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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Synthetic Coal Slag Infiltration into Varying Refractory Materials

The infiltrations of synthetic coal slag into 99%Al{sub 2}O{sub 3}, 85%Al{sub 2}O{sub 3}–15%SiO{sub 2}, and 90%Cr{sub 2}O{sub 3}–10%Al{sub 2}O{sub 3} refractories with a temperature gradient induced along the penetration direction were compared to one another. The infiltrating slag was synthesized with a composition that is representative of an average of the ash contents from U S coal feedstock. Experiments were conducted with a hot-face temperature of 1450°C in a CO/CO{sub 2} atmosphere. Minimal penetration was observed in the 90%Cr{sub 2}O{sub 3}–10%Al{sub 2}O{sub 3} material because interactions between the refractory and the slag produced a protective layer of FeCr{sub 2}O{sub 4}, which impeded slag flow into the bulk of the refractory. After 5 h, the 99%Al{sub 2}O{sub 3} sample exhibited an average penetration of 12.7 mm whereas the 85%Al{sub 2}O{sub 3}–15%SiO{sub 2} sample showed 3.8 mm. Slag infiltrated into the 99%Al{sub 2}O{sub 3} and 85%Al{sub 2}O{sub 3}–15%SiO{sub 2} refractory systems by dissolving the respective refractories' matrix materials, which consist of fine Al{sub 2}O{sub 3} particles and an amorphous alumino-silicate phase. Due to enrichment in SiO{sub 2}, a network-former, infiltration into the 85%Al{sub 2}O{sub 3}–15%SiO{sub 2} system yielded a higher viscosity slag and hence, a shallower penetration depth. The results suggest that slag infiltration can be limited by interactions with the refractory through the formation of either a solid layer that physically impedes fluid flow or a more viscous slag that retards infiltration.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases

Every year individuals experience symptoms that remain undiagnosed by healthcare providers. In the United States, these rare diseases are defined as a condition that affects fewer than 200,000 individuals. However, there are an estimated 7000 rare diseases, and there are an estimated 25–30 million Americans in total (7.6–9.2% of the population as of 2018) affected by such disorders. The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Mass spectrometry-based metabolomics and lipidomics analyses could advance the collective understanding of individual symptoms and advance diagnoses for individuals with heretofore undiagnosed disease. Here, we report the mass spectrometry-based metabolomics and lipidomics analyses of blood plasma, urine, and cerebrospinal fluid from 148 patients within the UDN and their families, as well as from a reference population of over 100 individuals with no known metabolic diseases. The raw and processed data are available to the research community so that they might be useful in the diagnoses of current or future patients suffering from undiagnosed disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

3-D Unsteady Model for Be-Steam Reaction in Water-Cooled Ceramic Breeder Blanket

Here, the design of the water-cooled ceramic breeder (WCCB) blanket includes beryllium multiplier layers. At elevated temperature, beryllium reacts with steam in an exothermic reaction producing beryllium oxide and hydrogen. Such situation may occur in WCCB in the case of the rupture of one of the cooling pipes in the blanket module. This process occurs locally in a complex 3-D geometry of the blanket containing several different granular levels and a network of cooling pipes and structural supports. The process is also inherently unsteady since reaction rate depends on concentration of steam and pure beryllium which changes in time. In order to perform the detailed analysis of the process, the model of the reacting flowthrough porous media was developed and introduced into 3-D computational fluid dynamics code. In this model, granular beds are introduced as porous solids simplifying the model geometry and reducing typical mesh size to manageable amount of tens of millions of elements. A reaction rate between solid beryllium and steam is obtained from experimental results, and depends on temperature and concentration of the reactants. The differential equation for beryllium oxide fraction is introduced, allowing obtaining distributions of beryllium oxide in space and time. Multicomponent flow consisting of a homogenous mixture of steam and hydrogen is considered flowing through the porous solid with variable properties. Sink and source terms for steam and hydrogen fractions are determined by local beryllium oxide mass fraction source according to the molar ratios of beryllium steam reaction. The conjugated heat transfer approach is applied to calculate heat transfer in support structures as well as coolant flow, simultaneously with the porous medium steam flow in a blanket's granular beds. The model is validated using experimental data on beryllium steam reaction for granular bed samples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Dynamic assembly of polymer nanotube networks via kinesin powered microtubule filaments

