SEARCH · Engineering Papers

Results for “network flow”

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.

SourceResearch connectionFrom yearThrough year

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

At least 991 records · Page 55

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Deep Neural Network Informed Markov Chain Monte Carlo Methods

In subsurface flow modeling, quantifying the uncertainty of model parameters and the corresponding uncertainly on output quantities is a crucial task for groundwater management. Markov chain Monte Carlo (MCMC) methods can take advantage of observed data to estimate parameters in a Bayesian setting. However, MCMC can be slow to converge and produce highly correlated samples when the dimensions of the parameters is high. Using gradients for the posterior distribution can help samplers explore the parameter space more efficiently, but obtaining gradients can be computationally challenging.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1981

A pulse-forming network for particle path visualization

A procedure is described for visualizing nonsteady fluid flow patterns over a wide velocity range using discrete nonluminous particles. The paramount element responsible for this capability is a pulse-forming network with variable inductance that is used to modulate the discharge of a fixed amount of electrical energy through a xenon flashtube. The selectable duration of the resultant light emission functions as a variable shutter so that particle path images of constant length can be recorded. The particles employed as flow markers are hydrogen bubbles that are generated by electrolysis in a water tunnel. Data are presented which document the characteristics of the electrical circuit and establish the relation of particle velocity to both section inductance and film exposure.

Mcalister, K. W.↗

DOE OSTI2021

CELAVI (Circular Economy Lifecycle Assessment and VIsualization) [SWR-20-87]

A circular economy emphasizes the efficient use of all resources (e.g., materials, land, water). Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. Current tools are unable to fully evaluate these potential externalities, which will be important for informing research prioritization and regional decision making. The Circular Economy Lifecycle Assessment and VIsualization (CELAVI) framework allows stakeholders to quantify and visualize potential regional and sectoral transfers of impacts that could result from transitioning to a circular economy, with particular focus on energy materials. The framework uses system dynamics to model material flows for multiple circular economy pathways and decisions are based on learning-by-doing and are implemented via cost and strategic value of different circular economy pathways. It uses network theory to track the spatial and sectoral flow of functional units across a graph and discrete event simulation to to step through time and evaluate lifecycle assessment data at each time step. The framework is designed to be flexible and scalable to accommodate multiple energy materials and multiple energy technologies. The primary goal of CELAVI is to help answer questions about how material flows and environmental and economic impacts of energy systems might change if the circularity of energy systems increases.

Eberle, Annika↗

DOE OSTI2022

CELAVI (Circular Economy Lifecycle Assessment and VIsualization) v.1.3.1 9/30/2022 [SWR-20-87]

Eberle, Annika↗

DOE OSTI2018

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

EFFECT OF HORIZONTALLY INHOMOGENEOUS HEATING ON FLOW AND MAGNETIC FIELD IN THE CHROMOSPHERE OF THE SUN

The solar chromosphere is heated by damped Alfvén waves propagating upward from the photosphere at a rate that depends on magnetic field strength, producing enhanced heating at low altitudes in the extended weak-field regions (where the additional heating accounts for the radiative losses) between the boundaries of the chromospheric network as well as enhanced heating per particle at higher altitudes in strong magnetic field regions of the network. The resulting inhomogeneous radiation and temperature distribution produces bulk flows, which in turn affect the configuration of the magnetic field. The basic flow pattern is circulation on the spatial scale of a supergranule, with upward flow in the strong-field region; this is a mirror image in the upper chromosphere of photospheric/subphotospheric convection widely associated with the formation of the strong network field. There are significant differences between the neutral and the ionized components of the weakly ionized medium: neutral flow streamlines can form closed cells, whereas plasma is largely constrained to flow along the magnetic field. Stresses associated with this differential flow may explain why the canopy/funnel structures of the network magnetic field have a greater horizontal extent and are relatively more homogeneous at high altitudes than is expected from simple current-free models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Coupled flow accumulation and atmospheric blocking govern flood duration

