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Results for “manifold optimization”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Combining complex flow manifold with three dimensional woven lattices as a thermal management unit

The present invention is directed to a manifold for directing cooling fluid and/or gas to a heat exchanger in a flow configuration designed to optimize heat transfer from the heat exchanger. The manifold can take many different forms such as a layered construction with distributed inlet paths, local outlet paths, a central collection changer and a path for fluid removal. The manifold can be formed from a metal, plastic, rubber, ceramic, or other heat resistant material known to or conceivable by one of skill in the art. The manifold can also be combined with any type of heat exchanger known to or conceivable by one of skill in the art to form a thermal management unit. To optimize overall properties such as low pressure drop, high heat transfer, and excellent temperature uniformity of the thermal management unit, the manifold can be graded, expanded and scaled as needed.

Hemker, Kevin J.↗
NASA NTRS2002

A Discontinuous Galerkin Discretization of the Eikonal Equation on Curved Piecewise Isoparametric Triangulated Manifolds

This viewgraph presentation provides information on optimizing the travel distance between two points on a curved surface. The presentation addresses the single source shortest path problem, fast algorithms for estimating the eikonal equation, fast schemes and barrier theorems, and the discontinuous Galerkin method, including hyperbolic causality, finite element method, scalars, and marching the discontinuous Galerkin Eikonal approximation.

Barth, TIm↗
DOE OSTI2023

Structure-aware methods for expensive derivative-free nonsmooth composite optimization

We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (MS-P) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (GOOMBAH) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at https://github.com/POptUS/ IBCDFO/.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Metal additively manufactured wavy fin cold-plate architecture for improved thermal-hydraulic performance

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.

3d printing↗
NASA NTRS2011

Flyby Design Using Heteroclinic and Homoclinic Connections of Unstable Resonant Orbits

Tour designs using flybys have traditionally been studied using two-body patched conic methods. Previous work has shown that trajectories designed using these techniques and with optimization methods follow the invariant manifolds of unstable resonant orbits as they transition between resonances. This work is continued here by computing heteroclinic and homoclinic trajectories associated with these unstable resonant orbits. These trajectories are used with multiple resonances to design flybys that transition between these resonances in the circular restricted three-body problem without the need for two-body approximations.

three body problem↗
NASA NTRS2011

Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels

We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.

trajectory design↗
DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automotive Silicon Carbide Power Module Cooling With A Novel Modular Manifold And Embedded Heat Sink

The next generation of integrated power electronics packages will implement wide-bandgap devices with ultrahigh device heat fluxes. Although jet impingement has received attention for power electronics thermal management, it is not used in commercial electric vehicles (EVs) because of the associated pressure drop and reliability concerns. Here, in this paper, we present a modular thermal management system designed for automotive power electronics. The system achieves superior thermal performance to benchmarked EVs, while adhering to reliability standards and with low pumping power. The system utilizes a low-cost and lightweight plastic manifold to generate jets over an optimized heat sink, which is embedded in the direct-bonded-copper (DBC) substrate. The embedded heat sink concept leverages additive manufacturing to add elliptical pin fins to the DBC substrate. The heat sink geometry is optimized for submerged jet impingement using a unit-cell model and an exhaustive search algorithm. The model predictions are validated using unit-cell experiments. A full-scale power module model is then used to compare the DBC-embedded heat sink against direct DBC cooling and baseplate-integrated heat sinks for single-sided (SS) and double-sided (DS) cooling concepts. Using the SS and DS DBC-embedded cooling concepts, the models predict a thermal resistance that represents a reduction of 75% and 85% compared to the 2015 BMW i3, respectively, for the same water-ethylene glycol inverter flow rate. We have shown that an inverter with a 100-kilo-Watt-per-liter power density is achievable with the proposed design.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Sensor System and Observer Algorithm Co-Design For Modern Internal Combustion Engine Air Management Based on H2 Optimization

This paper outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on H 2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the H 2 norm of the observer error and the weighted l 1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited engine using exhaust gas circulation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best H 2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.

Zhang, Xu↗
NASA NTRS2019

Flat-H Redundant Frangible Joint Design Evolution 2018: Feasibility Study Conclusions

This paper reports results of an investigation into developing a single failure tolerant pyrotechnic linear separation system which features completely redundant explosive trains suitable for human spaceflight. It is a follow up to “Flat-H Redundant Frangible Joint Design Evolution 2017” and “Flat-H Redundant Frangible Joint Evolution”. The paper chronicles the history of the redundant frangible joint development program including testing, analysis, and design improvements from 2014 to the present culminating in a successful proof-of-concept prototype. The paper describes work done to address debris control and containment of combustion products. A performance optimization strategy is presented along with optimization results. Additionally a novel containment manifold design is presented with test results.

