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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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Custom-built manifold mounted on cleaning basket enables simultaneous flushing of 80 tubes with cleaning solution. In original application, tubes components of rocket-engine nozzle under construction. However, basic manifold configuration adapted to other applications (e.g., fabrication of heat exchangers) in which there is need for simultaneous cleaning of many tubes of identical size and shape.
A new post-processing technique has been developed, based on the Intrinsic Low Dimensional Manifold (ILDM) method of Maas and Pope. The ILDM method is a dynamical systems approach to the simplification of large chemical kinetic mechanisms. By identifying low-dimensional attracting manifolds, the method allows complex full mechanisms to be parameterized by just a few variables: In effect, generating reduced chemical mechanisms by an automatic procedure. These resulting mechanisms however, still retain all the species used in the full mechanism. The NO(x) post-processor takes an ILDM reduced mechanism and attempts to map this mechanism to the results of a CFD calculation. This mapping allows the NO(x) concentrations at each grid node to be obtained from the ILDM reduced mechanism, as well as other trace species of interest. Because a mapping procedure is used, this method is very fast, being able to process one million node calculations in just a few minutes.
The mechanisms of evaporative freezing of leaking propellant and the creation of flow stoppages within injector manifolds is discussed. A quantitative analysis of the conditions, including the existence of minimum and maximum leak rates, for the accumulation of evaporatively frozen propellant is presented. Clogging of the injector manifolds of the Apollo SPS and the Gemini OAMS engines by the freezing of leaking propellant is predicted and the seriousness of the consequences are discussed. Based on the analysis a realistic evaluation of selected techniques to eliminate flow stoppages by frozen propellant is made.
Riccati differential equations are studied as vector fields on a Grassman manifold. Such manifolds serve as a compactification for the Riccati equations.
An improved exhaust system for an internal combustion gasoline-and/or diesel-fueled engine includes an engine exhaust manifold which has been fabricated from carbon- carbon composite materials in operative association with an exhaust pipe ducting which has been fabricated from carbon-carbon composite materials. When compared to conventional steel. cast iron. or ceramic-lined iron paris. the use of carbon-carbon composite exhaust-gas manifolds and exhaust pipe ducting reduces the overall weight of the engine. which allows for improved acceleration and fuel efficiency: permits operation at higher temperatures without a loss of strength: reduces the "through-the wall" heat loss, which increases engine cycle and turbocharger efficiency and ensures faster "light-off" of catalytic converters: and, with an optional thermal reactor, reduces emission of major pollutants, i.e. hydrocarbons and carbon monoxide.
This paper demonstrates continuous optimization on the differentiable manifold formed by continuous constraint functions. The first order tensor geodesic differential equation is solved on the manifold in both numerical and closed analytic form for simple nonlinear programs. Advantages and disadvantages with respect to conventional optimization techniques are discussed.
When the planar circular restricted 3-body problem (RTBP) is periodically perturbed, most unstable resonant periodic orbits become invariant tori. In this study, we 1) develop a quasi-Newton method which simultaneously solves for the tori and their center, stable, and unstable directions; 2) implement continuation by both perturbation as well as rotation numbers; 3) compute Fourier-Taylor parameterizations of the stable and unstable manifolds; 4) globalize these manifolds; 5) compute homoclinic and heteroclinic connections. Our methodology improves on efficiency and accuracy compared to prior studies, and applies to a variety of periodic perturbations. We demonstrate the tools on the planar elliptic RTBP.
The Manifold Integrated Pyrovalve (MI-PV) provides a state-of-the-art, low mass, low volume, normally-closed (NC) pyrotechnic isolation valve configuration which is optimized for small free-flier applications. Small scale free-flier (e.g. CubeSat) applications require high pressure fluid isolation valves with leak rates low enough to endure long pad stays, and often have severe mass and volume limitations. The MI-PV utilizes a unique configuration where the shear section and the pyrotechnic ram assembly (PRA) are installed directly into a ported gas manifold, allowing elimination of heavy fluid fittings and connections. This configuration can be readily scaled and adapted to the needs of other long duration missions.
A relatively simple equation has been found to express with fair accuracy, variation in manifold-charge temperature with charge in engine operating conditions. This equation and associated curves have been checked by multi cylinder-engine data, both test stand and flight, over a wide range of operating conditions. Average mixture temperatures, predicted by the equations of this report, agree reasonably well with results within the same range of carburetor-air temperatures from laboratories and test stands other than the NACA.
This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.
Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.
Integral manifolds and nonlinear oscillations
Irreducible unitary representations of restricted Lorentz group on manifolds of time-, light- and spacelike 4-vectors, obtaining plane-wave solutions of Klein-Gordon equation
The invariant manifolds associated with the outer planets are extremely large objects in phase space. They are trajectories in the ecliptic which intersect one another. This enables a low energy single impulse transfer between the planets which requries several orbital periods.
A key challenge in low-thrust trajectory design is generating preliminary solutions that simultaneously specify the spacecraft position and velocity vectors, as well as the thrust history. To mitigate this difficulty, dynamical structures within a combined low-thrust circular restricted 3-body problem (CR3BP) are nvestigated as candidate solutions to seed initial low-thrust trajectory designs. The addition of low-thrust to the CR3BP modifies the locations and stability of the equilibria, offering novel geometries for mission applications. Transfers between these novel equilibria are constructed by leveraging the associated stable and unstable manifolds and insights from the low-thrust CR3BP.
A key challenge in low-thrust trajectory design is generating preliminary solutions that simultaneously specify the spacecraft position and velocity vectors, as well as the thrust history. To mitigate this difficulty, dynamical structures within a combined low-thrust circular restricted 3-body problem (CR3BP) are investigated as candidate solutions to seed initial low-thrust trajectory designs. The addition of low-thrust to the CR3BP modifies the locations and stability of the equilibria, offering novel geometries for mission applications. Transfers between these novel equilibria are constructed by leveraging the associated stable and unstable manifolds and insights from the low-thrust CR3BP.
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.