In this study, we describe for the first time how biological nanomotors may be used to actively self-assemble mesoscale networks composed of diblock copolymer nanotubes. The collective force generated by multiple kinesin nanomotors acting on a microtubule filament is large enough to overcome the energy barrier required to extract nanotubes from polymer vesicles comprised of poly(ethylene oxide-b-butadiene) in spite of the higher force requirements relative to extracting nanotubes from lipid vesicles. Nevertheless, large-scale polymer networks were dynamically assembled by the motors. These networks displayed enhanced robustness, persisting more than 24 h post-assembly (compared to 4–5 h for corresponding lipid networks). The transport of materials in and on the polymer membranes differs substantially from the transport on analogous lipid networks. Specifically, our data suggest that polymer mobility in nanotubular structures is considerably different from planar or 3D structures, and is stunted by 1D confinement of the polymer subunits. Moreover, quantum dots adsorbed onto polymer nanotubes are completely immobile, which is related to this 1D confinement effect and is in stark contrast to the highly fluid transport observed on lipid tubules.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Absence of Off-Diagonal Long-Range Order in hcp 4 He Dislocation Cores

Here, the mass transport properties along dislocation cores in hcp 4 He are revisited by considering two types of edge dislocations as well as a screw dislocation, using a fully correlated quantum simulation approach. Specifically, we employ the zero-temperature path-integral ground state (PIGS) method together with ergodic sampling of the permutation space to investigate the fundamental dislocation core structures and their off-diagonal long-range order properties. It is found that the Bose-Einstein condensate fraction of such defective 4 He systems is practically null (≤ 10 –6 ), just as in the bulk defect-free crystal. These results provide compelling evidence for the absence of intrinsic superfluidity in dislocation cores in hcp 4 He and challenge the superfluid dislocation-network interpretation of the mass-flux-experiment observations, calling for further experimental investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Adsorption and Capillary Condensation-Induced Imbibition in Nanoporous Media

Multiphase flow phenomena in nanoporous media are encountered in many science and engineering applications. Shales, for example, possessing complex nanopore networks, have considerable importance as source rocks for unconventional oil and gas production and as low-permeability seals for geologic carbon sequestration or nuclear waste disposal. This work presents a theoretical investigation of the processes controlling adsorption, capillary condensation, and imbibition in such nanoporous media, with a particular focus on understanding the effects of fluid-fluid and fluid-pore wall interaction forces in the interconnected nanopore space. Building on a new theoretical framework, we developed a numerical model for the multiphase nanoporous flow and tested it against water vapor uptake measurements conducted on a shale core sample. The model, which is based on the density functional approach, explicitly includes the relevant interaction forces among fluids and solids while allowing for a continuum representation of the porous medium. The experimental data include gravimetrically measured mass changes in an initially dry core sample exposed to varying levels of relative humidity, starting with a low relative humidity (rh = 0.31) followed by a period of a higher relative humidity (rh = 0.81). During this process, water vapor uptake in the dry core is recorded as a function of time. Our model suggests that, under low rh conditions, the flow within the shale sample is controlled by adsorption- and diffusion-type processes. After increasing the rh to 0.81, the uptake of water vapor becomes more significant, and according to our model, this can be explained by capillary condensation followed by immiscible displacement in the core sample. It appears that strong fluid-pore wall attractive forces cause condensation near the inlet, which then induces water imbibition further into sample.

58 GEOSCIENCES↗
DOE OSTI2013

High Fidelity Computational Analysis of CO2 Trapping at Pore Scales (Final Technical Report)

With an alarming rise in carbon dioxide (CO2) emission from anthropogenic sources, CO2 sequestration has become an attractive choice to mitigate the emission. Some popular storage media for CO 2 are oil reservoirs, deep coal-bed, and deep oceanic-beds. These have been used for the long term CO 2 storage. Due to special lowering viscosity and surface tension property of CO 2 , it has been widely used for enhanced oil recovery. The sites for CO 2 sequestration or enhanced oil recovery mostly consist of porous rocks. Lack of knowledge of molecular mobility under confinement and molecule-surface interactions between CO2 and natural porous media results in generally governed by unpredictable absorption kinetics and total absorption capacity for injected fluids, and therefore, constitutes barriers to the deployment of this technology. Therefore, it is important to understand the flow dynamics of CO 2 through the porous microstructures at the finest scale (pore-scale) to accurately predict the storage potential and long-term dynamics of the sequestered CO 2 . This report discusses about pore-network flow modeling approach using variational method and analyzes simulated results this method simulations at pore-scales for idealized network and using Berea Sandstone CT scanned images. Variational method provides a promising way to study the kinetic behavior and storage potential at the pore scale in the presence of other phases. The current study validates variational solutions for single and two-phase Newtonian and single phase non-Newtonian flow through angular pores for special geometries whose analytical and/or empirical solutions are known. The hydraulic conductance for single phase flow through a triangular duct was also validated against empirical results derived from lubricant theory.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2022

