Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data conditioning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
DOE OSTI2022

Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons

Abstract Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Unlocking the potential of DNA-based tagging: current market solutions and expanding horizons

The commercialization of DNA tagging is a growing trend that demonstrates the increasing practicality of this novel approach. This interdisciplinary technology is based on the distinctive characteristics of DNA as a molecule that can remain stable in varying environmental conditions and store data following appropriate preparation. Moreover, newly developed technologies could simplify DNA synthesis and the encoding of data within DNA. The implementation of DNA tagging presents distinctive benefits in comparison to conventional labelling techniques, including universal product code (UPC) barcoding, radio-frequency identification (RFID), quick response (QR) codes, and Bluetooth technologies, by surmounting the limitations encountered by these systems. The discourse pertains to extant DNA-tagging mechanisms along with prospective implementations in a wide range of domains, including but not limited to art, the metaverse, forensics, wildlife monitoring, and the military. The potential of DNA labelling in various contexts underscores the importance of continued research and development in this rapidly evolving field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations

In situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP 2 EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Time-resolved photoelectron imaging of complex resonances in molecular nitrogen

We have used the FERMI free-electron laser to perform time-resolved photoelectron imaging experiments on a complex group of resonances near 15.38 eV in the absorption spectrum of molecular nitrogen, N-2, under jet-cooled conditions. The new data complement and extend the earlier work of Fushitani et al. [Opt. Express 27, 19702-19711 (2019)], who recorded time-resolved photoelectron spectra for this same group of resonances. Time-dependent oscillations are observed in both the photoelectron yields and the photoelectron angular distributions, providing insight into the interactions among the resonant intermediate states. In addition, for most states, we observe an exponential decay of the photoelectron yield that depends on the ionic final state. This observation can be rationalized by the different lifetimes for the intermediate states contributing to a particular ionization channel. Finally, although there are nine resonances within the group, we show that by detecting individual photoelectron final states and their angular dependence, we can identify and differentiate quantum pathways within this complex system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Large-eddy simulation of a multi-injection flame in a diesel engine environment using an unsteady flamelet/progress variable approach

In this work, large-eddy simulations (LESs) are conducted for a multiple-injection flame in a diesel engine environment using an unsteady flamelet/progress variable (UFPV) approach in which differential diffusion is considered. The suitability of the UFPV tabulation approach is first evaluated through a priori analyses using the state-of-the-art direct numerical simulation (DNS) dataset. Both the instantaneous data and the conditional values for the major and minor species' mass fractions are compared between the UFPV and the DNS. The comparisons show that the proposed UFPV tabulation approach can give good predictions for the multiple-injection flame at different injection phases. While the gas temperature and major species mass fractions can be accurately predicted with or without differential diffusion being considered in the UFPV flamelet library, the prediction accuracy for the highly diffusive species (e.g., hydrogen) in the main injection phase can be noticeably improved when differential diffusion is taken into account. The fully coupled LES/UFPV simulations show that the overall structure of the multiple-injection flame can be predicted, and the conditional thermo-chemical values are close to the filtered DNS dataset. The reasons for the remaining discrepancies found in the a priori analyses and the a posteriori simulations using the UFPV approach are analyzed.

Wen, Xu (ORCID:0000000331334292)↗
DOE OSTI2026

Experimental shock Hugoniot and melt boundary temperatures of aluminum

Aluminum is a ubiquitous component in dynamic compression, pulsed power, and other high energy density physics studies. Its high-pressure behavior and phase diagram are extensively studied standards in shockwave physics. While theoretical calculations and multiphase equations of state have been benchmarked to velocity measurements of loading and unloading waves, pressure and density under shock, and other mechanical data, experimental temperature data under these conditions have not been reported. We conducted a series of experiments shocking and releasing aluminum 6061 and 1100 samples into lithium fluoride windows. We measured temperature at the sample–window interface under steady compression and subsequent isentropic release. These results allow us to constrain the temperature of the solid Hugoniot and the boundary between the liquid and face-centered cubic solid phases.

Hartsfield, Thomas Murray [Sandia National Laborat↗
DOE OSTI2023

Clear-Sky Detection Using Time-Averaged, Tilted-Plane Data

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

clear-sky conditions↗
DOE OSTI2020

Comparison of Model Predictions and Performance Test Data for a Prototype Thermal Energy Storage Module

Although model predictions of thermal energy storage (TES) performance have been explored in previous investigations, relevant test data that enable experimental validation of performance models have been limited. This is particularly true for high-performance TES designs that facilitate fast input and extraction of energy. In this paper, we present a summary of experimental tests of a high-performance TES unit using lithium nitrate trihydrate phase change material as a storage medium. Performance data are presented for complete dual-mode cycles consisting of extraction (melting) followed by charging (freezing). These tests simulate the cyclic operation of a TES unit for asynchronous cooling in a variety of applications. Finally, the model analysis is found to agree reasonably well, within 10%, with the experimental data except for conditions very near the initiation of freezing, a consequence of subcooling that is required to initiate solidification.

