Search NASA⌕ Search

SEARCH · Search NASA

Results for “global 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Shape Optimization of Supersonic Turbines Using Response Surface and Neural Network Methods

Turbine performance directly affects engine specific impulse, thrust-to-weight ratio, and cost in a rocket propulsion system. A global optimization framework combining the radial basis neural network (RBNN) and the polynomial-based response surface method (RSM) is constructed for shape optimization of a supersonic turbine. Based on the optimized preliminary design, shape optimization is performed for the first vane and blade of a 2-stage supersonic turbine, involving O(10) design variables. The design of experiment approach is adopted to reduce the data size needed by the optimization task. It is demonstrated that a major merit of the global optimization approach is that it enables one to adaptively revise the design space to perform multiple optimization cycles. This benefit is realized when an optimal design approaches the boundary of a pre-defined design space. Furthermore, by inspecting the influence of each design variable, one can also gain insight into the existence of multiple design choices and select the optimum design based on other factors such as stress and materials considerations.

Papila, Nilay↗
NASA NTRS1991

A mathematical basis for the design and design optimization of adaptive trusses in precision control

A mathematical basis for the optimal design of adaptive trusses to be used in supporting precision equipment is provided. The general theory of adaptive structures is introduced, and the global optimization problem of placing a limited number, q, of actuators, so as to maximally achieve precision control and provide prestress, is stated. Two serialized optimization problems, namely, optimal actuator placement for prestress and optimal actuator placement for precision control, are addressed. In the case of prestressing, the computation of a 'desired' prestress is discussed, the interaction between actuators and redundants in conveying the prestress is shown in its mathematical form, and a methodology for arriving at the optimal placement of actuators and additional redundants is discussed. With regard to precision control, an optimal placement scheme (for q actuators) for maximum 'authority' over the precision points is suggested. The results of the two serialized optimization problems are combined to give a suboptimal solution to the global optimization problem. A method for improving this suboptimal actuator placement scheme by iteration is presented.

Das, S. K.↗
NASA NTRS2001

Accuracy Assessment of Response Surface Approximations for Supersonic Turbine Design

There is a growing trend to employ CFD tools to supply the necessary information for design optimization of fluid dynamics components/systems. Such results are prone to uncertainties due to reasons including discretization. errors, incomplete convergence of computational procedures, and errors associated with physical models such as turbulence closures. Based on this type of information, gradient-based optimization algorithms often suffer from the noisy calculations, which can seriously compromise the outcome. Similar problems arise from the experimental measurements. Global optimization techniques, such as those based on the response surface (RS) concept are becoming popular in part because they can overcome some of these barriers. However, there are also fundamental issues related to such global optimization technique such as RS. For example, in high dimensional design spaces, typically only a small number of function evaluations are available due to computational and experimental costs. On the other hand, complex features of the design variables do not allow one to model the global characteristics of the design space with simple quadratic polynomials. Consequently a main challenge is to reduce the size of the region where we fit the RS, or make it more accurate in the regions where the optimum is likely to reside. Response Surface techniques using either polynomials or and Neural Network (NN) methods offer designers alternatives to conduct design optimization. The RS technique employs statistical and numerical techniques to establish the relationship between design variables and objective/constraint functions, typically using polynomials. In this study, we aim at addressing issues related to the following questions: (1) How to identify outliers associated with a given RS representation and improve the RS model via appropriate treatments? (2) How to focus on selected design data so that RS can give better performance in regions critical to design optimization? (3) How to combine NN and polynomial techniques for improving the accuracy of the RS model?

