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

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Long Duration Head Down Tilt Bed Rest and Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

We have recently completed a long duration head down tilt bed rest (HDBR) study in which we performed structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations in a spaceflight analog environment. We are also collecting the same measures in crewmembers prior to and following a six month International Space Station mission. We will present data demonstrating that bed rest resulted in functional mobility and balance deterioration with recovery post-HDBR. We observed numerous changes in brain structure, function, and connectivity relative to a control group which were associated with pre to post bed rest changes in sensorimotor function. For example, gray matter volume (GMv) increased in posterior parietal areas and decreased in frontal regions. GMv increases largely overlapped with fluid decreases and vice versa. Larger increases in precentral gyrus (M1)/ postcentral gyrus (S1+2) GMv and fluid decreases were associated with smaller balance decrements. Vestibular activation in the bilateral insular cortex increased with bed rest and subsequently recovered. Larger increases in vestibular activation in multiple brain regions were associated with greater decrements in balance and mobility. We found connectivity increases between left M1 with right S1+2 and the superior parietal lobule, and right vestibular cortex with the cerebellum. Decreases were observed between right Lobule VIII with right S1+2 and the supramarginal gyrus, right posterior parietal cortex (PPC) with occipital regions, and the right superior posterior fissure with right Crus I and II. Connectivity strength between left M1 and right S1+2/superior parietal lobule increased the most in individuals that exhibited the least balance impairments. In sum, we observed HDBR-related changes in measures of brain structure, function, and network connectivity, which correlated with indices of sensorimotor function. Recovery was observed post HDBR but remained incomplete at 12 days post-HDBR. Preliminary findings from our parallel ongoing flight study will be compared and contrasted with bed rest results during this presentation.

Seidler, R. D.↗
DOE OSTI2023

Human cerebrospinal fluid contains diverse lipoprotein subspecies enriched in proteins implicated in central nervous system health

Lipoproteins in cerebrospinal fluid (CSF) of the central nervous system (CNS) resemble plasma high-density lipoproteins (HDLs), which are a compositionally and structurally diverse spectrum of nanoparticles with pleiotropic functionality. Whether CSF lipoproteins (CSF-Lps) exhibit similar heterogeneity is poorly understood because they are present at 100-fold lower concentrations than plasma HDL. To investigate the diversity of CSF-Lps, we developed a sensitive fluorescent technology to characterize lipoprotein subspecies in small volumes of human CSF. We identified 10 distinctly sized populations of CSF-Lps, most of which were larger than plasma HDL. Mass spectrometric analysis identified 303 proteins across the populations, over half of which have not been reported in plasma HDL. Computational analysis revealed that CSF-Lps are enriched in proteins important for wound healing, inflammation, immune response, and both neuron generation and development. Network analysis indicated that different subpopulations of CSF-Lps contain unique combinations of these proteins. Our study demonstrates that CSF-Lp subspecies likely exist that contain compositional signatures related to CNS health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1997

Visual Computing Environment

The Visual Computing Environment (VCE) is a NASA Lewis Research Center project to develop a framework for intercomponent and multidisciplinary computational simulations. Many current engineering analysis codes simulate various aspects of aircraft engine operation. For example, existing computational fluid dynamics (CFD) codes can model the airflow through individual engine components such as the inlet, compressor, combustor, turbine, or nozzle. Currently, these codes are run in isolation, making intercomponent and complete system simulations very difficult to perform. In addition, management and utilization of these engineering codes for coupled component simulations is a complex, laborious task, requiring substantial experience and effort. To facilitate multicomponent aircraft engine analysis, the CFD Research Corporation (CFDRC) is developing the VCE system. This system, which is part of NASA's Numerical Propulsion Simulation System (NPSS) program, can couple various engineering disciplines, such as CFD, structural analysis, and thermal analysis. The objectives of VCE are to (1) develop a visual computing environment for controlling the execution of individual simulation codes that are running in parallel and are distributed on heterogeneous host machines in a networked environment, (2) develop numerical coupling algorithms for interchanging boundary conditions between codes with arbitrary grid matching and different levels of dimensionality, (3) provide a graphical interface for simulation setup and control, and (4) provide tools for online visualization and plotting. VCE was designed to provide a distributed, object-oriented environment. Mechanisms are provided for creating and manipulating objects, such as grids, boundary conditions, and solution data. This environment includes parallel virtual machine (PVM) for distributed processing. Users can interactively select and couple any set of codes that have been modified to run in a parallel distributed fashion on a cluster of heterogeneous workstations. A scripting facility allows users to dictate the sequence of events that make up the particular simulation.

