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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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A multipoint fuel injection system includes a plurality of fuel manifolds. Each manifold is in fluid communication with a plurality of injectors arranged circumferentially about a longitudinal axis for multipoint fuel injection. The injectors of separate respective manifolds are spaced radially apart from one another for separate radial staging of fuel flow to each respective manifold.
Reachability manifold computation suffers from the curse of dimensionality and for large state spaces is computationally intractable. This paper examines the use of continuation methods to address this issue by formulating the reachability sub-space manifold calculation into a number of initial valued problems. As a result of computing the reachability manifold for a subspace of interest, an exponential improvement in computational cost occurs. This concept is applied to a position subspace reachability problem of a spacecraft in a Keplerian orbit under maximum thrust constraints. Future work includes a comparison of the proposed method with computing reachability manifolds using viscosity solutions of the Hamilton Jacobi Bellman partial differential equation.
Lissajous orbits and approximation of their invariant manifolds are used to generate landing trajectories to the surface of Europa. Each lissajous is discretized into individual revolutions that each resemble a periodic orbit. The unstable manifolds of each individual revolution propagated forward in time generate more surface coverage than manifolds of simple libration point orbits such as halo or Lyapunov orbits. The stable manifolds propagated backwards in time from the individual lissajous revolutions provide direct connections to the last phase of a moon tour. The strategy developed produces ballistic landing trajectories with a wide surface coverage, and allows for the decoupling of the landing and moon tour phase by using the lissajous as an intermediate staging orbit. The multiple revolutions of the lissajous, multiple departure times along each revolution, multiple quasi periodic options at each energy, and multiple energies of the lissajous family provide many degrees of freedom in the design process.
When the planar circular restricted 3-body problem is periodically perturbed, most unstable periodic orbits become invariant tori. However, 2D Poincare ́ sections no longer work to find their manifolds’ intersections; new methods are needed. In this study, we first review a method of restricting the intersection search to only certain manifold subsets. We then implement this search using Julia and OpenCL, representing the manifolds as triangular meshes and gaining a 30x speedup using GPUs. We finally show how to use manifold parametrizations to refine the ap- proximate connections found in the mesh search. We demonstrate the tools on the planar elliptic RTBP.
An engine system includes an engine having first and second exhaust manifolds each having outlet ports and a bypass runner. A first turbocharger is in fluid communication with the outlet port of the first manifold, and a second turbocharger in fluid communication with the outlet port of the second manifold. An exhaust aftertreatment device is in fluid communication with the first and second turbochargers. A turbocharger bypass circuit includes a valve assembly having an inlet side connected in fluid communication with the bypass runners of the first and second manifolds, an outlet side in fluid communication with the aftertreatment device, and a valve having an open position in which the inlet and outlet sides are in fluid communication and a closed position in which the inlet and outlet sides are not in fluid communication.
A multichannel inlet system for a mass spectrometer includes a plurality of valve assemblies coupled to a manifold, and a pulsed valve driver. The manifold is configured to be connected in fluid connection with an ion trap of the mass spectrometer. Each valve assembly includes a valve and an injection port operably coupled to receive the reagent. The valve has an actuated state in which the valve provides fluid communication between the injection port and the manifold, and an unactuated state in which the valve substantially prevents fluid communication between the injection port and the manifold. The pulsed valve driver is operably connected to receive a pulse signal sequence from a processor, and is configured to generate pulsed valve drive signals for one or more of the valves based on the pulse signal sequence to cause a corresponding one of the valves to be in the actuated state.
In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.
Ensuring long-term voltage stability is critical for reliable operations of power grids. High share of distributed energy resources (DERs) can create complicated system operation modes that may invalidate the traditional long-term voltage stability analysis based on typical operation modes. To address this challenge, this paper investigates how to compute the shortest path to the voltage stability boundary in the DER aggregated load space with large dispersion. Instead of working in the Euclidean space, we establish the analysis and computations on the algebraic power flow manifold to better capture the curvature change of the shortest path along the direction of losing stability. A modified optimal control framework is presented for obtaining the ground-truth of the smooth shortest path on the manifold. To efficiently and accurately solve for the shortest path, we further leverage the geometric features of the power flow manifold and propose a tri-sectional approximation model that is scalable for large-scale systems. Several numerical examples, up to the 1354-bus system, with different DER penetration levels and high dimensional renewable power injection variations are evaluated. The simulation results demonstrate that the tri-sectional approximation achieves high accuracy and efficiency to approximate the shortest path to the voltage stability boundary.
