regional survey to identify major geologic units that were correlated to recognized stratigraphy and regional geologic maps. Several candidate drill sites were identified. During the rest of the mission, successful GPR surveys were conducted in four locations. Soil cores were collected in 5 locations representing soils from 4 different geologic units, to depths up to 1m. Soil cores from two locations were analyzed with PCR in the laboratory. The remainder were reserved for subsequent analysis. XRD analysis was performed in the habitat and in the field on 39 samples, to assist with sample characterization, conservation, and archiving. MUM was deployed at 3 field locations and 1 test location (outside the habitat) where it operated autonomously for 2-4 hours at each site. Depths achieved ranged from 15 to 70 cm depending on the soil compressive strength and the presence and depth of subsurface indurated layers. Subsurface samples weighing 0.5 to 1 g were collected at the deepest depth encountered at each of the sites using the MUM automated sample collection system, and subsequently analyzed with XRD. Downhole inspection of holes produced by MUM with the Raman spectrometer was acquired on two of the holes and spectral features associated with selenite were identified in specific soil layers. Previously unreported fossilized remains of vertebrate fauna from the Jurassic era were discovered during our mission. Analysis of mineral biomarkers associated with this discovery are underway.

Stoker, C. R.↗

Electromagnetic Simulations of Ground-Penetrating Radar Propagation near Lunar Pits and Lava Tubes

Placing an Orion capsule at the Earth-Moon L2 point (EML2) would potentially enable telerobotic operation of a rover on the lunar surface. The Human Exploration Virtual Institute (HEVI) is proposing that rover operations be carried out near one of the recently discovered lunar pits, which may provide radiation shielding for long duration human stays as well as a cross-disciplinary, science-rich target for nearer-term telerobotic exploration. Ground penetrating radar (GPR) instrumentation included onboard a rover has the potential to reveal many details of underground geologic structures near a pit, as well as characteristics of the pit itself. In the present work we employ the full-wave electromagnetic code MEEP to simulate such GPR reflections from a lunar pit and other subsurface features including lava tubes. These simulations will feed forward to mission concepts requiring knowledge of where to hide from harmful radiation and other environmental hazards such as plama charging and extreme diurnal temperatures.

Lava Tubes↗

Investigating Buried Ice at Askja Volcano, Northern Iceland using Ground Penetrating Radar: A Planetary Analog Perspective

Shallow subsurface ice deposits on Mars and the Moon have been identified as potential targets for in situ resource utilization by future crewed missions to the surfaces of these bodies. On Mars, such deposits have been identified through a variety of orbital measurements at the poles, mid-latitudes, and even preserved by volcanic ash near the Tharsis Montes. At some locations, massive ice deposits have been documented to be 100 m in thickness at depths as shallow as 1 m. On the Moon, the presence of thick, massive ice has not been confirmed; however, orbital observations indicate that the upper 1-2 m of regolith may contain a few weight percent in thermally favorable locations. Eruptions of the Askja Volcano in March, 1875 and November, 1961 deposited abundant pyroclasts, which blanketed and insulated fresh snowfall that later densified into massive ice and is preserved today. The pyroclasts consist of a buff colored pumice from the eruption in 1875 and basaltic lapilli and ash from the eruption in 1961. The largely unvegetated, unconsolidated nature of the pyroclasts and their stratigraphic relationships with shallow subsurface ice make them potentially analogous to some ice deposits within regolith and pyroclasts at the Moon and Mars. Ground penetrating radar(GPR) can be used by future human or robotic missions to identify water ice similarly preserved in the shallow subsurface of these bodies. Our team conducted over 66 GPR surveys inside and surrounding the Askja caldera in August 2019 at 200 and 400 MHz to map both the volcanic deposits and the subsurface ice deposits. We also used a hammer drill augur to take boreholes in order to confirm subsurface stratigraphy and the presence of ice down to 1-1.5 m and aerial surveys using a Mavic 2 Pro quadcopter at each site for additional context. We observed shallow ice deposits within pore spaces of the pyroclasts at depths of ~15-30 cm and pure ice deposits at varying depths (0.6-1 m) as thick as 2-3 m. We also plan to use the data to characterize the frequency-dependent attenuation of the radar signal as it travels through volcanoclastic material and ice in the subsurface. Our investigation of Askja as a planetary analog will provide insight into analytical methods that can be used to investigate subsurface water ice from surface operations at other terrestrial bodies.

Geophysics↗

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗

Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system

Bayesian finite element (FE) model updating using direct model evaluations of large-scale high-fidelity FE models is extremely computationally expensive. Surrogate models can be used as fast emulators of FE models to accelerate the model calibration process. The physics/mechanics-based FE models are still the underpinning behind the surrogate models. Here, this paper evaluates the loss in accuracy and the gain in computational time while performing Bayesian model updating by using surrogate model evaluations compared to using direct FE model evaluations. This evaluation is crucial before entirely relying on surrogate models in model updating for structural health monitoring (SHM) and damage prognosis (DP) purposes. This paper also demonstrates Bayesian updating and surrogate model construction of large-scale high-fidelity FE models of infrastructure systems. In this regard, the miter gate structural system is considered as the testbed structure. Three predominant damage modes (loss of contact between gate and wall, loss of thickness due to corrosion, and loss of tension in the diagonal rods) are considered for model updating purposes. Bayesian model updating is performed using direct FE evaluations by leveraging parallel computing. Two types of surrogates, namely polynomial chaos expansion (PCE) and Gaussian process regression (GPR), are developed for the miter gate. Model updating is performed again using the trained surrogate models, and the updating results are compared with their counterparts obtained using the direct FE evaluation results. The posterior distribution of the FE model parameters obtained using the trained surrogates are sufficiently accurate with respect to the posterior obtained utilizing the direct FE evaluations. In addition, an approximate 4-fold decrease in the computational time was observed when using surrogate model evaluations instead of direct FE evaluations for model updating.

42 ENGINEERING↗