Lawrence, Charles↗
DOE OSTI2021

Liquid-induced topological transformations of cellular microstructures

The fundamental topology of cellular structures—the location, number and connectivity of nodes and compartments—can profoundly affect their acoustic, electrical5, chemical, mechanical and optical properties, as well as heat, fluid and particle transport. Approaches that harness swelling, electromagnetic actuation and mechanical instabilities in cellular materials have enabled a variety of interesting wall deformations and compartment shape alterations, but the resulting structures generally preserve the defining connectivity features of the initial topology. Achieving topological transformation presents a distinct challenge for existing strategies: it requires complex reorganization, repacking, and coordinated bending, stretching and folding, particularly around each node, where elastic resistance is highest owing to connectivity. Here we introduce a two-tiered dynamic strategy that achieves systematic reversible transformations of the fundamental topology of cellular microstructures, which can be applied to a wide range of materials and geometries. Our approach requires only exposing the structure to a selected liquid that is able to first infiltrate and plasticize the material at the molecular scale, and then, upon evaporation, form a network of localized capillary forces at the architectural scale that ‘zip’ the edges of the softened lattice into a new topological structure, which subsequently restiffens and remains kinetically trapped. Reversibility is induced by applying a mixture of liquids that act separately at the molecular and architectural scales (thus offering modular temporal control over the softening–evaporation–stiffening sequence) to restore the original topology or provide access to intermediate modes. Guided by a generalized theoretical model that connects cellular geometries, material stiffness and capillary forces, we demonstrate programmed reversible topological transformations of various lattice geometries and responsive materials that undergo fast global or localized deformations. In conclusion, we then harness dynamic topologies to develop active surfaces with information encryption, selective particle trapping and bubble release, as well as tunable mechanical, chemical and acoustic properties.

42 ENGINEERING↗
DOE OSTI2023

Utah FORGE 4-2492: Design and Implementation of Innovative Stimulation Treatments to Maximize Energy Recovery - Workshop Presentation

This is a presentation on the Design and Implementation of Innovative Stimulation Treatments to Maximize Energy Recovery Efficiency at the Utah FORGE Site project by The University of Texas at Austin, presented by Professor Mukul M. Sharma. The project's objectives were to place fractures uniformly in a horizontal well (improve cluster efficiency) to ensure uniform distribution of flow into all hydraulic fractures, maximize the area of the created fracture network, ensure connectivity of the fractures from the injector to the producer, and optimize fracture size. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2011

Methane Recovery from Hydrate-bearing Sediments

Gas hydrates are crystalline compounds made of gas and water molecules. Methane hydrates are found in marine sediments and permafrost regions; extensive amounts of methane are trapped in the form of hydrates. Methane hydrate can be an energy resource, contribute to global warming, or cause seafloor instability. This study placed emphasis on gas recovery from hydrate bearing sediments and related phenomena. The unique behavior of hydrate-bearing sediments required the development of special research tools, including new numerical algorithms (tube- and pore-network models) and experimental devices (high pressure chambers and micromodels). Therefore, the research methodology combined experimental studies, particle-scale numerical simulations, and macro-scale analyses of coupled processes. Research conducted as part of this project started with hydrate formation in sediment pores and extended to production methods and emergent phenomena. In particular, the scope of the work addressed: (1) hydrate formation and growth in pores, the assessment of formation rate, tensile/adhesive strength and their impact on sediment-scale properties, including volume change during hydrate formation and dissociation; (2) the effect of physical properties such as gas solubility, salinity, pore size, and mixed gas conditions on hydrate formation and dissociation, and it implications such as oscillatory transient hydrate formation, dissolution within the hydrate stability field, initial hydrate lens formation, and phase boundary changes in real field situations; (3) fluid conductivity in relation to pore size distribution and spatial correlation and the emergence of phenomena such as flow focusing; (4) mixed fluid flow, with special emphasis on differences between invading gas and nucleating gas, implications on relative gas conductivity for reservoir simulations, and gas recovery efficiency; (5) identification of advantages and limitations in different gas production strategies with emphasis; (6) detailed study of CH4-CO2 exchange as a unique alternative to recover CH4 gas while sequestering CO2; (7) the relevance of fines in otherwise clean sand sediments on gas recovery and related phenomena such as fines migration and clogging, vuggy structure formation, and gas-driven fracture formation during gas production by depressurization.

