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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Fluid design studies of integrated modular engine system

A study was performed to develop a fluid system design and show the feasibility of constructing an integrated modular engine (IME) configuration, using an expander cycle engine. The primary design goal of the IME configuration was to improve the propulsion system reliability. The IME fluid system was designed as a single fault tolerant system, while minimizing the required fluid components. This study addresses the design of the high pressure manifolds, turbopumps and thrust chambers for the IME configuration. A physical layout drawing was made, which located each of the fluid system components, manifolds and thrust chambers. Finally, a comparison was made between the fluid system designs of an IME system and a non-network (clustered) engine system.

Frankenfield, Bruce↗
NASA NTRS1994

High-performance parallel analysis of coupled problems for aircraft propulsion

This research program deals with the application of high-performance computing methods for the analysis of complete jet engines. We have entitled this program by applying the two dimensional parallel aeroelastic codes to the interior gas flow problem of a bypass jet engine. The fluid mesh generation, domain decomposition, and solution capabilities were successfully tested. We then focused attention on methodology for the partitioned analysis of the interaction of the gas flow with a flexible structure and with the fluid mesh motion that results from these structural displacements. This is treated by a new arbitrary Lagrangian-Eulerian (ALE) technique that models the fluid mesh motion as that of a fictitious mass-spring network. New partitioned analysis procedures to treat this coupled three-component problem are developed. These procedures involved delayed corrections and subcycling. Preliminary results on the stability, accuracy, and MPP computational efficiency are reported.

Felippa, C. A.↗
DOE OSTI2017

Modeling of Hydraulic Fracture Propagation at the kISMET Site Using a Fully Coupled 3D Network-Flow and Quasi- Static Discrete Element Model

Aimed at supporting the design of hydraulic fracturing experiments at the kISMET site, ~1500 m below ground in a deep mine, we performed pre-experimental hydraulic fracturing simulations in order to estimate the breakdown pressure, propagation pressure, fracture geometry, and the magnitude of induced seismicity using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beams between them. A conjugate 3D flow network based on the DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams. In this paper, the 3D hydraulic fracturing model was verified against the analytic solution for a penny-shaped crack model. We applied the model to simulate fracture propagation from a vertical open borehole based on initial estimates of rock mechanical properties and in-situ stress conditions. The breakdown pressure and propagation pressure are directly obtained from the simulation. In addition, the released elastic strain energies of individual fracturing events were calculated and used as a conservative estimate for the magnitudes of the potential induced seismic activities associated with fracturing. The comparisons between model predictions and experimental results are still ongoing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Randomized methods to characterize large-scale vortical flow networks

We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the connectivity structures among a set of vortical elements and analyze their collective dynamics. These approaches have recently been generalized to analyze high-dimensional turbulent flows, for which network computations can become prohibitively expensive. In this work, we propose efficient methods to approximate network quantities, such as the leading eigendecomposition of the adjacency matrix, using randomized methods. Specifically, we use the Nyström method to approximate the leading eigenvalues and eigenvectors, achieving significant computational savings and reduced memory requirements. The effectiveness of the proposed technique is demonstrated on two high-dimensional flow fields: two-dimensional flow past an airfoil and two-dimensional turbulence. We find that quasi-uniform column sampling outperforms uniform column sampling, while both feature the same computational complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
NASA NTRS2019

Numerical Modeling of Boiling in a Heated Tube

This paper presents numerical models of boiling in a heated tube using the Generalized Fluid System Simulation Program (GFSSP), a finite-volume-based general-purpose flow network code developed at NASA/Marshall Space Flight Center. The heated tube is discretized into a one-dimensional array of nodes and branches to represent the flow of liquid and vapor in a tube with a prescribed pressure differential. The solid wall is also discretized into solid nodes and conductors to allow for heat transfer between the wall and the fluid. The conservation equations of mass, momentum, and energy of the fluid are solved simultaneously with the energy conservation equation for the solid wall. Two experimental configurations of fluid flowing in a vertical tube have been simulated, one with water and the other with liquid hydrogen. This paper compares experimental data with numerical predictions based on four different published correlations for boiling heat transfer coefficients. Three of these correlations are applicable to the saturated vertical flow conditions of the experiments. One of them is applicable to film boiling and has been used for the liquid hydrogen experiment, which was in film boiling regime. For the case of boiling water, the predictions of wall temperatures using the boiling heat transfer correlations agreed well with the experimental results. However, in the case of boiling hydrogen larger discrepancies were observed between the experimental data and numerical predictions.

