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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Parallel Computation of Unsteady Flows on a Network of Workstations

Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced.

Source record↗
NASA NTRS2011

Method of Simulating Flow-Through Area of a Pressure Regulator

The flow-through area of a pressure regulator positioned in a branch of a simulated fluid flow network is generated. A target pressure is defined downstream of the pressure regulator. A projected flow-through area is generated as a non-linear function of (i) target pressure, (ii) flow-through area of the pressure regulator for a current time step and a previous time step, and (iii) pressure at the downstream location for the current time step and previous time step. A simulated flow-through area for the next time step is generated as a sum of (i) flow-through area for the current time step, and (ii) a difference between the projected flow-through area and the flow-through area for the current time step multiplied by a user-defined rate control parameter. These steps are repeated for a sequence of time steps until the pressure at the downstream location is approximately equal to the target pressure.

Hass, Neal E.↗
DOE OSTI2024

Applications of Lifted Nonlinear Cuts to Convex Relaxations of the AC Power Flow Equations

Here, we demonstrate that valid inequalities, or lifted nonlinear cuts (LNC), can be projected to tighten the Second Order Cone (SOC), Convex DistFlow (CDF), and Network Flow (NF) relaxations of the AC Optimal Power Flow (AC-OPF) problem. We conduct experiments on 38 cases from the PGLib-OPF library, showing that the LNC strengthen the SOC and CDF relaxations in 100% of the test cases, with average and maximum differences in the optimality gaps of 6.2% and 17.5% respectively. The NF relaxation is strengthened in 46.2% of test cases, with average and maximum differences in the optimality gaps of 1.3% and 17.3% respectively. We also study the trade-off between relaxation quality and solve time, demonstrating that the strengthened CDF relaxation outperforms the strengthened SOC formulation in terms of runtime and number of iterations needed, while the strengthened NF formulation is the most scalable with the lowest relaxation quality improvement due to these LNC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

The Medical Science DMZ

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbpsþ) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2021

A Mathematical Analysis of an Example Delay Tolerant Network using the Theory of Sheaves

NASA’s High-Data Rate Architecture (HiDRA) project is working towards a general yet practical toolkit and knowledge base to help usher in the era of new technologies for space systems communications, such as optical links. The High-Rate Delay Tolerant Networking (HDTN) implementation falls under the umbrellas of both the toolkit and the knowledge base, as its advancements illuminate more general areas of Delay Tolerant Networking (DTN) that need growth. The goal of this paper is to explore the usage of particular mathematical machineries, namely temporal flow networks and sheaves, to identify fundamental, underlying structures in DTN for space systems. Satellites, space assets, ground stations, etc. give rise to a disconnected network, and it is the goal of DTN to glue disparate links together into a cohesive system, that is, a network. Depending on a given link, the latencies might be beyond that which the Transmission Control Protocol (TCP) can handle, and contact times might have one-way light times in excess of minute (sometimes significantly longer). Some links might be periodic (say, due to orbital mechanics) or they might not be. This diversity has made it difficult to probe the underlying structure. An immediate consequence is that DTNs in practice today are controlled by globally distributed contact plans (schedules), which are the input to the contact graph routing (CGR) algorithm. While this is effective for smaller networks, it will be very difficult to scale for future networks. Deeper and more rigorous theory is needed to bring DTN to the next evolutionary step. To this end, this paper introduces and suggests a mathematical framework for DTN, and applies it to a space network that is simulated using an orbital analysis toolkit. The tag-line for the structure known as sheaves is that they are the mathematically precise way of gluing local data together into unique, global data. If we consider routing, we see that networking is a “sheafy” science. We then discuss a simplified sheaf model, known as the cellular sheaf. The sheaf-theoretic analysis is presented and discussed, as it is hoped that this and related papers will help form the primordial ooze of DTN theory. Finally there is a section of future work suggesting follow-on research.

Delay Tolerant Networking↗
DOE OSTI2020

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the inputs. Consequently, the overall formulation sets up a procedure to predict the hyperparameters of this distribution which are then used to compute an objective function given training data. We demonstrate that this framework has the ability to provide for prediction confidence intervals based on the assumption of a probabilistic posterior, given an appropriate model architecture and adequate training data. The applicability of the present framework to cases with noisy measurements and limited observations is also assessed. To demonstrate the capabilities of this framework, we consider canonical regression problems of fluid dynamics from the viewpoint of reduced-order modeling and spatial data recovery for four canonical data sets. The examples considered in this study arise from (i) the shallow-water equations, (ii) a two-dimensional cylinder flow, (iii) the wake of a NACA0012 airfoil with a Gurney flap, and (iv) the NOAA sea surface temperature data set. Furthermore, the present results indicate that the probabilistic neural network not only produces a machine-learning-based fluid flow surrogate model but also systematically quantifies the uncertainty therein to assist with model interpretability.