03 NATURAL GAS↗
DOE OSTI2024

Direct numerical simulation of open-channel flow over a heterogeneous particle bed at low relative submergence

In this article, this study investigates turbulent open-channel flows over beds of irregularly arranged particles, using direct numerical simulations at a friction Reynolds number of Re T = 300⁠. Two distinct cases are examined: a polydisperse bed (P800) composed of multiple layers of randomly distributed spheres of varying sizes, and a monodisperse bed (M1015) formed by a random distribution of uniform sized spheres, with a bottommost single layer of varied-sized particles to introduce realistic randomness. Our investigation unveils a rich network of low- and high-speed streaks within the flow field, exhibiting distinctive behaviors in different bed configurations. The P800 case presents a poorly organized flow pattern induced by the varied particle sizes and arrangements, while the M1015 case shows a more regular flow pattern, marked by larger streaks. We also observe that total wall shear stress is substantially influenced by surface roughness-induced drag, extending beyond the effects documented in existing studies of open-channel flows. The present study reveals intricate secondary flow patterns over irregular particle beds. Large-scale circulations are discerned around particle crests in the P800 case and localized circulations with increased turbulence in the M1015 case. Furthermore, analysis of Reynolds stress tensor components indicates that roughness disrupts coherent turbulent eddies, consequently mitigating peak stress. We quantify correlations between drag force and local fluid velocity fluctuations. Notably, a larger deviation in drag is observed in the P800 case compared to M1015, accentuating the influence of particle size and distribution on fluid–particle interactions.

42 ENGINEERING↗
DOE OSTI2016

Enhanced working memory performance via transcranial direct current stimulation: The possibility of near and far transfer

Although working memory (WM) training programs consistently result in improvement on the trained task, benefit is typically short-lived and extends only to tasks very similar to the trained task (i.e., near transfer). It is possible that pairing repeated performance of a WM task with brain stimulation encourages plasticity in brain networks involved in WM task performance, thereby improving the training benefit. In the current study, transcranial direct current stimulation (tDCS) was paired with performance of a WM task (n-back). In Experiment 1, participants performed a spatial location-monitoring n-back during stimulation, while Experiment 2 used a verbal identity-monitoring n-back. In each experiment, participants received either active (2.0 mA) or sham (0.1 mA) stimulation with the anode placed over either the right or the left dorsolateral prefrontal cortex (DLPFC) and the cathode placed extracephalically. In Experiment 1, only participants receiving active stimulation with the anode placed over the right DLPFC showed marginal improvement on the trained spatial n-back, which did not extend to a near transfer (verbal n-back) or far transfer task (a matrix-reasoning task designed to measure fluid intelligence). Here, in Experiment 2, both left and right anode placements led to improvement, and right DLPFC stimulation resulted in numerical (though not sham-adjusted) improvement on the near transfer (spatial n-back) and far transfer (fluid intelligence) task. Results suggest that WM training paired with brain stimulation may result in cognitive enhancement that transfers to performance on other tasks, depending on the combination of training task and tDCS parameters used.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Generating one-column grids with fractal flow dimension

The grid generation capability built into the numerical simulator TOUGH for multi-phase fluid and heat flow through geologic media can create one-column grids with linear or radial geometry, corresponding to one-dimensional or two-dimensional radial flow, respectively. The integral-finite-difference-method that TOUGH employs for spatial discretization makes it very simple to generalize the grid-generation algorithm from integer to non-integer (fractal) flow dimension. Here the grid-generation algorithm is generalized to create one-column grids with fractal flow dimension ranging from less than 1 to 3. The fractal grid generation method is verified by comparing numerical simulation results to an analytical solution for a generalized Theis solution for integer and non-integer flow dimensions between 0.4 and 3. It is then applied to examine gas production decline curves from hydraulically fractured shale that is modeled as a fractal-dimensioned fracture network with flow dimensions between 0.25 and 3. Grids with fractal flow dimension are useful for representing flow through fracture networks or highly heterogeneous geologic media with fractal geometry, and may be particularly useful for inverse methods.

