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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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Integrated numerical methods for hypersonic aircraft cooling systems analysis
Numerical methods have been developed for the analysis of hypersonic aircraft cooling systems. A general purpose finite difference thermal analysis code is used to determine areas which must be cooled. Complex cooling networks of series and parallel flow can be analyzed using a finite difference computer program. Both internal fluid flow and heat transfer are analyzed, because increased heat flow causes a decrease in the flow of the coolant. The steady state solution is a successive point iterative method. The transient analysis uses implicit forward-backward differencing. Several examples of the use of the program in studies of hypersonic aircraft and rockets are provided.
Modeling and Analysis of Chill and Fill Processes for the EDU Tank
NASA's future missions may require long-term storage and transfer of cryogenic propellants. The Engineering Development Unit (EDU), a NASA in-house effort supported by both Marshall Space Flight Center (MSFC) and Glenn Research Center (GRC), is a Cryogenic Fluid Management (CFM) test article that primarily serves as a manufacturing pathfinder and a risk reduction task for a future CFM payload. The EDU test article, comprises a flight like tank, internal components, insulation, and attachment struts. The EDU is designed to perform integrated passive thermal control performance testing with liquid hydrogen in a space-like vacuum environment. A series of tests, with liquid hydrogen as a testing fluid, was conducted at Test Stand 300 at MSFC during summer of 2014. The objective of this effort was to develop a thermal/fluid model for evaluating the thermodynamic behavior of the EDU tank during the chill and fill processes. Generalized Fluid System Simulation Program (GFSSP), an MSFC in-house general-purpose computer program for flow network analysis, was utilized to model and simulate the chill and fill portion of the testing. The model contained the liquid hydrogen supply source, feed system, EDU tank, and vent system. The modeling description and comparison of model predictions with the test data will be presented in the final paper.
NASAs VESGEN: Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways Using GeneLab.
One fundamental requirement shared by humans with all higher terrestrial life forms, including insect wings, higher land plants and other vertebrates, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. One unifying perspective is that vascular patterning offers a useful readout that necessarily integrates complex molecular signaling pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, stress response, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascular-related changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the Euclidean and dynamic dimensions (x,y,t) of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions (i,j,k,...). Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.
Using NASA's GeneLab for VESGEN Systems Analysis of Vascular Phenotypes from Stress and Other Signaling Pathways
One fundamental requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants, is a complex, fractally branching vascular system. NASA's VESsel GENeration Analysis (VESGEN) software maps and quantifies vascular trees, networks, and tree-network composites according to weighted physiological rules such as vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport requires a complex distributed system of highly regulated laminar flow. Microvascular branching rules within vertebrates, dicot leaves and the other organisms therefore display many similarities. A unifying perspective is that vascular patterning offers a useful readout of molecular signaling that necessarily integrates these complex pathways. VESGEN has elucidated changes in vascular pattern resulting from inflammatory, developmental and other signaling within numerous tissues and major model organisms studied for Space Biology. For a new VESGEN systems approach, we analyzed differential gene expression in leaves of Arabidopsis thaliana reported by GeneLab (GLDS-7) for spaceflight. Vascularrelated changes in leaf gene expression were identified that can potentially be phenocopied by mutants in ground-based experiments. To link transcriptional, protein and other molecular change with phenotype, alterations in the spatial and dynamic dimensions of vascular patterns for Arabidopsis leaves and other model species are being co-localized with signaling patterns of single molecular expression analyzed as information dimensions. Previously, Drosophila microarray data returned from space suggested significant changes in genes related to wing venation development that include EGF, Notch, Hedghog, Wingless and Dpp signaling. Phenotypes of increasingly abnormal ectopic wing venation in the (non-spaceflight) Drosophila wing generated by overexpression of a Notch antagonist were analyzed by VESGEN. Other VESGEN research applications include the mouse retina, GI and coronary vessels, avian placental analogs and translational studies in the astronaut retina related to health challenges for long-duration missions.
A Liquefaction Model for A Cryogenically Refrigerated Ellipsoidal Tank
Over the past few decades NASA has been developing technology to produce cryogenic propellants on the surfaces of the Moon or Mars. In-situ propellant production reduces the amount of propellants needed to be taken to Moon/Mars and ultimately reduces mission cost. Utilizing Lunar/Martian resources, the produced gases are liquefied and stored prior to use on the ascent vehicle. This paper presents a model for the liquefaction process of gaseous propellants in a cryogenically refrigerated tank. The tank is cylindrical with elliptical top and bottom domes. A transient model is developed based on the mass and energy conservation principles and wall-gas and liquid-gas interfacial mass and heat transfer correlations. The model is incorporated into the Generalized Fluid System Simulation Program (GFSSP), an MSFC in-house general-purpose computer program for flow network analysis. Description of the model and comparison of predicted results with available test data is presented.