We study the perturbative unitarity of scattering amplitudes in general dimensional reductions of Yang–Mills theories and general relativity on closed internal manifolds. For the tree amplitudes of the dimensionally reduced theory to have the expected high-energy behavior of the higher-dimensional theory, the masses and cubic couplings of the Kaluza–Klein states must satisfy certain sum rules that ensure there are nontrivial cancellations between Feynman diagrams. These sum rules give constraints on the spectra and triple overlap integrals of eigenfunctions of Laplacian operators on the internal manifold and can be proven directly using Hodge and eigenfunction decompositions. One consequence of these constraints is that there is an upper bound on the ratio of consecutive eigenvalues of the scalar Laplacian on closed Ricci-flat manifolds with special holonomy. This gives a sharp bound on the allowed gaps between Kaluza–Klein excitations of the graviton that also applies to Calabi–Yau compactifications of string theory.
Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.
An anti-clogging perforated plate atomizer is designed for high temperature and salinity applications in a novel solar-powered zero liquid discharge humidification-dehumidification desalination system (US Patent Application US62882953). Herein this paper presents a detailed discussion on its design, and operation. Experiments are performed on seven atomizers to study the effect of design, and operating parameters on spray cone angle, and average droplet diameter. Spray cone angle remains constant with change in air mass flux, and increases with increasing orifice diameter, and manifold diameter. It also increases initially with water mass flow rate, and manifold height, before attaining a constant value. Average droplet diameter increases with increasing water mass flow rate, and decreasing air mass flux, orifice diameter, and manifold diameter. Further, anti-clogging performance of the atomizer is tested with hypersaline water of 100,000 ppm total dissolved solid (NaCl) at elevated temperatures such as air at 175 °C and saline water at 45 °C. Results show no clogging for 2–13 h of operation. Near-complete suppression of atomizer clogging makes our new additively-manufactured perforated plate atomizer an ideal fit for high salinity, and zero liquid discharge humidification-dehumidification desalination systems. Additionally, its open-surface design allows additional surface modifications to further reduce clogging, and enhance self-cleaning characteristics.
Rapid growth in artificial intelligence and data center workloads demands high-performance liquid cooling to manage increasing chip power. This study presents two metal-additive-manufactured cold plates with sinusoidal fins, constant-amplitude wavy fins and linearly variable-amplitude wavy fins and compares them against metal-additive-manufactured straight fins using experiments conducted at 1 kW heat dissipation as well as high-fidelity 3D conjugate computational fluid dynamic simulations. The cold plates were printed in AlSi10Mg material and underwent design using a Python-automated workflow prior to manufacture and testing. The experiments show that wavy fins reduce the normalized thermal resistance by 35 to 45 % at water flow rates from 1 to 4 LPM. At a fixed 20 kPa pressure drop, the variable-waviness design lowered peak surface temperature by 9 °C and thermal resistance by 51 %, while edge-channel maldistribution in the constant wavy fin design limited gains. A thermal resistance breakdown revealed that 55–63 % of the total thermal resistance in wavy designs comes from base heat conduction, 27–33 % from fin heat conduction, and 9–13 % from fin heat convection, indicating the need to address conduction bottlenecks. Parametric sweeps identify a 3 mm fin pitch as optimal, and that horizontal inlet/outlet manifolds further reduce pressure drop by 30–60 % and thermal resistance by 9–16 % relative to vertical inlet-outlet manifolds. The results yield comprehensive guidelines for fin geometry, manifold alignment, material selection and additive-manufacturing constraints to realize high-performance liquid-cooled cold plates for power-dense electronics.
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent transport. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then “looked-up” during the run-time to estimate the high-dimensional system state by the turbulent transport system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look-up model, can yield more accurate results. Here, we build on the base formulation and implementation to include the dynamically generated Thermochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate its superior performance.