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗
DOE OSTI2012

Theory of kinetic arrest, elasticity, and yielding in dense binary mixtures of rods and spheres

We extend the quiescent and stressed versions of naïve mode coupling theory to treat the dynamical arrest, shear modulus, and absolute yielding of particle mixtures where one or more species is a nonrotating nonspherical object. The theory is applied in detail to dense isotropic “chemically matched” mixtures of variable aspect ratio rods and spheres that interact via repulsive and short range attractive site-site pair potentials. A remarkably rich ideal kinetic arrest behavior is predicted with up to eight “dynamical phases” emerging: an ergodic fluid, partially localized states where the spheres remain fluid but the rods can be a gel, repulsive glass or attractive glass, doubly localized glasses and gels, a porous rod gel plus sphere glass, and a narrow window where a type of rod glass and gel localization coexist. Dynamical complexity increases with rod length and the introduction of attractive forces between all species which both enhance gel network formation. Multiple dynamic reentrant features and triple points are predicted, and each dynamic phase has unique particle localization characteristics and mechanical properties. Orders of magnitude variation of the linear shear modulus and absolute yield stress are found as rod length, mixture composition and the detailed nature of interparticle attractions are varied. The interplay of total (high) mixture packing fraction and composition at fixed temperature is also briefly studied. The present work provides a foundation to study more complex rod-sphere mixtures of both biological and synthetic interest that include physical features such as interaction site size asymmetry, rod-sphere specific attractions, and/or Coulomb repulsion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation

Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R 2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Subsurface Characterization of Hydraulic Fracture Test Site-2 (HFTS-2), Delaware Basin

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Wolfcamp Formation of the Permian (Delaware) Basin. This paper focuses on integration, advanced geological characterization, and 3D subsurface modeling of the comprehensive HFTS-2 dataset. The study showcases a multidisciplinary reservoir characterization approach that incorporates geology, petrophysics, geochemistry, geomechanics, microseismic, and subsurface engineering analysis. Subsurface characterization of organic-rich mudstone formations requires understanding complex hydraulic fracture network growth in relation to inherent lithology, geomechanical properties, and interaction with pre-existing natural fractures. This paper presents a characterization workflow incorporating pre- and post-stimulation subsurface data, unique to the HFTS-2 dataset. The study integrated: (1) rock properties from logs, cores, and thin sections; (2) natural and hydraulic fracture descriptions from cores and image logs; (3) local and regional stresses; (4) geomechanics; (5) microseismic; (6) fiber optic (FO) and bottomhole pressure gauge (BHPG) response; and (7) produced fluids analysis. During a stimulation treatment, creation of the stimulated rock volume (SRV) is influenced by several subsurface factors. Key contributing factors include structural context, stress conditions, lithology, facies architecture, pre-existing natural fractures, and geomechanical properties. The HFTS-2 subsurface data integration indicates that the SRV is comprised of a complex juxtaposition of hydraulic fracture swarms, as evidenced by image logs analysis, core description, and microseismic monitoring. The HFTS-2 microseismic event density was used to generate 3D heat maps that serve as a representative SRV footprint, corroborated by secondary datasets. These maps were further integrated with petrophysical and geomechanical characteristics, as well as responses from FO and BHPG, to estimate the lateral and vertical dimensions of the effective fractures. The geological characterization for the HFTS-2 dataset combined with 3D modeling for petrophysical and geomechanical properties provides a strong foundation for subsurface simulation and optimization studies. Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S005R001/2477501/urtec-2021-5243-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5243 The workflow improved our understanding of HFTS-2 hydraulic fracture propagation and characteristics in relation to offset pressure depletion and interaction with pre-existing natural fractures. Analysis showed that fracture geometry varies by stage and by well, and a complex fracture network is generated with varying fracture density. The multidisciplinary workflow presented herein for integration and characterization serves as a foundation to evaluate completion efficiency and estimate areal and vertical stimulation and depletion extent for the project. Furthermore, the workflow and learnings can also be transferred to other unconventional plays.