25 ENERGY STORAGE↗
DOE OSTI2023

Binary pseudo-random array (BPRA) for inspection and calibration for cylindrical wavefront interferometry

High-accuracy metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate X-ray light with nanometer-scale wavelengths. However, surface topography measurements are instrument dependent, and without the knowledge of how the instrument performs under the practical usage conditions, the measured data contain some degree of uncertainty. Binary Pseudo Random Array (BPRA) “white noise” artifact are effective and useful for characterizing the Instrument Transfer Function (ITF) of surface topography metrology tools and wavefront measurement instrument. BPRA artifact contains features with all spatial frequencies in the instrument bandpass with equal weight. As a result, power spectral density of the patterns has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range. The application examples include electron microscopes, x-ray microscopes, interferometric microscopes, and large field-of-view Fizeau Interferometers. Furthermore, we will introduce the application of BPRA method to characterizing the ITF of Cylindrical Wavefront Interferometry (CWI), by developing the BPRA artifact which matches the radius of curvature of the cylindrical wavefront. The data acquisition and analysis procedures for different applications of the ITF calibration technique developed are also discussed.

Munechika, K↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2021

Attend and Decode: 4D fMRI Task State Decoding Using Attention Models

Source code for Brain Attend and Decode paper. Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is challenging due to the high dimensionality (hundreds of million sampling points per datum) and complex spatio-temporal blood flow patterns inherent in the data. In this work, we propose to tackle the fMRI task state decoding problem by casting it as a 4D spatiotemporal classification problem. We present a novel architecture called Brain Attend and Decode (BAnD), that uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms for temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the large-scale Human Connectome Project-Young Adult (HCP-YA) dataset. We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model. The pre-trained features from BAnD are useful on similar tasks while finetuning them yields competitive results on unseen tasks/conditions.

Ng, BrendaM.↗
DOE OSTI2020

Second Annual Progress Report on Correlation Between Microstructure and Mechanical Properties of Neutron-Irradiated Ferritic-Martensitic and Austenitic Steels