Structures↗
DOE OSTI2024

Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. Here we describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Trifluoromethylated Phenanthroline Ligands Reduce Excited-State Distortion in Homoleptic Copper(I) Complexes

Herein, we report the synthesis and excited-state dynamics for a series of homoleptic copper(I) trifluoromethylated phenanthroline complexes with two, three, and four trifluoromethyl functional groups. Our analysis of the steady-state absorbance and emission, transient-absorption spectroscopy, and electronic-structure-theory calculations results enable in-depth analysis of the pseudo-Jahn–Teller distortion inhibition from increased steric hindrance of the trifluoromethyl functional group relative to the prototypical dimethyl phenanthroline complex. Surprisingly, our results demonstrate that the greatest degree of pseudo-Jahn–Teller distortion inhibition is achieved with trifluoromethylation of only the 2 and 9 positions by an unusual combination of steric hindrance and stabilization of a nondistorted 1 MLCT manifold observed by transient kinetic lifetimes and optimized excited-state structures. The intersystem-crossing (ISC) lifetime for the 2,9-bis(trifluoromethyl)-1,10-phenanthroline Cu(I) complex is 69 ps, while the triplet excited-state lifetime and emission quantum yield are 106 ns and 4 × 10 –3 , respectively. Further trifluoromethylation of the phenanthroline yields a greater σ bond inductive withdrawing force on the phenanthroline nitrogens, ultimately resulting in weaker coordination to the copper. Last, the surprising success of the 2,9-bis(trifluoromethyl)-1,10-phenanthroline Cu(I) complex by adjusting both ligand sterics and electronic properties outlines a new strategy for developing long-lived Cu(I) charge-transfer complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

High Performance Database Management for Earth Sciences

The High Performance Database Research Center at Florida International University is completing the development of a highly parallel database system based on the semantic/object-oriented approach. This system provides exceptional usability and flexibility. It allows shorter application design and programming cycles and gives the user control via an intuitive information structure. It empowers the end-user to pose complex ad hoc decision support queries. Superior efficiency is provided through a high level of optimization, which is transparent to the user. Manifold reduction in storage size is allowed for many applications. This system allows for operability via internet browsers. The system will be used for the NASA Applications Center program to store remote sensing data, as well as for Earth Science applications.

Rishe, Naphtali↗
NASA NTRS1973

Sensitivity of optimal control systems with bang-bang control.

The effects of small parameter variations on the performance index of optimal control systems with initial and final target manifolds, free end time, and bang-bang control are analyzed in this paper. A new approach to the sensitivity equation is presented. This approach takes into account the pulse-shaped variation produced by the parameter change on the bang-bang control. An expression, that relates the variations of the performance index, the trajectory, the final time, and the parameter, is derived. This expression extends to the class of optimal systems with bang-bang control, a result previously obtained by Courtin and Rootenberg (1971).

Rootenberg, J.↗
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↗
NASA NTRS2016

Orion Exploration Mission Entry Interface Target Line

The Orion Multi-Purpose Crew Vehicle is required to return to the continental United States at any time during the month. In addition, it is required to provide a survivable entry from a wide range of trans-lunar abort trajectories. The Entry Interface (EI) state must be targeted to ensure that all requirements are met for all possible return scenarios, even in the event of no communication with the Mission Control Center to provide an updated EI target. The challenge then is to functionalize an EI state constraint manifold that can be used in the on-board targeting algorithm, as well as the ground-based trajectory optimization programs. This paper presents the techniques used to define the EI constraint manifold and to functionalize it as a set of polynomials in several dimensions.

Rea, Jeremy R.↗
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 NTRS2017

The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View

Farhi et al. recently proposed a class of quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA), for approximately solving combinatorial optimization problems. A level-p QAOA circuit consists of steps in which a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2p times for which these two Hamiltonians are applied are the parameters of the algorithm. As p increases, however, the parameter search space grows quickly. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here, we analytically and numerically study parameter setting for QAOA applied to MAXCUT. For level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MAXCUT, the Ring of Disagrees, or the 1D antiferromagnetic ring, we provide an analysis for arbitrarily high level. Using a Fermionic representation, the evolution of the system under QAOA translates into quantum optimal control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of QAOA for any p. It also greatly simplifies numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional sub-manifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

quantum algorithm↗