Abstract We present a physically based Bayesian network model for inference and prediction of flood duration that allows for a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. Distinct scaling factors at the land surface and regional atmospheric levels are unraveled using this Bayesian network model. Land surface scaling explains the variability in flood duration as a function of cumulative exceedance index, a new measure that represents the evolution of the flood in the basin. Dynamic atmospheric scaling explains the cumulative exceedance index using the interaction between atmospheric blocking system and the synergistic model of wind divergence and atmospheric water vapor. Our findings underline that the synergy between a large persistent low-pressure blocking system and a higher rate of divergent wind often triggers a long-duration flood, even in the presence of moderate moisture supply in the atmosphere. This condition in turn causes an extremely long-duration flood if the basin-wide cumulative flow prior to the flood event was already high. Thus, this new land-atmospheric interaction framework integrates regional flood duration scaling and dynamic atmospheric scaling to enable the coupling of ‘horizontal’ (for example, streamflow accumulation inside the basin) and ‘vertical’ flow of information (for example, interrelated land and ocean-atmosphere interactions), providing an improved understanding of the critical forcing of regional hydroclimatic systems. This Bayesian model approach is applied to the Missouri River Basin, which has the largest system of reservoirs in the United States. Our predictive model can aid in decision support systems for the protection of national infrastructure against long-duration flood events.

Najibi, Nasser (ORCID:0000000205157192)↗

DOE OSTI2020

Transport Simulations on Scanning Transmission Electron Microscope Images of Nanoporous Shale

Digital rock physics is an often-mentioned approach to better understand and model transport processes occurring in tight nanoporous media including the organic and inorganic matrix of shale. Workflows integrating nanometer-scale image data and pore-scale simulations are relatively undeveloped, however. In this paper, a workflow is demonstrated progressing from sample acquisition and preparation, to image acquisition by Scanning Transmission Electron Microscopy (STEM) tomography, to volumetric reconstruction to pore-space discretization to numerical simulation of pore-scale transport. Key aspects of the workflow include (i) STEM tomography in high angle annular dark field (HAADF) mode to image three-dimensional pore networks in µm-sized samples with nanometer resolution and (ii) lattice Boltzmann method (LBM) simulations to describe gas flow in slip, transitional, and Knudsen diffusion regimes. It is shown that STEM tomography with nanoscale resolution yields excellent representation of the size and connectivity of organic nanopore networks. In turn, pore-scale simulation on such networks contributes to understanding of transport and storage properties of nanoporous shale. Interestingly, flow occurs primarily along pore networks with pore dimensions on the order of tens of nanometers. Smaller pores do not form percolating pathways in the sample volume imaged. Apparent gas permeability in the range of 10−19 to 10−16 m2 is computed.

42 ENGINEERING↗

NASA NTRS1984

Geomorphic clues to the Martian volatile inventory. 1: Flow ejecta blankets

There are classes of landforms whose presence on Mars is strongly suggestive, if not confirmatory, of the participation of volatiles, presumably water, in its geomorphic development: (1) valley networks, (2) outflow channels, (3) landslides, and (4) flow-ejecta blankets. The first two may represent landforms generated by the movement of volatiles from sources, while the latter two probably represent the dissipation of energy generated by forcing inputs (e.g., kinetic energy and gravity) modulated by volatiles. In many areas on Mars, all four processes have acted on the same lithologic materials and were influenced by the composition of those units, and possibility by the climatic regime at the time of their formation. One of the approaches discussed to this specific problem of landform genesis, and to the general problem of the present and past states of martian volatiles, is to attempt to constrain the distribution, amount, and history of available volatiles by using possible evidence of volatile participation expressed in the morphology of other related landforms (e.g., flow-ejecta blankets and landslides) coupled with physical models for landform genesis.

Pieri, D.↗

NASA NTRS1993

A concurrent hybrid Navier-Stokes/Euler approach to fluid dynamic computations

We present a methodology for the numerical simulation of flow fields by the simultaneous application of two distinct approaches to computational aerodynamics. We compute the three dimensional flow field of a missile at moderate angle of attack by dividing the flow field into two regions: a region near the surface where we use a structured grid and a Navier Stokes solver, and a region farther away from the surface where we utilize an unstructured grid and an Euler solver. The two solvers execute as independent UNIX processes either on the same machine or on two machines. The solvers communicate data across their common interfaces within the same machine or over the network. The computations indicate that extensively separated flow fields can be computed without significant distortion by combining viscous and inviscid solvers.