Ferritic-martensitic steels G92-2b (an optimized Grade 92 heat), NF616 and T91, and austenitic stainless steel 800H and its Grain Boundary Engineering (GBE)-treated version 800H-TMP (ThermoMechanical Processing) were irradiated in the High Flux Isotope Reactor (HFIR) of Oak Ridge National Laboratory (ORNL) and the Advanced Test Reactor (ATR) of Idaho National Laboratory (INL). Selected G92-2b samples were irradiated up to 14.66 dpa in the HFIR at two temperature ranges: 400–496.7°C and 683.3– 720°C. NF616 and T91 were irradiated in the ATR up to 8.16 dpa with the irradiation temperatures ranged from 241°C to 447.5°C. Alloy 800H and 800H-TMP samples were irradiated in both the HFIR and the ATR. Selected 800H and 800H-TMP samples had HFIR irradiation to 1.28 dpa at 580°C and ATR irradiation up to 9.12 dpa at 359°C to 431°C. Vickers hardness measurements, fractography, and microstructural characterization were performed on the selected samples in the Low Activation Materials Design and Analysis (LAMDA) laboratory. Radiation-hardening of G92-2b was observed at the lower doses and lower irradiation temperatures (400- 496.7°C), with GB03 (0.52 dpa at 400°C) and GB04 (7.44 dpa at ~490°C) showing ~12% and ~8% hardening, respectively. Softening by ~14% was observed for GB05 (14.66 dpa at 496.7°C). Radiationsoftening of G92-2b was more prevalent at the higher irradiation temperatures (683.3-~720°C), with GB10 (0.46 dpa at 683.3°C), GB11 (7.44 dpa at ~720°C), and GB12 (14.63 dpa at ~720°C) showing ~8%, ~8%, and ~40% softening, respectively. Radiation-hardening of NF616 and T91 was observed with the hardness increased by ~37% to ~65% depending on the irradiation doses and irradiation temperatures. Within the studied irradiation conditions of NF616 and T91, samples with a higher dose had a larger hardness after irradiation. All the tested alloy 800H and 800H-TMP samples in this work showed radiation-hardening by ~96±7% to ~152±10%. Alloy 800H-TMP tended to have slightly smaller radiation-hardening than alloy 800H. The fractography results of G92-2b sample GB03, GB10, and GB11, together with the previously characterized fractography of GB04, GB05, and GB12, indicated that the ductility of G92-2b was maintained up to 14.66 dpa at the lower irradiation temperatures of 400-496.7°C, while some loss of ductility (less necking) was observed for higher doses at the higher irradiation temperatures of 683.3-720°C. This agrees with the previously reported tensile test results of G92-2b, where the elongation of G92-2b was reduced at higher doses at the higher irradiation temperatures. Dimple sizes increased at higher doses, which are more evident at the higher irradiation temperatures of 683.3-~720°C. The fractography of NF616 sample D2 (2.96 dpa at 291.5°C), D4 (5.91 dpa at 359°C), and D6 (8.16 dpa at 431°C) indicated loss of ductility with negligible necking for sample D2, while ductile failure for samples D4 and D6. Fractography of alloy 800H and 800H-TMP samples in various irradiation conditions showed ductile failure with obvious necking. Dimples were observed, with some of them containing large Ti-rich particles, in all the 800H/800H-TMP samples. Electron backscatter diffraction characterization of GB12 indicated the recovery of the lath structure, which was generally replaced by an equiaxed grain structure. Transmission electron microscopy (TEM) characterization showed the presence of frequent M 23 C 6 (M = primarily Cr), MX (M = primarily V), spherical Nb(C,N) precipitates, and occasional Laves phase precipitates in the G92-2b samples. MX precipitates with sizes of 20-30 nm were observed at boundaries of smaller grains, indicating the pinning effect of the V-rich precipitates. The lath structure recovery was more evident at the higher irradiation temperatures (683.3-~720°C), with decreased densities of line dislocations and M23C6 precipitates. The irradiated T91 (TA04) showed the growth of M23C6 precipitates to 101 ± 40 nm from the initial 68 ± 22 nm in the unirradiated condition. Dislocation loops of both {100} and {111} types were present in TA04. TEM characterization was also performed on the irradiated 800H (N4, N5, N6, and AR2) and 800H-TMP (P4, P5, P6, and HG1). Accumulation of M 23 C 6 precipitates at grain boundaries was observed in all the xii 800H/800H-TMP samples, and the presence of Ti(C,N) precipitates at grain boundaries and in the matrix was observed in the irradiated 800H-TMP. Some Ti(C,N) precipitates are embedded in the M 23 C 6 precipitates, maintaining specific orientation relationships between the precipitates and between the precipitate and matrix. In addition, nanoscale Si-rich clusters were observed in the matrix of all the 800H/800H-TMP samples, with EDS Si maps tending to have a lower contrast in 800H-TMP samples. Atom probe tomography was conducted on the same samples, supported by a Rapid Turnaround Examination project under Nuclear Science User Facilities. The results are being analyzed to be integrated with the TEM results for a confident description of the γ’ precipitates. Dislocation loops also formed in all the 800H/800H-TMP samples. The density and the average size of dislocation loops were quantified to be in the order of 10 22 – 10 23 m -3 and 11.7 – 15.9 nm, respectively, in the ATR-irradiated 800H/800H-TMP samples. The loop densities in irradiated 800H were higher than that in irradiated 800H-TMP under the same irradiation conditions. Further systematic data analyses, together with some complementary experiments, will be pursued for these samples to foster peer-reviewed journal article publications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Report on partially complete post-irradiation-examination of the INL samples