42 ENGINEERING↗
DOE OSTI2020

Joint Estimation of Topology and Injection Statistics in Distribution Grids with Missing Nodes

Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This article discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular, we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance is validated using both linearized and nonlinear ac power flow samples in test distribution grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗
DOE OSTI2021

A Bilevel Approach for Identifying the Worst Contingencies for Nonconvex Alternating Current Power Systems

We address the bilevel optimization problem of identifying the most critical attacks to an alternating current (AC) power flow network. The upper-level binary maximization problem consists of choosing an attack that is treated as a parameter in the lower-level defender minimization problem. Instances of the lower-level global minimization problem by themselves are NP-hard due to the nonconvex AC power flow constraints, and bilevel solution approaches commonly apply a convex relaxation or approximation to allow for tractable bilevel reformulations at the cost of underestimating some power system vulnerabilities. Our main contribution is to provide an alternative branch-and-bound algorithm whose upper bounding mechanism (in a maximization context) is based on a reformulation that avoids relaxation of the AC power flow constraints in the lower-level defender problem. Lower bounding is provided with semidefinite programming (SDP) relaxed solutions to the lower-level problem. We establish finite termination with guarantees of either a globally optimal solution to the original bilevel problem, or a globally optimal solution to the SDP-relaxed bilevel problem which is included in a vetted list of upper-level attack solutions, at least one of which is a globally optimal solution to the bilevel problem. We demonstrate through computational experiments applied to IEEE case instances both the relevance of our contribution, and the effectiveness of our contributed algorithm for identifying power system vulnerabilities without resorting to convex relaxations of the lower-level problem. We conclude with a discussion of future extensions and improvements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Modeling of Cryogenic Heated-Tube Flow Boiling Experiments of Hydrogen and Helium With GFSSP

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled are hydrogen and helium. Predictions of wall temperature, heat transfer coefficient, and pressure drop are presented and compared to the test data.

multi-phase flow↗
NASA NTRSDate not supplied

Modeling of Cryogenic Heated-Tube Flow Boiling Experiments of Nitrogen and Methane with the Generalized Fluid System Simulation Program

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled are methane and nitrogen. Predictions of wall temperature and heat transfer coefficient are presented and compared to the test data.

Multi-Phase Flow↗
NASA NTRSDate not supplied

Modeling of Cryogenic Heated-Tube Flow Boiling Experiments of Hydrogen and Helium With the Generalized Fluid System Simulation Program

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed universal correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled in this study are liquid hydrogen and liquid helium. Predictions of local wall temperature and pressure drop are presented and compared to the test data.

multi-phase flow↗
NASA NTRS2015

Generalized Fluid System Simulation Program (GFSSP) - Version 6

The Generalized Fluid System Simulation Program (GFSSP) is a finite-volume based general-purpose computer program for analyzing steady state and time-dependent flow rates, pressures, temperatures, and concentrations in a complex flow network. The program is capable of modeling real fluids with phase changes, compressibility, mixture thermodynamics, conjugate heat transfer between solid and fluid, fluid transients, pumps, compressors, flow control valves and external body forces such as gravity and centrifugal. The thermo-fluid system to be analyzed is discretized into nodes, branches, and conductors. The scalar properties such as pressure, temperature, and concentrations are calculated at nodes. Mass flow rates and heat transfer rates are computed in branches and conductors. The graphical user interface allows users to build their models using the 'point, drag, and click' method; the users can also run their models and post-process the results in the same environment. The integrated fluid library supplies thermodynamic and thermo-physical properties of 36 fluids, and 24 different resistance/source options are provided for modeling momentum sources or sinks in the branches. Users can introduce new physics, non-linear and time-dependent boundary conditions through user-subroutine.

Majumdar, Alok↗
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↗
NASA NTRS1987

Computer-aided-engineering system for modeling and analysis of ECLSS integration testing

The accurate modeling and analysis of two-phase fluid networks found in environmental control and life support systems is presently undertaken by computer-aided engineering (CAE) techniques whose generalized fluid dynamics package can solve arbitrary flow networks. The CAE system for integrated test bed modeling and analysis will also furnish interfaces and subsystem/test-article mathematical models. Three-dimensional diagrams of the test bed are generated by the system after performing the requisite simulation and analysis.

Sepahban, Sonbol↗
DOE OSTI2021

SD-WAN Evaluation Criteria for a Defense Information Systems Network Expeditionary Customer Edge

The Defense Information Systems Agency (DISA) has identified a service provision gap midway between the capacity and capability of a Defense Information Systems Network (DISN) transport Edge Points of Presence (POP) and U.S. Department of Defense (DoD) Enterprise Classified Travel Kit (DECTK). Some deployed force 100-user Tactical Operations Centers are in field locations far removed from the DISN transport core, in challenging environmental conditions with limitations on space, weight, power, and cooling for network equipment. As part of a multi-phase project, Pacific Northwest National Laboratory (PNNL) will gather and analyze requirements, design, develop, prototype, and test a miniaturized and ruggedized DISN Customer Edge POP scaled to support approximately 100 users in a field Tactical Operations Center. An Expeditionary Customer Edge (ECE) will be more suitable for field deployment than a DISN transport Edge POP, using ruggedized hardware and network function virtualization (NFV) to operate in challenging field environmental conditions, plus reduce weight, power, and cooling requirements. A key enabling technology for ECE is Software Defined Wide Area Networking (SD-WAN). This document provides: • A brief overview of SD-WAN use cases and how they apply to ECE • How SD-WAN technology compares to existing networks like DISN that use Optical Transport Networking (OTN) and Multiprotocol Label Switching (MPLS) • SD-WAN functional requirements relevant to ECE • A description of an ECE Virtual Prototype, including simulated wide area network paths and a DISN-representative implementation of MPLS, in Cisco Modeling Labs • Detailed network flow walkthroughs • Evaluation criteria based on ECE-relevant SD-WAN functional requirements Finally, an appendix provides an informal evaluation of Speedify—a commercial retail Virtual Private Network service—against the ECE SD-WAN evaluation criteria.

42 ENGINEERING↗
NASA NTRS1998

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.

Schallhorn, Paul↗