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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2021

Latent map Gaussian processes for mixed variable metamodeling

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. Here we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.

42 ENGINEERING↗
DOE OSTI2025

Power Flow Geometry and Approximation

Here, the power flow equations are important in numerous power systems problems of practical interest which consider alternating current power flow (ACPF) physics. Perhaps the most well studied being the alternating current optimal power flow problem (ACOPF), seeking to optimize the operation of an electric power system. Due to their non-linearity, problems which include the power flow equations are typically challenging, particularly in optimization. Interestingly, the set of solutions to the power flow equations forms a smooth manifold. As a result, differential geometry can be used to describe and analyze this set of equations. This approach has proven effective in several engineering applications (e.g., solving ACOPF and analyzing the solution space boundary). Central to the success of this approach is an understanding of the power flow manifold's geometry. In this work, we develop the geometric and topological properties of this manifold using concepts from differential geometry. After demonstrating the convenience of this manifold's representation as a function's graph, computational methods are emphasized: we develop retractions, error bounds for linear approximation, and formulas for evaluating the Riemannian metric (including associated objects such as geodesics and the curvature tensor). Scalar curvature and the second fundamental form play a new role in quantifying the quality of linear approximations, like the popular direct current approximation. All functions are implemented in Julia and available in an online repository. Proofs are included for completeness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2021

$\mathrm{A}$ $\mathrm{CFT}$ distance conjecture

We formulate a series of conjectures relating the geometry of conformal manifolds to the spectrum of local operators in conformal field theories in d > 2 spacetime dimensions. We focus on conformal manifolds with limiting points at infinite distance with respect to the Zamolodchikov metric. Our central conjecture is that all theories at infinite distance possess an emergent higher-spin symmetry, generated by an infinite tower of currents whose anomalous dimensions vanish exponentially in the distance. Stated geometrically, the diameter of a non-compact conformal manifold must diverge logarithmically in the higher-spin gap. In the holographic context our conjectures are related to the Distance Conjecture in the swampland program. Interpreted gravitationally, they imply that approaching infinite distance in moduli space at fixed AdS radius, a tower of higher-spin fields becomes massless at an exponential rate that is bounded from below in Planck units. We discuss further implications for conformal manifolds of superconformal field theories in three and four dimensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Mocking the u-plane integral

Abstract The u -plane integral is the contribution of the Coulomb branch to correlation functions of $${\mathcal {N}}=2$$ N = 2 gauge theory on a compact four-manifold. We consider the u -plane integral for correlators of point and surface observables of topologically twisted theories with gauge group $$\mathrm{SU}(2)$$ SU ( 2 ) , for an arbitrary four-manifold with $$(b_1,b_2^+)=(0,1)$$ ( b 1 , b 2 + ) = ( 0 , 1 ) . The u -plane contribution equals the full correlator in the absence of Seiberg–Witten contributions at strong coupling, and coincides with the mathematically defined Donaldson invariants in such cases. We demonstrate that the u -plane correlators are efficiently determined using mock modular forms for point observables, and Appell–Lerch sums for surface observables. We use these results to discuss the asymptotic behavior of correlators as function of the number of observables. Our findings suggest that the vev of exponentiated point and surface observables is an entire function of the fugacities.

Mathematics↗
DOE OSTI2021

The Stellar decomposition: A compact representation for simplicial complexes and beyond

Here, we introduce the Stellar decomposition, a model for efficient topological data structures over a broad range of simplicial and cell complexes. A Stellar decomposition of a complex is a collection of regions indexing the complex’s vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region’s vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain. They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by a corresponding global topological data structure on the complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex’s spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-manifold simplicial complexes. We conclude with a high-level description of several mesh processing and analysis applications that utilize Stellar trees to process large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving Experimental Design through Uncertainty Analysis

In this paper, the development of a fission-gas collecting and physical-analysis-enabling instrument was proposed for small-volume determination. Analysis specifications require a design capable of accurately and repeatably determining volumes in the range of 0.07–2.5 mL. This system relies on a series of gas expansions originating from a cylinder with known internal volume. The combined gas law is used to derive the unknown volumes from these expansions. Initial system designs included one of two known volumes, 11.85 ± 0.34 mL and 5.807 ± 0.078 mL, with a manifold volume of 32 mL. Results obtained from modeling this system’s operation showed that 0.07 mL can be determined with a relative expanded uncertainty greater than 300% (k = 2) for a single replicate, which was unacceptable for the proposed experimental design. Initial modeling showed that the volume connecting the known volume and rodlet, i.e., the manifold volume, and the sensitivity of the pressure sensor were key contributors to the expanded uncertainty of the measured rodlet volume. The system’s design limited the available options for pressure sensors, so emphasis was placed on the design of the manifold volume. The final system design reduced the manifold volume to 17 mL. These changes in design, combined with replicate analysis, were able to reduce the relative expanded uncertainty by ±12% (k = 2) for the 0.07 mL volume.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1949