Majumdar, Alok↗
DOE OSTI2020

Machine learning prediction of self-diffusion in Lennard-Jones fluids

In this work, different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Play-fairway analysis for geothermal exploration: Examples from the Great Basin, western USA

Elevated permeability within fault systems provides pathways for circulation of geothermal fluids. Future geothermal development depends on precise and accurate location of such fluid flow pathways in order to both accurately assess geothermal resource potential and increase drilling success rates. The collocation of geologic characteristics that promote permeability in a given geothermal system define the geothermal ‘fairway’, the location(s) where upflow zones are probable and where exploration efforts including drilling should be focused. We define the geothermal fairway as the collocation of 1) fault zones that are ideally oriented for slip or dilation under ambient stress conditions, 2) areas with a high spatial density of fault intersections, and 3) lithologies capable of supporting dense interconnected fracture networks. Areas in which these characteristics are concomitant with both elevated temperature and fluids are probable upflow zones where economic-scale, sustainable temperatures and flow rates are most likely to occur. Employing a variety of surface and subsurface data sets, we test this ‘play-fairway’ exploration methodology on two Great Basin geothermal systems, the actively producing Brady’s geothermal system and a ‘greenfield’ geothermal prospect at Astor Pass, NV. These analyses, based on 3D structural and stratigraphic framework models, reveal subsurface characteristics about each system, well beyond the scope of standard exploration methods. At Brady’s, the geothermal fairways we define correlate well with successful production wells and pinpoint several drilling targets for maintaining or expanding production in the field. In addition, hot-dry wells within the Brady’s geothermal field lie outside our defined geothermal fairways. At Astor Pass, our play-fairway analysis provides for a data-based conceptual model of fluid flow within the geothermal system and indicates several targets for exploration drilling.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Hydraulic characterization of a fault zone from fracture distribution

A quantitative assessment of how faults control the migration of geofluids is critical in many areas of geosciences. Here we integrated geological fieldwork, quantitative analysis of the fractures distribution and numerical modeling to build a geometrical representation of a fault zone and to characterize its hydraulic properties. Our target is a fault located in the Majella Mountain (Italy). We collected 21 scan lines across the fault profile in order to characterize its architecture. The numerical modeling of the fracture network of the damage zones and their hydraulic parameters was performed using both commercial (Move ® ) and open source software (dfnWorks and PFLOTRAN). Move ® was used to build a representative model of the fault zone using fracture spacing as a proxy, and to model the hydraulic parameters of the different fault domains. dfnWorks and PFLOTRAN were employed to infer the hydraulic parameters of the damage zones of the fault and then upscale these properties to an equivalent continuum domain, suitable for fluid flow simulations through the whole fault zone. Our findings show how even in a relatively small area it is possible to describe changes in terms of hydraulic properties of a fault zone and to build models capable to represent these variations.

58 GEOSCIENCES↗
DOE OSTI2023

CoolPINNs: A physics-informed neural network modeling of active cooling in vascular systems

Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during the design and operational phases of these engineered systems. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. Here, this paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation—called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs since the approach is meshless. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the most significant utility of the current framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Fault structure, stress, or pressure control of the seismicity in shale? Insights from a controlled experiment of fluid-induced fault reactivation

It is clear that clay formations are present in reservoirs and earthquake faults, but questions remain on their mechanical behavior, as they can vary from ductile (aseismic) to brittle (seismic). An experiment, at a scale of 10 m, aims to reactivate a natural fault by fluid pressure in shale materials. The injection area was surrounded by a dense monitoring network comprising pressure, deformation, and seismicity sensors, in a well-characterized geological setting. Thirty-two microseismic events were recorded during several injection phases in five different locations within the fault zone. Their computed magnitude ranged between -4.3 and -3.7. Their spatiotemporal distribution, compared with the measured displacement at the injection points, shows that most of the deformation induced by the injection is aseismic. Whether the seismicity is controlled by the fault architecture, mineralogy of fracture filling, fluid, and/or stress state is then discussed. The fault damage zone architecture and mineralogy are of crucial importance, as seismic slip mainly localizes on the sealed-with-calcite fractures which predominate in the fault damage zone. As no seismicity is observed in the close vicinity of the injection areas, the presence of fluid seems to prevent seismic slips. The fault core acts as an impermeable hydraulic barrier that favors fluid confinement and pressurization. Therefore, the seismic behavior seems to be strongly sensitive to the structural heterogeneity (including permeability) of the fault zone, which leads to a heterogeneous stress response to the pressurized volume.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Axisymmetric Two-Dimensional Modeling of No Vent Filling of a Cryogenic Tank using Generalized Fluid System Simulation Program