Papila, Nilay↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS2004

The International Global Network of Geodetic Fiducial Stations

Scientific need and technological opportunity require that we move toward implementing a global network of geodetic fiducial stations which feature co-located SLR, VLBI, GNSS, and DORIS instrumentation. Earth science of the next decade will require more accurate global change measurements of sea level topography, sea level change, polar ice mass balance, hydrological and atmospheric mass flux. and topographic deformation, real time mm scale navigation and precision time transfer on a global scale. These scientific requirements have been translated into a goal of mm scale annual stability for the terrestrial reference frame, earth orientation parameters, as well as the orbit and clock determinations tbr the GNSS systems. To meet these challenges, the four geodetic observing systems must be more tightly integrated in technology, location, and analysis. NASA strongly supports the objectives of the IGGOS initiative vis NASA's National Geodetic Observatory and INDIGO programs. The Global networks of GNSS, SLR. and VLBI observatories are for the most part poorly suited for these new demands. These important geodetic networks have evolved with little planning yet these systems are providing essential measurements to a wide swath of society. New signal structures in the GPS and the developing Galileo GNSS will soon require replacement of the GNSS receivers. The SLR network is poorly distributed globally, requires labor intensive observations and analysis, and for the most part relies upon antiquated technology. The VLBI observatories utilize large radio telescopes in remote regions that are poorly distributed globally. Co-location of these networks is sparse and co-location errors contribute significantly to the observing error spectrum. Increasing use of the S and X band by commercial and other government services will also contribute to increased observational errors. The time is upon us for an international effort to develop an optimized global geodetic fiducial network of twelve or more integrated automated geodetic observatories that will provide in near real time high density mea.surements of Earth orientation, geodetic positioning, and GNSS system monitoring. The strategic goal for the optimized global fiducial network should be efficient autonomous operation, real time data streaming to analysis centers with an integrated near real time analysis capability for all four observing systems. These goals are achievable in the next decade given the dramatic technological improvements in all geodetic systems. For VLBI, the BKG TIGO might be viewed as a preliminary prototype of technology for fundamental stations. Mark 5, PC-VSI. e-VLBI broadband development efforts are laying the groundwork to reduce the aperture of the VLBI antennas and to allow for precision mm level co-location of observing systems. Operating costs and enhanced accuracy would accrue from the elimination of hydrogen maser clocks using double differencing, GNSS time transfer technology, GHz broadband recording, real time analysis, and mm wavelength VLBI observations. A global geodetic fiducial network will not be realizable without international efforts and the support of the four services.

LaBrecque, John↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
NASA NTRS2010

Establishing a Modern Ground Network for Space Geodesy Applications

Ground-based networks of co-located space-geodesy techniques (VLBI, SLR, GLASS, DORIS) are the basis for the development and maintenance of the :International Terrestrial deference Frame (ITRE), which is the basis for our metric measurements of global change. The Global Geodetic Observing System (GGOS) within the International Association of Geodesy has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at I mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence. As a first step, simulations focused on establishing the optimal global SLR and VLBI network, since these two techniques alone are sufficient to define the reference frame. The GLASS constellations will then distribute the reference frame to users anywhere on the Earth. Using simulated data to be collected by the future networks, we investigated various designs and the resulting accuracy in the origin, scale and orientation of the resulting ITRF. We present here the results of extensive simulation studies aimed at designing optimal global geodetic networks to support GGOS science products. Current estimates are the network will require 24 - 32 globally distributed co-location sites. Stations in the near global network will require geologically stable sites witla good weather, established infrastructure, and local support and personnel. EGOS will seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to take part in the network implementation and operation_ Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in designing a co-location station.

Pearlman, M.↗
NASA NTRS1991

On a global aerodynamic optimization of a civil transport aircraft

An aerodynamic optimization procedure developed to minimize the drag to lift ratio of an aircraft configuration: wing - body - tail, in accordance with engineering restrictions, is described. An algorithm developed to search a hypersurface with 18 dimensions, which define an aircraft configuration, is discussed. The results, when considered from the aerodynamic point of view, indicate the optimal configuration is one that combines a lifting fuselage with a canard.

Savu, G.↗
NASA NTRS2001

Design Space Exploration for MDO on a Teraflop Computer

The objective of this work was to develop global optimization techniques suitable for identifying promising regions in high dimensional aircraft configuration design space. Different optimization methods have been examined to evaluation their ability to efficiently search the design space to locate promising regions. Multistart local optimization methods as well as global search techniques were examined and modified to compare their performance on high dimensional design space.

Haftka, Raphael↗
NASA NTRS2003

Tractable Pareto Optimization of Temporal Preferences

This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.

Morris, Robert↗
NASA NTRS2017

Computationally Efficient Motion Planning Algorithms for Agile Autonomous Vehicles in Cluttered Environments

Fast, real-time motion planning of an agile, autonomous vehicle in a cluttered environment, with many geometrically-fixed obstacles, is a very complex problem, especially because of the vehicle dynamics constraints and resource constrained computational capabilities onboard the vehicle. In this paper, we present computationally-efficient versions of our novel motion planning algorithm called the Spherical Expansion and Sequential Convex Programming (SE–SCP) algorithm. The SE–SCP algorithm first uses a spherical-expansion-based randomized sampling algorithm to explore the workspace. Oncea path is found from the start position to the goal position, the algorithm computes a locally optimal trajectory, within its homotopy class for a desired cost function, by solving a sequence of convex optimization problems. Thus, the SE–SCP algorithm is anytime locally optimal and the trajectory is globally optimal if the number of samples tends to infinity. In this paper, we further enhance the computational efficiency of the SE–SCP algorithm using uni-directional and bi-directional rewiring techniques. We also present a detailed proof of the local optimality characteristics of the new SE–SCP algorithms for aspecial case of vehicle dynamics. Simulation examples involving quadrotor and spacecraft help demonstrate the effectiveness of our new algorithms.