Ferritic-martensitic steels (NF616 and T91), and austenitic stainless steel 800H and its Grain Boundary Engineering (GBE)-treated version 800H-TMP (ThermoMechanical Processing) were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) and their microstructures and mechanical properties were characterized. Ferritic-martensitic steels were irradiated up to 8.16 dpa in the irradiation temperatures ranging from 241 °C to 447.5 °C. Selected 800H and 800H-TMP samples were also irradiated up to 9.12 dpa at 359°C to 431°C. Vickers hardness measurement, fractography, and microstructural characterization were performed on the selected samples in the LAMDA lab. Radiation-induced hardening of NF616 and T91 was observed in the hardness measurements which show ~37% to ~65% increases, depending on irradiation dose and irradiation temperature. Within the irradiation conditions studied in this work, samples with a higher dose had a higher hardness. All the tested alloy 800H and 800H-TMP in this work showed irradiation-hardening by ~96±7% to ~152±10%. Alloy 800HTMP tended to have slightly smaller irradiation-hardening than alloy 800H. The fractography of NF616 samples D2 (2.96 dpa at 291.5°C), D4 (5.91 dpa at 359°C), and D6 (8.16 dpa at 431°C) indicated loss of ductility with negligible necking for the sample D2, yet obvious ductile failure for samples D4 and D6. Fractography of alloy 800H samples N4 (7.27 dpa at 359°C), N5 (3.9 dpa at 451.5°C), and N6 (9.01 dpa at 431°C), and alloy 800H-TMP samples P4 (7.36 dpa at 359°C), P5 (3.95 dpa at 451.5°C), and P6 (9.12 dpa at 431°C) indicated that all samples failed in a ductile mode, with obvious necking. Dimples were observed where large Ti-containing particles were observed for the samples. TEM characterization was performed on alloy 800H (N4, N5, N6) and 800H-TMP (P4, P5 and P6). Accumulation of M23C6 precipitates at grain boundaries was observed in all samples, and the presence of Ti(C,N) precipitates at grain boundaries and in the matrix was observed in alloy 800H-TMP. M23C6 precipitates were found to have the cube-on-cube orientation relationship with the matrix. In addition, nanoscale Si-rich clusters were observed in the matrix of all samples. Atom probe tomography was conducted on the same samples, and their results are being analyzed to be integrated with the TEM results for a confident description of the clustering behavior. Due to the low irradiation temperature (up to 451.5°C), dislocation loops formed in all samples, with the density and the average size of dislocation loops in the order of 1022 – 1023 m-3 and 11.7 – 15.9 nm, respectively. The loop densities in alloy 800H were higher than that in alloy 800H-TMP under the same irradiation conditions. Further systematic data analyses, together with some complementary experiments, will be pursued for these samples to foster peer-reviewed journal article publications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

SNL will be able to provide well-calibrated models to weapons systems analysts by integrating an engine for constitutive model calibration with the GRANTA materials database

Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection

Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.

58 GEOSCIENCES↗
DOE OSTI2021

Predictive Data-driven Platform for Subsurface Energy Production

Subsurface energy activities such as unconventional resource recovery, enhanced geothermal energy systems, and geologic carbon storage require fast and reliable methods to account for complex, multiphysical processes in heterogeneous fractured and porous media. Although reservoir simulation is considered the industry standard for simulating these subsurface systems with injection and/or extraction operations, reservoir simulation requires spatio-temporal “Big Data” into the simulation model, which is typically a major challenge during model development and computational phase. In this work, we developed and applied various deep neural network-based approaches to (1) process multiscale image segmentation, (2) generate ensemble members of drainage networks, flow channels, and porous media using deep convolutional generative adversarial network, (3) construct multiple hybrid neural networks such as convolutional LSTM and convolutional neural network-LSTM to develop fast and accurate reduced order models for shale gas extraction, and (4) physics-informed neural network and deep Q-learning for flow and energy production. We hypothesized that physicsbased machine learning/deep learning can overcome the shortcomings of traditional machine learning methods where data-driven models have faltered beyond the data and physical conditions used for training and validation. We improved and developed novel approaches to demonstrate that physics-based ML can allow us to incorporate physical constraints (e.g., scientific domain knowledge) into ML framework. Outcomes of this project will be readily applicable for many energy and national security problems that are particularly defined by multiscale features and network systems.

58 GEOSCIENCES↗
DOE OSTI2022

Benchmark Specification for Select Experiments Conducted at the University of Wisconsin-Madison Thermal Stratification Test Facility

The Department of Energy (DOE)-Nuclear Energy University Programs (NEUP) supported the creation and operation of the Thermal Stratification Test Facility (TSTF) at the University of Wisconsin Madison (UWM) as part of a larger effort to understand thermal stratification behavior in liquid-metal-cooled reactors. The TSTF was designed to simulate transients in a reactor plenum that are known to cause thermal stratification. High-reliability and high-resolution measurements of the flow and temperature were collected for use as experimental benchmarks to support validation efforts for computational models. The results of these tests contribute to the greater understanding of thermal stratification behavior of liquid sodium under various configurations and operating conditions. The six TSTF tests selected for benchmarking are a set of forced circulation tests at a fixed flow rate with different Upper Internal Structure (UIS) configurations in the test section (no UIS, solid UIS, and a UIS with flow area of 4, 8, 12, and 100%). This report provides a complete description of the benchmark problems, including all key test facility details, descriptions of each test condition, and measured data for comparison with modeled results.

42 ENGINEERING↗