Tavella, Domingo A.↗

DOE OSTI2016

Poster - 51: A tumor motion-compensating system with tracking and prediction – a proof-of-concept study

Purpose: This work reports on the development of a mechanical slider system for the counter-steering of tumor motion in adaptive Radiation Therapy (RT). The tumor motion was tracked using a weighted optical flow algorithm and its position is being predicted with a neural network (NN). Methods: The components of the proposed mechanical counter-steering system includes: (1) an actuator which provides the tumor motion, (2) the motion detection using an optical flow algorithm, (3) motion prediction using a neural network, (4) a control module and (5) a mechanical slider to counter-steer the anticipated motion of the tumor phantom. An asymmetrical cosine function and five patient traces (P1–P5) were used to evaluate the tracking of a 3D printed lung tumor. In the proposed mechanical counter-steering system, both actuator (Zaber NA14D60) and slider (Zaber A-BLQ0070-E01) were programed to move independently with LabVIEW and their positions were recorded by 2 potentiometers (ETI LCP12S-25). The accuracy of this counter-steering system is given by the difference between the two potentiometers. Results: The inherent accuracy of the system, measured using the cosine function, is −0.15 ± 0.06 mm. While the errors when tracking and prediction were included, is (0.04 ± 0.71) mm. Conclusion: A prototype tumor motion counter-steering system with tracking and prediction was implemented. The inherent errors are small in comparison to the tracking and prediction errors, which in turn are small in comparison to the magnitude of tumor motion. The results show that this system is suited for evaluating RT tracking and prediction.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Design of an Additively Manufactured Recuperator With 800 °C Inlet Temperature for sCO2 Power Cycle Application

Abstract Recuperators with design temperatures at and above 800 °C can further increase the thermal efficiency of supercritical CO2 power cycles by enabling higher turbine exhaust temperatures. Mar-M247 is a well-suited nickel-based superalloy for high temperature service due to its high creep strength that prevents excessive material thickness being required for pressure containment. Additive manufacturing using a high-speed laser-directed energy deposition (L-DED) process presents a promising solution, with build trials demonstrating the ability to produce nonconventional flow channels for enhanced heat transfer. A design process is presented that includes aerothermal and mechanical evaluation to maximize performance within the constraints of the manufacturing process. A 2-D heat transfer network and pressure drop code allows prediction of flow distribution and its effect on overall thermal performance. Established literature correlations, along with CFD simulation, inform the prediction of heat transfer coefficients and friction factors for the flowpaths and enhancement features in the heat exchanger core. Mechanical evaluation using finite element analysis (FEA) modeling with the intent of the ASME Boiler & Pressure Vessel Code (BPVC) Section VIII, Div. 2 assesses the operational safety of the design. The detailed design features annular finned passages that take advantage of helical flow paths to distribute the flow from separated headers to shared heat transfer surfaces. Performance predictions for the recuperator at a 50 kW scale provide insights into the feasibility of the additively manufactured (AM) process to produce recuperators on a commercial scale that extend existing operating envelopes.

Engineering↗

DOE OSTI2021

Where Have All of the Electrons Gone? Understanding the Role of Alternative Electron Flow and O 2 Reduction in Balancing Cellular Redox (Final Technical Report)