Bandyopadhyay, Saptarshi↗
NASA NTRS2018

An Active Subspace Method for Accelerating Convergence in Delaunay-Based Optimization via Dimension Reduction

Delaunay-based derivative-free optimization, ∆DOGS, is an efficient and provably-convergent global optimization method for the problems which has computationally expensive objection function and the analytical expression for the objective function is not available. ∆-DOGS is a novel optimization scheme in the family of response surface methods (RSMs); however, it suffers from the curse of dimensionality since the computational cost increases dramatically as the number of design parameters increases. As a result, the number of design parameters in ∆-DOGS algorithm is relatively low (n.10). To avoid such problems, this paper proposes a combination of derivative-free optimization, seeking the global minimizer of an expensive and nonconvex objective function f(x) and active subspace method, detecting the directions of the most variability using evaluations of the gradient. The contribution of other directions to the objective function is bounded by a sufficiently small constant. This new algorithm iteratively applied Delaunay-based derivative-free optimization to seek the minimizer on the d-dimensional active subspace that has most function variation. Inverse mapping is needed to project data from active subspace to full-model for evaluating function values. This task is overcome by solving an inequality constrained problem that curves the response surface of the objective function. The test results show that this strategy is effective on a handful of optimization problems.

Bewley, Thomas R.↗
DOE OSTI2026

Relaxations of the steady optimal gas flow problem for a non-Ideal gas

Natural gas ranks second in U.S. primary energy consumption. Because most production sites are remote, gas must be transported through pipeline networks equipped with compressors, valves, and other components. For both economic efficiency and system reliability, it is desirable to operate these networks optimally. The governing physics across pipeline components entails nonlinear, non-convex equality and inequality constraints, and the most general steady-flow operations problem is a Mixed-Integer Nonlinear Program (MINLP).This work focuses on one such steady-flow problem-the Optimal Gas Flow (OGF) for a natural gas pipeline network-which minimizes production cost subject to the steady-flow physics. For day-to-day operations, the ability to quickly compute a globally optimal solution and a strong lower bound for varying demand profiles is crucial. A promising strategy is to build tight relaxations of the OGF’s nonlinear constraints. However, many nonlinearities arising from non-ideal equations of state either lack relaxations or have relaxations that do not scale to realistic network sizes. We address this gap by combining recent advances in polyhedral relaxations for univariate functions to construct tight, computationally efficient relaxations of the OGF with a non-ideal equation of state. These relaxations solve within seconds on a standard laptop. In conclusion, we demonstrate their quality through extensive numerical experiments on very large-scale test networks from the literature and find that the proposed approach proves optimality in 92% of tested instances.

03 NATURAL GAS↗
NASA NTRS2008

A New Maneuver for Escape Trajectories

This presentation put forth a new maneuver for escape trajectories and specifically sought to find an analytical approximation for medium thrust trajectories. In most low thrust derivations the idea is that escape velocity is best achieved by accelerating along the velocity vector. The reason for this is that change in specific orbital energy is a function of velocity and acceleration. However, Levin (1952) suggested that while this is a locally optimal solution it might not be a globally optimal one. Turning acceleration inward would drop periapse giving a higher velocity later in the trajectory. Acceleration at that point would be dotted against a higher magnitude V giving a greater rate of change of mechanical energy. The author then hypothesized that decelerating from the initial orbit and then accelerating at periapse would not lead to a gain in greater specific orbital energy--however, the hypothesis was incorrect. After considerable derivation it was determined that this new maneuver outperforms a direct burn when the overall DeltaV budget exceeds the initial orbital velocity (the author has termed this the Heinlein maneuver). The author provides a physical explanation for this maneuver and presents optimization analyses.

Adams, Robert B.↗
DOE OSTI2025

Off-Equilibrium Reactivity of Boron-Enriched Metal Diboride Surfaces in Electroreduction Conditions

Boron-based materials, featuring B-dependent reactivity and diverse phases, are emerging as promising catalyst systems. However, the catalytic mechanism on many borides remains poorly understood due to complex surface reconstructions under reaction conditions. Here, we investigate the MoB 2 surface in conditions of hydrogen evolution reaction in acidic media, using grand canonical global optimization, grand canonical density functional theory, ab initio molecular dynamics, free energy surface sampling, and an analytical model for electrochemical barrier evaluation. We propose a boron-enrichment strategy to tune the surface reactivity of the hexagonal face of MoB 2 . We reveal the dynamic nature of the B-enriched surface under H coverage and kinetic trapping of the system in the metastable regime with an extensive examination of the deactivation pathways. The metastable center B site on B-enriched surfaces, featuring buckled-up configuration and a usual relaxation effect, is found to be highly active toward HER via the Volmer–Heyrovsky mechanism. In conclusion, this work demonstrates how off-equilibrium behaviors can arise from the interplay between adsorbate coverage and surface reconstruction on a seemingly simple surface, and we present a theoretical framework and computational workflows to address these behaviors, along with other realistic complexities, in kinetics simulations.

Adsorption↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