Channels and valley networks
Attention is given to Martian channels and valley networks, since they have become a principal element of evidence to the effect that the Martian atmosphere evolved from an early volatile-rich state to its present condition. The outflow channels are relatively young, later Hesperian or Amazonian in age. They formed by immense outbursts of fluid from subsurface sources. Complexity in outflow-channel morphology was generated by varying amounts of sediment and ice in the aqueous-fluid flow systems. The overall cataclysmic-flood morphology may thus be locally transitional to morphologies generated by ice and debris flowage. Although local areas of valley networks, such as on Alba Patera, formed coevally with outflow channel activity, regionally extensive networks dominate in the heavily cratered terrains. The morphology of many valleys suggests genesis by ground-water sapping; for some valleys, surface runoff may have been more important.
Seismic velocity changes associated with aseismic deformations of a fault stimulated by fluid injection
Fluid pressure plays an important role in the stability of tectonic faults. However, the in situ mechanical response of faults to fluid pressure variations is still poorly known. To address this question, we performed here a fluid injection experiment in a fault zone in shales while monitoring fault movements at the injection source and seismic velocity variations from a near-distance (<10 m) monitoring network. We measured and located the P and S wave velocity perturbations in and around the fault using repetitive active sources. We observed that seismic velocity perturbations dramatically increase above 1.5 MPa of injection pressure. This is consistent with an increase of fluid flow associated with an aseismic dilatant shearing of the fault as shown by numerical modeling. We find that seismic velocity changes are sensitive to both fault opening by fluid invasion and effective stress variations and can be an efficient measurement for monitoring fluid-driven aseismic deformations of faults.
Pore connectivity, electrical conductivity, and partial water saturation: Network simulations
Abstract The electrical conductivity of brine‐saturated rock is predominantly dependent on the geometry and topology of the pore space. When a resistive second phase (e.g., air in the vadose zone and oil/gas in hydrocarbon reservoirs) displaces the brine, the geometry and topology of the pore space occupied by the electrically conductive phase are changed. We investigated the effect of these changes on the electrical conductivity of rock partially saturated with brine. We simulated drainage and imbibition as invasion and bond percolation processes, respectively, in pipe networks assumed to be perfectly water‐wet. The simulations included the formation of a water film in the pipes invaded by the nonwetting fluid. During simulated drainage/imbibition, we measured the changes in resistivity index as well as a number of relevant microstructural parameters describing the portion of the pore space saturated with water. Except Euler topological number, all quantities considered here showed a significant level of “universality,” i.e., insensitivity to the type of lattice used (simple cubic, body‐centered cubic, or face‐centered cubic). Hence, the coordination number of the pore network appears to be a more effective measure of connectivity than Euler number. In general, the simulated resistivity index did not obey Archie's simple power law. In log‐log scale, the resistivity index curves displayed a substantial downward or upward curvature depending on the presence or absence of a water film. Our network simulations compared relatively well with experimental data sets, which were obtained using experimental conditions and procedures consistent with the simulations. Finally, we verified that the connectivity/heterogeneity model proposed by Bernabé et al. (2011) could be extended to the partial brine saturation case when water films were not present.
Three‐Phase CO 2 Flow in a Basalt Fracture Network
Abstract Geologic CO 2 sequestration in basalt reservoirs is predicated on permanent CO 2 isolation via rapid mineralization reactions. This process is supported by a substantial body of evidence, including laboratory experiments documenting rapid mineralization rates, regional storage estimates indicating large, accessible storage reservoirs, and two successful pilot‐scale studies. Nevertheless, there remains significant uncertainty in the behavior of CO 2 flow within basalt fracture networks, particularly in the context estimating physical trapping potential in early time and as CO 2 undergoes phase change. In this study, a Monte Carlo numerical model is designed to simulate a supercritical CO 2 plume infiltrating a low‐permeability flood basalt entablature. The fracture network model is based on outcrop‐scale LiDAR mapping of Columbia River Basalt, and CO 2 flow is simulated within fifty equally probable realizations of the fracture network. The spatial distribution of fracture permeability for each realization is randomly drawn from a basalt aperture distribution, and ensemble results are analyzed with e‐type estimates to compute mean and standard deviation of fluid pressure and CO 2 saturation. Results of this model after 10 years of simulation suggest that (1) CO 2 flow converges on a single dominant flow path, (2) CO 2 accumulates at fracture intersections, and (3) variability in permeability can account for a 1.6 m depth interval within which free CO 2 may change phase from supercritical fluid to subcritical liquid or gas. In the context of CO 2 sequestration in basalt, these results suggest that physical CO 2 trapping may be substantially enhanced as carbonate minerals precipitate within the basalt fracture network.