58 GEOSCIENCES↗
DOE OSTI2013

Saltstone Oxidation Study: Leaching Method

Cementitious waste forms can be designed to chemically stabilize selected contaminants, such as Tc{sup +7} and Cr{sup +6}, by chemically reduction to lower valance states, Tc{sup +4} and Cr{sup +3}, respectively, and precipitation of these species in alkaline media as low solubility solid phases. Data for oxidation of this type of cementitious waste form cured under field conditions as a function of time is required for predicting the performance of the waste form and disposal facility. The rate of oxidation (oxidation front advancement) is an important parameter for predicting performance because the solubilities of some radionuclide contaminants, e.g., technetium, are a function of the oxidation state. A non-radioactive experiment was designed for quantifying the oxidation front advancement using chromium, as an approximate redox-sensitive surrogate (Cr{sup +6} / Cr{sup +3}) for technetium (Tc{sup +7} / Tc{sup +4}). Nonradioactive cementitious waste forms were prepared in the laboratory and cured under both laboratory and ?field conditions.? Laboratory conditions were ambient temperature and sealed sample containers. Field conditions were approximated by curing samples in open containers which were placed inside a plastic container stored outdoors at SRS. The container had a lid and was instrumented with temperature and humidity probes. Subsamples as thin as 0.2 mm were taken as a function of distance from the exposed surface of the as-cast sample. The subsamples were leached and the leachates were analyzed for chromium, nitrate, nitrite and sodium. Nitrate, nitrite, and sodium concentrations were used to provide baseline data because these species are not chemically retained in the waste form matrix to any significant extent and are not redox sensitive. ?Effective? oxidation fronts for Cr were measured for samples containing 1000, 500 and 20 mg/kg Cr added as soluble sodium chromate, Na{sub 2}CrO{sub 4}. For a sample cured for 129 days under field conditions, leachable Cr (assumed to be the oxidized form, i.e., Cr{sup +6} was detected in the top 15 to 20 mm of the sample spiked with 1000 mg/kg Cr. Below about 20 mm, the Cr concentrations in leachates were below the detection limit (< 0.010 mg/L) which indicates that the oxidation as the result of exposure to air was limited to the top 20 mm of the sample after exposure for 129 days and that the bulk of the waste form was not affected, i.e., the Cr was stabilized and insoluble. For samples cured in the laboratory, leachable Cr was detected in the top 8 mm of the Cr 1000 sample cured in the laboratory for 37 days. Between 8 and 14 mm, the concentration Cr in the leachate dropped by a factor of about 20 to just above the detection limit. These depth of penetration results indicate that the rate of advancement of the oxidation front for samples spiked with 1000 mg/kg Cr cured under ?field conditions? for 129 days is less than that for the sample cured in the laboratory for 37 days, i.e., 0.156 and 0.216 mm/day, respectively. Additional data are presented for samples spiked with 500 and 20 mg/kg Cr. In summary, cementitious waste forms are porous solids with a network of interconnected pores ranging in diameter from 10E-10 m to greater than a few mm. The oxidation process is assumed to occur as the result of oxygen transport through the interconnected porosity which may be filled with air, aqueous salt solution, or both. Upon oxidation, the Cr becomes soluble and can be transported in solution through aqueous pore fluid or leachate.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

Feasibility of Vascular Parameter Estimation for Assessing Hypertensive Pregnancy Disorders

Abstract Hypertensive pregnancy disorders (HPDs), such as pre-eclampsia, are leading sources of both maternal and fetal morbidity in pregnancy. Noninvasive imaging, such as ultrasound (US) and magnetic resonance imaging (MRI), is an important tool for predicting and monitoring these high risk pregnancies. While imaging can measure hemodynamic parameters, such as uterine artery pulsatility and resistivity indices (PI and RI), the interpretation of such metrics for disease assessment relies on ad hoc standards, which provide limited insight to the physical mechanisms underlying the emergence of hypertensive pregnancy disorders. To provide meaningful interpretation of measured hemodynamic data in patients, advances in computational fluid dynamics can be brought to bear. In this work, we develop a patient-specific computational framework that combines Bayesian inference with a reduced-order fluid dynamics model to infer parameters, such as vascular resistance, compliance, and vessel cross-sectional area, known to be related to the development of hypertension. The proposed framework enables the prediction of hemodynamic quantities of interest, such as pressure and velocity, directly from sparse and noisy MRI measurements. We illustrate the effectiveness of this approach in two systemic arterial network geometries: an aorta with branching carotid artery and a maternal pelvic arterial network. For both cases, the model can reconstruct the provided measurements and infer parameters of interest. In the case of the maternal pelvic arteries, the model can make a distinction between the pregnancies destined to develop hypertension and those that remain normotensive, expressed through the value range of the predicted absolute pressure.

Biophysics↗