Investigation of Icing Characteristics of Typical Light Airplane Engine Induction Systems

The icing characteristics of two typical light-airplane engine induction systems were investigated using the carburetors and manifolds of engines in the horsepower ranges from 65 to 85 and 165 to 185. The smaller system consisted of a float-type carburetor with an unheated manifold and the larger system consisted of a single-barrel pressure-type carburetor with an oil-jacketed manifold. Carburetor-air temperature and humidity limits of visible and serious Icing were determined for various engine power conditions. Several.methods of achieving ice-free induction systems are discussed along with estimates of surface heating requirements of the various induct ion-system components. A study was also made of the icing characteristics of a typical light-airplane air scoop with an exposed filter and a modified system that provided a normal ram inlet with the filter located in a position to Induce inertia separation of the free water from the charge air. The principle of operation of float-type carburetors is proved to make them inherently more susceptible to icing at the throttle plate than pressure-type carburetors.. The results indicated that proper jacketing and heating of all parts exposed to the fuel spray can satisfactorily reduce or eliminate icing in the float-type carburetor and the manifold. Pressure-type carburetors can be protected from serious Icing by proper location of the fuel-discharge nozzle combined with suitable application of heat to critical parts.

Coles, W. D.↗
NASA NTRS1991

Method of injecting fluid propellants into a rocket combustion chamber

A rocket injector is provided with multiple sets of manifolds for supplying propellants to injector elements. Sensors transmit the temperatures of the propellants to a suitable controller which is operably connected to valves between these manifolds and propellant storage tanks. Additional valves are opened to furnish propellants to more of the manifolds when cryogenic propellant temperatures are sensed. Only a portion of the valves are opened to furnish propellants to some of the manifolds when lower temperatures are sensed.

Schneider, Steven J.↗
NASA NTRS1991

Biofilm monitoring coupon system and method of use

An apparatus and method is disclosed for biofilm monitoring of a water distribution system which includes the mounting of at least one fitting in a wall port of a manifold in the water distribution system with a passage through the fitting in communication. The insertion of a biofilm sampling member is through the fitting with planar sampling surfaces of different surface treatment provided on linearly arrayed sample coupons of the sampling member disposed in the flow stream in edge-on parallel relation to the direction of the flow stream of the manifold under fluid-tight sealed conditions. The sampling member is adapted to be aseptically removed from or inserted in the fitting and manifold under a positive pressure condition and the fitting passage sealed immediately thereafter by appropriate closure means so as to preclude contamination of the water distribution system through the fitting. The apparatus includes means for clamping the sampling member and for establishing electrical continuity between the sampling surfaces and the system for minimizing electropotential effects. The apparatus may also include a plurality of fittings and sampling members mounted on the manifold to permit extraction of the sampling members in a timed sequence throughout the monitoring period.

Sauer, Richard L.↗
NASA NTRS1991

Extended temperature range rocket injector

A rocket injector is provided with multiple sets of manifolds for supplying propellants to injector elements. Sensors transmit the temperatures of the propellants to a suitable controller which is operably connnected to valves between these manifolds and propellant storage tanks. When cryogenic propellant temperatures are sensed, only a portion of the valves are opened to furnish propellants to some of the manifolds. When lower temperatures are sensed, additional valves are opened to furnish propellants to more of the manifolds.

Schneider, Steven J.↗
NASA NTRS1999

Optimization of Insertion Cost for Transfer Trajectories to Libration Point Orbits

The objective of this work is the development of efficient techniques to optimize the cost associated with transfer trajectories to libration point orbits in the Sun-Earth-Moon four body problem, that may include lunar gravity assists. Initially, dynamical systems theory is used to determine invariant manifolds associated with the desired libration point orbit. These manifolds are employed to produce an initial approximation to the transfer trajectory. Specific trajectory requirements such as, transfer injection constraints, inclusion of phasing loops, and targeting of a specified state on the manifold are then incorporated into the design of the transfer trajectory. A two level differential corrections process is used to produce a fully continuous trajectory that satisfies the design constraints, and includes appropriate lunar and solar gravitational models. Based on this methodology, and using the manifold structure from dynamical systems theory, a technique is presented to optimize the cost associated with insertion onto a specified libration point orbit.