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
We report recent experiments have observed that the chemical and photophysical properties of molecules can be modified inside an optical Fabry– Pérot microcavity under collective vibrational strong coupling (VSC) conditions, and such modification is currently not well understood by theory. In an effort to understand the origin of such cavity-induced phenomena, some recent studies have focused on the effect of the cavity environment on the nonlinear optical response of the molecular subsystem. Here, we use a recently proposed protocol for classical cavity molecular dynamics simulations to numerically investigate the linear and the nonlinear response of liquid carbon dioxide under such VSC conditions following an optical pulse excitation. We find that applying a strong pulse of excitation to the lower hybrid light–matter state, i.e., the lower polariton (LP), can lead to an overall molecular nonlinear absorption that is enhanced by up to two orders of magnitude relative to the excitation outside the cavity. This polariton-enhanced multiphoton absorption also causes an ultrashort LP lifetime (0.2 ps) under strong illumination. Unlike usual polariton relaxation processes—whereby polaritonic energy transfers directly to the manifold of singly excited vibrational dark states—under the present mechanism, the LP transfers energy directly to the manifold of higher vibrationally excited dark states; these highly excited dark states subsequently relax to the manifold of singly excited states with a lifetime of tens of ps. Because the present mechanism is generic in nature, we expect these numerical predictions to be experimentally observed in different molecular systems and in cavities with different volumes.
Abstract In the sigma model, soft insertions of moduli scalars enact parallel transport ofS-matrix elements about the finite-dimensional moduli space of vacua, and the antisymmetric double-soft theorem calculates the curvature of the vacuum manifold. We explore the analogs of these statements in gauge theory and gravity in asymptotically flat spacetimes, where the relevant moduli spaces are infinite-dimensional. These models have spaces of vacua parameterized by (trivial) flat connections on the celestial sphere, and soft insertions of photons, gluons, and gravitons parallel transportS-matrix elements about these infinite-dimensional manifolds. We argue that the antisymmetric double-soft gluon theorem ind + 2 bulk dimensions computes the curvature of a connection on the infinite-dimensional space Map ( S d , G ) / G , whereGis the global part of the gauge group. The analogous metrics in abelian gauge theory and gravity are flat, as indicated by the vanishing of the antisymmetric double-soft theorems in those models. In other words, Feynman diagram calculations not only know about the vacuum manifold of Yang–Mills theory, they can also be used to compute its curvature. The results have interesting implications for flat space holography.
It remains a great challenge in condensed matter physics to develop a method to treat strongly correlated many-body systems with balanced accuracy and efficiency. We introduce an extended Gutzwiller (EG) method incorporating a manifold technique, which builds an effective manifold of the many-body Hilbert space, to describe the ground- and excited-state properties of strongly correlated electrons. Here we systematically apply an EG projector onto the ground and excited states of a non-interacting system. Diagonalization of the true Hamiltonian within the manifold formed by the resulting EG wavefunctions gives the approximate ground and excited states of the correlated system. To validate this technique, we implement it on even-numbered fermionic Hubbard rings at half-filling with periodic boundary conditions, and compare the results with the exact diagonalization (ED) method. The EG method is capable of generating high-quality ground and low-lying excited state wavefunctions, as evidenced by the high overlaps of wavefunctions between the EG and ED methods. Favorable comparisons are also achieved for other quantities including the total energy, the double occupancy, the total spin and the staggered magnetization. With the capability of accessing the excited states, the EG method can capture the essential features of the one-electron removal spectral function that contains contributions from states deep in the excited spectrum. Finally, we provide an outlook on the application of this method on large extended systems.
We establish a dynamical mechanism to explain the origin of the asymmetry between the arms observed in some barred disc galaxies, where one of the two arms emanating from the bar ends is very well defined, while the second one displays a ragged structure, extending between its ridge and the bar. To this purpose, we study the invariant manifolds associated with the Lyapunov periodic orbits around the unstable equilibrium points at the ends of the bar. Matter from the galaxy centre is transported along these manifolds to the periphery, forming this way the spiral arms that emanate from the bar ends. If the mass distribution in the galaxy centre is not homogeneous, because of an asymmetric bar with one side stronger than the other, or because of a non-centred bulge, the dynamics about the two unstable Lagrange points at the ends of the bar will not be symmetric as well. One of their invariant manifolds becomes more extended than the other, enclosing a smaller section and the escaping orbits on it are fewer and dispersed in a wider region. The result is a weaker arm and more ragged than the one at the other end of the bar.