58 GEOSCIENCES↗
DOE OSTI2016

Artificial Neural Network Representation of Dynamic Criticality Excursion Experiment Data

This paper outlines time dependent considerations in applying the artificial neural network (ANN) paradigm to dynamic nuclear criticality excursion experiment data. The ANN amongst a variety of uses, can apply to function fitting of multiple input-multiple output data sets, pattern recognition, cluster analysis, and time series prediction. The ANN is a component of Machine Learning (ML) data analysis. The practical application of ML to criticality safety has been funded through the Y-12 Plant Directed Research and Demonstration (PDRD) program to support criticality safety and emergency planning efforts. The initial effort considered function fitting of criticality excursion data. Recent efforts successfully extended the ANN to criticality safety code validation. The initial ANN work applied to criticality excursions centered around multivariate function fitting of static (tabular) data taken from the French Consequences Radiologiques d'un Accident de Criticite (CRAC) and SILENE reactor campaigns. The former report (LeCorche) summarized important tabular data for the first 44 CRAC experiments. The initial ANN was trained to specific time-independent inputs, then applied to predict peak fission rate and total fissions for a wide variety of fissile solutions process conditions. This work extends that effort to apply the ANN paradigm to dynamic prediction of time-dependent criticality excursion experiment data. A dynamic criticality excursion in fissile solution represents a complex multiphysics phenomenon. Factors include among other things, coupled neutronic, multiphase fluid flow, temperature, pressure, acoustic shock, boiling heat transfer, reactivity feedback, and radiolytic gas considerations. There have been many efforts to model (over time) as coupled ordinary and (space) as partial differential equations (ODE, PDE). The ANN representation noted here does not rely on solving explicit ODE or PDEs. Rather the intent is to demonstrate that as a time series, the criticality excursion can be predicted by training specific ANN architectures to experimental input and output time series exclusively. The rationale for extending this work to the time-dependent regime is important in developing a 'criticality excursion emulator' solely on the basis of learning characteristics from experiment dynamic data. An emulator is different than a computer code simulator driven by the solution of ODEs and PDEs given select 'tuning parameters' that match outputs to experiments. A criticality excursion emulator, derived exclusively from an ANN or ML, could predict most likely excursion characteristics, trained from historic dynamic experiment data, presented with input values within the range of those experiments. The work noted shows that generic time-series ANN architectures can be satisfactorily applied to the prediction of dynamic, extremely chaotic criticality excursion power given an initial time dependent input series. In reality, an excursion emulator could be driven by user-supplied initial (t=0), time independent or constant values to generate time dependent outputs. Thus ongoing work includes developing a custom ANN such that initial dynamic conditions would be predicted. Those results then serve as inputs to a customized ANN architecture currently in development. (author)

42 ENGINEERING↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Nanoscale dynamics and aging of fibrous peptide-based gels

Solutions of the aromatic dipeptide derivative molecule fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF) in dimethyl sulfoxide produce fibrous gels when mixed with water. We study the evolution of density fluctuations of this three-component system using X-ray photon correlation spectroscopy (XPCS) and compare these results to the macroscopic rheology of the gels and optical observations of the microstructure evolution. At the investigated scattering angles, the intensity autocorrelation functions do not follow behavior expected for simple diffusion of individual Fmoc-FF molecules localized within cages of nearest neighbors. Instead, the dynamics are associated with density fluctuations on length scales of ∼10–100 nm arising from disaggregation and reformation of fibers, leading to an increasingly uniform network. This process is correlated with the growth of the elastic modulus, which saturates at long times. Autocorrelation functions and relaxation times acquired from XPCS measurements are consistent with relaxation rates of structures at dynamic equilibrium. This study provides further support to the concept of exploring peptide-based gelators as valence-limited patchy particles capable of forming equilibrium gels.

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

Room temperature depinning of the charge-density waves in quasi-two-dimensional 1T-TaS 2 devices

Here we report on the depinning of nearly commensurate charge-density waves in 1T-TaS 2 thin films at room temperature. A combination of the differential current–voltage measurements with the low-frequency noise spectroscopy provides unambiguous means for detecting the depinning threshold field in quasi-2D materials. The depinning process in 1T-TaS 2 is not accompanied by an observable abrupt increase in electric current—in striking contrast to depinning in the conventional charge-density-wave materials with quasi-1D crystal structure. We explained it by the fact that the current density from the charge-density waves in the 1T-TaS 2 devices is orders of magnitude smaller than the current density of the free carriers available in the discommensuration network surrounding the commensurate charge-density wave islands. The depinning fields in 1T-TaS 2 thin-film devices are several orders of magnitude larger than those in quasi-1D van der Waals materials. Obtained results are important for the proposed applications of the charge-density wave devices in electronics.