Howell, K. C.↗
NASA NTRS2010

Capillary condenser/evaporator

A heat transfer device is disclosed for transferring heat to or from a fluid that is undergoing a phase change. The heat transfer device includes a liquid-vapor manifold in fluid communication with a capillary structure thermally connected to a heat transfer interface, all of which are disposed in a housing to contain the vapor. The liquid-vapor manifold transports liquid in a first direction and conducts vapor in a second, opposite direction. The manifold provides a distributed supply of fluid (vapor or liquid) over the surface of the capillary structure. In one embodiment, the manifold has a fractal structure including one or more layers, each layer having one or more conduits for transporting liquid and one or more openings for conducting vapor. Adjacent layers have an increasing number of openings with decreasing area, and an increasing number of conduits with decreasing cross-sectional area, moving in a direction toward the capillary structure.

Valenzuela, Javier A.↗
NASA NTRS2004

Improved Cryostat for Cooling a Wide Panel

An improved cryostat has been developed for cooling a wide panel evenly over its surface to a temperature of -423 F (approximately equal to -253 C) by use of liquid helium. Originally, the cryostat was to be used in measuring apparent strains in wide aluminum/lithium panels as functions of temperature in order to develop data for temperature compensation of the readings of strain gauges on a tank containing liquid hydrogen. Relative to the cryostat used previously for this purpose, the improved cryostat can be prepared for a test in less time, and it loses less helium during each test. Each wide panel to be tested is instrumented with thermocouples in preparation for a test. The previous cryostat was made of two aluminum halves that, for each test, were sandwiched together and sealed around the instrumented wide panel to be tested. The panel was thus enclosed in a plenum. The cryostat and adjacent panel areas protruding from the cryostat were then coated with a thermally insulating foam. During a test, liquid helium was made to flow into the plenum through a port on the bottom. The helium vaporized and expanded, filling the plenum with cold helium gas, which eventually flowed out of the plenum through a port on the top. The nature of the flow was such that a significant portion of the helium did not come into contact with the wide panel; hence, cooling was less efficient than it might otherwise have been. After completion of each test, the foam and the cryostat were separated from the panel. The cryostat was cleaned and prepared for installation on another instrumented wide panel for the next test. It took 28 hours to install the cryostat onto the instrumented panel, apply the foam, and perform ancillary operations in preparation for a test. The volume of liquid helium consumed during each test was 750 liters. The improved cryostat (see figure) includes an upper section and a lower section, both of which include permanent housings made of a thermally insulating foam 2-in. (approximately equal to 5-cm) thick. A liquid-helium- injection manifold is attached to the inside of the top section. The bottom section includes an outlet for helium gas. The manifold contains slots that, when the cryostat is installed on the panel, are located approximately 1 in. (approximately equal to 2.5 cm) from the wide panel. The array of slots spans a substantial portion of the area of the panel. The top and bottom sections of the cryostat are sealed to the panel by use of polytetrafluoroethylene cord and aluminum tape. Liquid helium is fed into the manifold from the top. The helium leaves the manifold through the slots and thus impinges directly on the panel. Hence, all the helium entering the cryostat must come into contact with the panel before leaving the cryostat. After a test, the cryostat is removed from the panel and reinstalled onto another panel for the next test. Installation of the cryostat on an instrumented panel takes a negligible amount of time, in comparison with the 28 hours associated with the previous cryostat. The amount of liquid helium consumed during a test in the improved cryostat is 500 liters - 250 liters less than before.

Clifton, W. B.↗
NASA NTRS2011

Modeling of Supersonic Film Cooling on the J-2X Nozzle Extension

Supersonic film cooling (SSFC) of nozzles has been used in several liquid rocket engine designs, and is being applied to the nozzle extension (NE) of the J-2X upper stage engine currently under development. Turbine exhaust gas (TEG) is injected tangentially from a manifold along the NE, and provides a thermal barrier from the core nozzle flow for the NE. As the TEG stream mixes with the nozzle flow, the effectiveness of the thermal barrier is reduced. This paper documents computational fluid dynamics (CFD) analysis work performed by NASA Marshall Space Flight Center (MSFC) to model the flow of the TEG through the manifold, into the nozzle, and the subsequent mixing of the TEG stream with the core flow. The geometry and grid of the TEG manifold, structural support ribs, and the NE wall will be shown, and the CFD boundary conditions described. The Loci-CHEM CFD code used in this work will also be briefly described. A unique approach to modeling the combined TEG manifold/thrust chamber assembly (TCA) was employed, as it was not practical to model the entire 360 circumferential range in one simulation. Prior CFD validation work modeling Calspan SSFC experiments in the early 1990s, documented in a previous AIAA paper, will also be briefly discussed. The fluid dynamics of the TEG flow through the manifold, into and between the structural support ribs, and into the nozzlette that feeds the TCA will be described. Significant swirl and non-uniformities are present, which along with the wakes from the ribs, act to degrade the film cooling effectiveness compared to idealized injection of TEG gas. The effect of these flow characteristics on the adiabatic wall temperature profile on the NE will be discussed.

Ruf, Joseph H.↗