Transient flow modeling in fractured media using graphs
In this work, we describe a method to simulate transient fluid flows in fractured media using an approach based on graph theory. Our approach builds on past work where the graph-based approach was successfully used to simulate steady-state fluid flows in fractured media. We find a mean computational speedup of the order of 1400 from an ensemble of a 100 discrete fracture networks in contrast to the O(10 4 ) speedup that was obtained for steady-state flows earlier. However, the transient flows considered here involve an additional degree of complexity that was not present in the steady-state flows considered previously with a graph-based approach, that of time marching and solution of the flow equations within a time-stepping scheme. We verify our method with an analytical test case and demonstrate its use on a practical problem related to fluid flows in hydraulically fractured reservoirs. By enabling the study of transient flows, we create an opportunity for a wide set of possibilities where a steady-state approximation is not sufficient, such as the example motivated by hydraulic fracturing that we present here. This work validates the concept that graphs are able to reliably capture the topological properties of the fracture network and serve as effective surrogates in an uncertainty-quantification framework.
Perivascular network segmentations derived from high-field MRI and their implications for perivascular and parenchymal mass transport in the rat brain
A custom segmentation workflow was applied to ex vivo high-field MR images of rat brains acquired following in vivo intraventricular contrast agent infusion to generate maps of the perivascular spaces (PVS). The resulting perivascular network segmentations enabled analysis of perivascular connections to the ventricles, parenchymal solute clearance, and dispersive solute transport within PVS. Numerous perivascular connections between the brain surface and the ventricles suggest the ventricles integrate into a PVS-mediated clearance system and raise the possibility of cerebrospinal fluid (CSF) return from the subarachnoid space to the ventricles via PVS. Assuming rapid solute exchange between the PVS and CSF spaces primarily by advection, the extensive perivascular network decreased the mean clearance distance from parenchyma to the nearest CSF compartment resulting in an over 21-fold reduction in the estimated diffusive clearance time scale, irrespective of solute diffusivity. This corresponds to an estimated diffusive clearance time scale under 10 min for amyloid-beta which suggests that the widespread distribution of PVS may render diffusion an effective parenchymal clearance mechanism. Additional analysis of oscillatory solute dispersion within PVS indicates that advection rather than dispersion is likely the primary transport mechanism for dissolved compounds greater than 66 kDa in the long (> 2 mm) perivascular segments identified here, although dispersion may be significant for smaller compounds in shorter perivascular segments.
Constructing Neural Network Based Models for Simulating Dynamical Systems
Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.
Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation
Our understanding of Alzheimer’s disease (AD) pathophysiology remains incomplete. Here, for this work, we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.
Numerical Prediction of Transient Axial Thrust and Internal Flows in a Rocket Engine Turbopump
This paper presents the application of the Generalized Fluid System Simulation Program (GFSSP) to model the time-dependent flow in a complex secondary flow circuit of the turbopump of the Fastrac engine currently under development at Marshall Space Flight Center. GFSSP is a general purpose computer program for analyzing steady-state and time-dependant flowrates, pressures, temperatures, and concentrations in a complex flow network. The program employs a finite volume formulation of mass, momentum and energy conservation equations in conjunction with the thermodynamic equation of state of real fluids. GFSSP was used to calculate the axial thrust and internal flow distribution of the Fastrac engine turbopump during the start and shut down transients. The models discussed in this paper use boundary conditions that were extracted from turbopump test data. The GFSSP predicted turbopump secondary flow passage pressures and temperatures were compared with actual measured values.
Flow Simulation in Secondary Flow Passages of a Rocket Engine Turbopump
This paper presents application of a Generalized Fluid System Simulation Program (GFSSP) to model fluid flow in a very complex secondary flow circuit of a rocket engine turbopump, SIMPLEX. SIMPLEX was a low cost turbopump designed, manufactured and tested to demonstrate the ability to reduce the overall cost and design cycle time of turbomachinery. GFSSP is a general purpose computer program for analyzing flowrate, pressure, temperature and concentration distribution in a complex flow network. The program employs a finite volume formulation of mass, momentum and energy conservation equations in conjunction with thermodynamic equation of state of a real fluid. The secondary flow passages modeled include flow between the impeller shroud and housing, impeller back face and housing, flow through labyrinth seal and bearing, return line flow to inducer and flow adjacent to front and back face of the turbine. Axial load on the bearing are calculated from the predicted pressure on all axial faces of the rotor. The predicted pressure and temperature distributions compared well with the test data.
Multi-fidelity modeling to predict the rheological properties of fiber suspensions
Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.
Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network
The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.