An informed understanding of the fundamentals of light-driven electron transfer reactions in photoautotrophic organisms is necessary to enable improved photosynthetic efficiencies for the production of renewable fuels and novel biomaterials. To obtain high photosynthetic yields, light energy must be efficiently coupled to the fixation of CO 2 . Sub-optimal environmental conditions and metabolic routing (caused by blocks in biosynthetic routes) can severely impact the conversion of light energy to biomass and lead to reactive oxygen production, which in turn can cause cellular damage and productivity losses. Hence, plants, algae, and photosynthetic bacteria have evolved a network of alternative outlets to sustain the flow of photosynthetically derived-electrons. Our research was focused on the nature and integration of these outlets. The data obtained in this project will inform efforts to rationally engineer crop plants and algae to improve photosynthetic yields by enhancing electron transfer reactions to CO 2 reduction and targeted biomass accumulation. A major project research thrust was focused on identifying and quantifying the flow of photosynthetic reductant through electron transfer circuits that result in the light-dependent reduction of O 2 . New energy management strategies were identified that are used when normal carbon assimilation pathways are compromised due to nutrient deprivation, and/or by a reduction in starch synthesis/carbon storage, all conditions resulting in highly reduced intracellular redox pools. The green alga Chlamydomonas reinhardtii, and likely many other algae, have multiple O 2 -reducing pathways that play critical roles in maintaining cellular metabolic balance and scavenging electrons that could potentially cause cellular damage, which in some instances leads to reduced photosynthetic yields but increased fitness. We explored the activities of three essential outlets associated with Chlamydomonas reinhardtii photosynthetic electron transport: (1) reduction of O 2 to H 2 O through Flavodiiron proteins (FLVs) and (2) Plastid Terminal Oxidases (PTOX), and (3) the synthesis of starch. Real-time measurements of O 2 exchange demonstrated that FLVs immediately engage during dark to light transitions, allowing electron transport when the CBBC is not fully activated. Under these conditions, we quantified, for the first time, maximal FLV activity and its overall capacity to direct photosynthetic electrons towards O 2 reduction. However, when starch synthesis is compromised, a greater proportion of electrons is directed toward O 2 reduction through the FLVs, while PTOX, which is sensitive to the PQ pool redox state, is activated. This suggests, that starch synthesis has an important role in priming/regulating CBBC and electron transport. We also identified a biological ‘switch’ in the green alga Chlamydomonas reinhardtii that reversibly restricts photosynthetic electron transport (PET) at the cytochrome b 6 f complex when reductant and ATP generated by PET are in excess of the capacity of carbon metabolism to utilize these products; we specifically show a restriction at this switch when sta6 mutant cells, which cannot synthesize starch, are limited for nitrogen (growth inhibition) and subjected to a dark to light transition. This restriction causes diminished electron flow to PSI, which prevents PSI photodamage, and the plastid alternative oxidase (PTOX) becomes fully activated, serving as an electron valve that dissipates excitation energy absorbed by PSII, thereby lessening PSII photoinhibition. Furthermore, illumination of the cells following the dark acclimation gradually diminishes the restriction at the switch. Future engineering of these switches may allow more effective electron transfer to lipid (biofuel) pathways and diminish the number of electrons “wasted” in the reduction of O 2 to water. Lastly, we explored metabolic routing of electrons during algal fermentation and discovered multiple novel pathways that are activated when the preferred anoxic routes are blocked. These provide valuable products (e.g. lactate, glycerol) that can be used in broad portfolio of biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Thermal Drawdown-Induced Flow Channeling in Fractured Geothermal Reservoirs

In this paper, we investigate the flow-channeling phenomenon caused by thermal drawdown in fractured geothermal reservoirs. A discrete fracture network-based, fully coupled thermal–hydrological–mechanical simulator is used to study the interactions between fluid flow, temperature change, and the associated rock deformation. The responses of a number of randomly generated 2D fracture networks that represent a variety of reservoir characteristics are simulated with various injection-production well distances. We find that flow channeling, namely flow concentration in cooled zones, is the inevitable fate of all the scenarios evaluated. We also identify a secondary geomechanical mechanism caused by the anisotropy in thermal stress that counteracts the primary mechanism of flow channeling. This new mechanism tends, to some extent, to result in a more diffuse flow distribution, although it is generally not strong enough to completely reverse flow channeling. We find that fracture intensity substantially affects the overall hydraulic impedance of the reservoir but increasing fracture intensity generally does not improve heat production performance. Finally, increasing the injection-production well separation appears to be an effective means to prolong the production life of a reservoir.