2D materials↗
NASA NTRSDate not supplied

Plant Water Management in Microgravity

The NASA Plant Water Management (PWM) technology demonstrations aboard ISS apply recent advances in microgravity capillary fluidics research towards the mundane yet problematic challenges of simply watering plants in space. Plant growth in a low-g environment is often hampered by inadequate aeration and over-saturation of the root zone. The present effort aims to exploit the passive capillary forces of poorly wetting liquids (i.e., contaminated water) within unique system geometries that effectively replace the role of gravity in providing sufficient aeration and hydration for simulated plants. Several flight demonstrations have been completed on ISS, including soil and hydroponic models in single and parallel channel networks. Two future demonstrations are still in work and plan to further develop the system to handle sustained plant growth with additional sensors to monitor the growth environment. The results to date demonstrate proof-of-concept, system stability, limits of operation, and more, for simulated plant models. Eventually, real plants will be incorporated into these systems and tested on orbit. The implications are discussed in relation to plant growth facilities for further near-term microgravity plant science research as well as for automated food production for long duration human exploration missions.

microgravity↗
DOE OSTI2021

High-temperature high-pressure microfluidic system for rapid screening of supercritical CO 2 foaming agents

CO 2 foam helps to increase the viscosity of CO 2 flood fluid and thus improve the process efficiency of the anthropogenic greenhouse gas’s subsurface utilization and sequestration. Successful CO 2 foam formation mandates the development of high-performance chemicals at close to reservoir conditions, which in turn requires extensive laboratory tests and evaluations. This work demonstrates the utilization of a microfluidic reservoir analogue for rapid evaluation and screening of commercial surfactants (i.e., Cocamidopropyl Hydroxysultaine, Lauramidopropyl Betaine, Tallow Amine Ethoxylate, N,N,N' Trimethyl-N'-Tallow-1,3-diaminopropane, and Sodium Alpha Olefin Sulfonate) based on their performance to produce supercritical CO 2 foam at high salinity, temperature, and pressure conditions. The microfluidic analogue was designed to represent the pore sizes of the geologic reservoir rock and to operate at 100 °C and 13.8 MPa. Values of the pressure drop across the microfluidic analogue during flow of the CO 2 foam through its pore network was used to evaluate the strength of the generated foam and utilized only milliliters of liquid. The transparent microfluidic pore network allows in-situ quantitative visualization of CO 2 foam to calculate its half-life under static conditions while observing if there is any damage to the pore network due to precipitation and blockage. The microfluidic mobility reduction results agree with those of foam loop rheometer measurements, however, the microfluidic approach provided more accurate foam stability data to differentiate the foaming agent as compared with conventional balk testing. The results obtained here supports the utility of microfluidic systems for rapid screening of chemicals for carbon sequestration or enhanced oil recovery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Application of predictive data analytics to model daily hydrocarbon production using petrophysical, geomechanical, fiber-optic, completions, and surface data: A case study from the Marcellus Shale, North America

Predicting gas production from stimulated unconventional reservoirs has always been a challenge to oil and gas companies because the physics of fluid flow through such reservoirs is not well understood. On the other hand, modeling gas production from shale reservoirs is a complex multi-variate problem that requires proper integration of data from multiple disciplines, such as geology, petrophysics, geomechanics, completions, and surface-based measurements, etc. Here this study demonstrates the application of data-driven machine learning algorithms, integrating geoscientific, distributed acoustic sensing (DAS), distributed temperature sensing (DTS) fiber-optic, completions, flow scanner production log, and surface data to model daily gas production from a 28-stage stimulated horizontal well drilled in the Marcellus Shale of the Appalachian basin in North America. In addition, this study aims to utilize the data from a fiber-optic monitoring system, such as DAS and DTS to evaluate the well performance. We build supervised data-driven machine learning models using Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms to understand the well performance and forecast the daily gas production. The study compares the forecasted results, tests the level of accuracy, and addresses different issues with these machine learning algorithms. A spatio-temporal database is constructed and used to perform sensitivity analyses to identify the key drivers for gas production. The results show that RF and ANN algorithms can be used to predict daily gas production with significantly high accuracy; however, RF algorithm is the best predictor in terms of highest accuracy (96%), less computational time and cost. Based on sensitivity analysis, it appears that Poisson's ratio, minimum horizontal stress, DAS, casing pressure, gamma log are the most important parameters to predict gas production.

02 PETROLEUM↗