This paper presents preliminary analysis using a finite volume based computational method for network flow analysis for the simulation of chill and fill of a cryogenic tank. An axisymmetric two-dimensional model of a spherical tank was developed using a nodal approach. No Vent Filling (NVF) was accomplished using a Thermodynamic Vent System assisted injector where the injector was cooled by the cold gas generated by Joule-Thompson expansion of the liquid propellant. The numerical model accounts for a) different regimes of pool boiling heat transfer, b) condensation of vapor around spray droplets, and c) condensation of vapor at the interface of the cooled injector and ullage. The numerical predictions of tank pressure, filling rate, and wall temperatures were compared with the test data and one-dimensional (1D) model predictions. It was demonstrated that the axisymmetric 2D model provided a more realistic prediction than the 1D model.

Cryogenics↗
DOE OSTI2012

FROM PRESTELLAR TO PROTOSTELLAR CORES. II. TIME DEPENDENCE AND DEUTERIUM FRACTIONATION

We investigate the molecular evolution and D/H abundance ratios that develop as star formation proceeds from a dense molecular cloud core to a protostellar core, by solving a gas-grain reaction network applied to a one-dimensional radiative hydrodynamic model with infalling fluid parcels. Spatial distributions of gas and ice-mantle species are calculated at the first-core stage, and at times after the birth of a protostar. Gas-phase methanol and methane are more abundant than CO at radii r {approx}< 100 AU in the first-core stage, but gradually decrease with time, while abundances of larger organic species increase. The warm-up phase, when complex organic molecules are efficiently formed, is longer-lived for those fluid parcels infalling at later stages. The formation of unsaturated carbon chains (warm carbon-chain chemistry) is also more effective in later stages; C{sup +}, which reacts with CH{sub 4} to form carbon chains, increases in abundance as the envelope density decreases. The large organic molecules and carbon chains are strongly deuterated, mainly due to high D/H ratios in the parent molecules, determined in the cold phase. We also extend our model to simulate simply the chemistry in circumstellar disks, by suspending the one-dimensional infall of a fluid parcel at constant disk radii. The species CH{sub 3}OCH{sub 3} and HCOOCH{sub 3} increase in abundance in 10{sup 4}-10{sup 5} yr at the fixed warm temperature; both also have high D/H ratios.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2024

Symbolic diagnostics to interpret and analyze neural network models

Embedded machine-learned models (EMLMs) have the promise to improve the predictive accuracy of engineering simulators in environments of national interest. EMLMs often comprise complex input-output maps (e.g., neural networks), which make them unamenable to rigorous analysis and generally difficult to interpret. In the face of decades of theory, this lack of interpretability is a significant barrier to building confidence in these models. This work outlines an approach to interpret EMLMs using sparse polynomial regression for comparison with theoretical understanding. To do so, we build on the concept of Locally Interpretable Model-agnostic Explanations (LIME) using physics-informed clustering, prototype selection, and library construction. While general, we demonstrate our method on tensor-basis neural networks used in Reynolds-Averaged Navier-Stokes simulations of hypersonic fluid flows. Results are presented for a simulated toy model and for direct numerical simulations (DNS) of turbulent flows over a flat plate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
NASA NTRS1988

NAS: The first year

Discussed are the capabilities of NASA's Numerical Aerodynamic Simulation (NAS) Program and its application as an advanced supercomputing system for computational fluid dynamics (CFD) research. First, the paper describes the NAS computational system, called the NAS Processing System Network, and the advanced computational capabilities it offers as a consequence of carrying out the NAS pathfinder objective. Second, it presents examples of pioneering CFD research accomplished during NAS's first operational year. Examples are included which illustrate CFD applications for predicting fluid phenomena, complementing and supplementing experimentation, and aiding in design. Finally, pacing elements and future directions for CFD and NAS are discussed.

Bailey, F. R.↗