15 GEOTHERMAL ENERGY↗

NASA NTRS2011

Developing Flexible, High Performance Polymers with Self-Healing Capabilities

Flexible, high performance polymers such as polyimides are often employed in aerospace applications. They typically find uses in areas where improved physical characteristics such as fire resistance, long term thermal stability, and solvent resistance are required. It is anticipated that such polymers could find uses in future long duration exploration missions as well. Their use would be even more advantageous if self-healing capability or mechanisms could be incorporated into these polymers. Such innovative approaches are currently being studied at the NASA Kennedy Space Center for use in high performance wiring systems or inflatable and habitation structures. Self-healing or self-sealing capability would significantly reduce maintenance requirements, and increase the safety and reliability performance of the systems into which these polymers would be incorporated. Many unique challenges need to be overcome in order to incorporate a self-healing mechanism into flexible, high performance polymers. Significant research into the incorporation of a self-healing mechanism into structural composites has been carried out over the past decade by a number of groups, notable among them being the University of I1linois [I]. Various mechanisms for the introduction of self-healing have been investigated. Examples of these are: 1) Microcapsule-based healant delivery. 2) Vascular network delivery. 3) Damage induced triggering of latent substrate properties. Successful self-healing has been demonstrated in structural epoxy systems with almost complete reestablishment of composite strength being achieved through the use of microcapsulation technology. However, the incorporation of a self-healing mechanism into a system in which the material is flexible, or a thin film, is much more challenging. In the case of using microencapsulation, healant core content must be small enough to reside in films less than 0.1 millimeters thick, and must overcome significant capillary and surface tension forces to flow, mix and react to achieve healing. Vascular networks small enough to fit into such films must also overcome these same flow limitations. Self-healing has also been demonstrated in ionomeric substrates such as Surlyn , wherein the heat generated by a projectile impact triggers the latent ability of this substrate to flow back to its original shape. Recent work using Diels-Alder reactions have shown promise in bringing about actual reforming of broken chemical bonds to achieve self-healing [2]. All self-healing mechanisms that rely on the use of inherent latent substrate properties require some degree of polymer chain flow to achieve any significant level of healing.

Jolley, Scott T.↗

DOE OSTI2022

Designing Secure and Resilient Cyber-Physical Systems Using Formal Models

This work-in-progress paper proposes a design methodology that addresses the complexity and heterogeneity of cyber-physical systems (CPS) while simultaneously proving resilient control logic and security properties. The design methodology involves a formal methods-based approach by translating the complex control logic and security properties of a water flow CPS into timed automata. Timed automata are a formal model that describes system behaviors and properties using mathematics-based logic languages with precision. Due to the semantics that are used in developing the formal models, verification techniques, such as theorem proving and model checking, are used to mathematically prove the specifications and security properties of the CPS. This work-in-progress paper aims to highlight the need for formalizing plant models by creating a timed automata of the physical portions of the water flow CPS. Extending the time automata with control logic, network security, and privacy control processes is investigated. The final model will be formally verified to prove the design specifications of the water flow CPS to ensure efficacy and security.

42 ENGINEERING↗

DOE OSTI2023

Stretching of immersed polyelectrolyte brushes in shear flow

In this article, the way that polymer brushes respond to shear flow has important implications in various applications, including antifouling, corrosion protection, and stimuli-responsive materials. However, there is still much to learn about the behaviours and mechanisms that govern these responses. To address this gap in knowledge, our study uses in situ X-ray reflectivity to investigate how poly(styrene sulfonate) (PSS) brushes stretch and change in different environments, such as isopropanol (a poor solvent), water (a good solvent), and aqueous solutions containing various cations (Cs + , Ba 2+ , La 3+ , and Y 3+ ). We have designed a custom apparatus that exposes the PSS brushes to both tangential shear forces from the primary flow and upward drag forces from a secondary flow. Our experimental findings clearly show that shear forces have a significant impact on how the chains in PSS brushes are arranged. At low shear rates, the tangential shear force causes the chains to tilt, leading to brush contraction. In contrast, higher shear rates generate an upward shear force that stretches and expands the chains. By analysing electron density profiles obtained from X-ray reflectivity, we gain valuable insights into how the PSS brushes respond structurally, especially the role of the diffuse layer in this dynamic behaviour. Our results highlight the importance of the initial chain configuration, which is influenced by the solvent and cations present, in shaping how polymer brushes respond to shear flow. The strength of the salt bridge network also plays a crucial role in determining how easily the brushes can stretch, with stronger networks offering more resistance to stretching. Ultimately, our study aims to enhance our understanding of polymer physics at interfaces, with a particular focus on practical applications involving polymer brushes.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