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Results for “Design Space Exploration”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
NASA NTRS2018

Design Space Exploration of Pericyclic Transmission

The pericyclic transmission provides the opportunity to vastly impact transmission design in rotorcraft due to its ability to provide exceedingly high reduction ratios in a single stage that would normally require multiple gear stages to produce. This could lead to lighter transmissions with fewer components, increased range, reliability, efficiency, speed and decreased cost to maintain. While many previous studies have focused upon the gearing within the pericyclic transmission, this work focused on what influences pericyclic geometry, and how changes in geometry impacts bearing loads. Specifically the loading of bearings that must deliver power from the input shaft to the nutating and rotating gears of the system were of primary concern. A comprehensive look at dynamic loads generated by nutating bodies was performed. Methods to address these dynamic loads via application of counterbalances and deviation from conventional pericyclic transmission designs were utilized to negate the dynamic moment of concern. Finally a static solver was used to determine the bearing loads with updated component geometries and mass moment of inertias that included the required counterbalances.

Nutational Gearing↗
DOE OSTI2014

Classifier-guided sampling for discrete variable, discontinuous design space exploration: Convergence and computational performance

A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodeling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates nondifferentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill-suited for conventional metamodeling techniques and too computationally expensive to be solved by population-based algorithms alone. In addition, the rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.

42 ENGINEERING↗
DOE OSTI2022

Design space exploration and decision‐making for a segmented ultralight morphing 50‐MW wind turbine

Abstract Wind turbine design encompasses many different aspects including aerodynamic, structural, electrical, and control system design. To achieve optimal plant performance, a system design approach is utilized in which the performance of the whole wind turbine is evaluated and quantified during operational scenarios with subsystem interactions. In this paper, the design for a Segmented Ultralight Morphing Rotor (SUMR) 50‐MW wind turbine is presented utilizing levelized cost of energy (LCOE) for design choices, with additional quantification of simulated performance shortcomings at the 50‐MW scale. The multi‐disciplinary design process results in a final ultra‐scale turbine configuration that outperforms other existing offshore wind farms regarding the LCOE.

Kianbakht, Sepideh↗
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 NTRS2018

Design Space Exploration of Pericyclic Transmission with Counterbalance and Bearing Load Analysis

The pericyclic transmission provides the opportunity to vastly impact transmission design in rotorcraft due to its ability to provide exceedingly high reduction ratios in a single stage that would normally require multiple gear stages to produce. This could lead to lighter transmissions with fewer components, increased range, reliability, efficiency, speed and decreased cost to maintain. While many previous studies have focused upon the gearing within the pericyclic transmission, this work focused on what influences pericyclic geometry, and how changes in geometry impacts bearing loads. Specifically the loading of bearings that must deliver power from the input shaft to the nutating and rotating gears of the system were of primary concern. A comprehensive look at dynamic loads generated by nutating bodies was performed. Methods to address these dynamic loads via application of counterbalances and deviation from conventional pericyclic transmission designs were utilized to negate the dynamic moment of concern. Finally a static solver was used to determine the bearing loads with updated component geometries and mass moment of inertias that included the required counterbalances.

Pericyclic↗
NASA NTRS2018

Design Space Exploration of Pericyclic Transmission with Counterbalance and Bearing Load Analysis

The pericyclic transmission provides the opportunity to vastly impact transmission design in rotorcraft due to its ability to provide exceedingly high reduction ratios in a single stage that would normally require multiple gear stages to produce. This could lead to lighter transmissions with fewer components, increased range, reliability, efficiency, speed and decreased cost to maintain. While many previous studies have focused upon the gearing within the pericyclic transmission, this work focused on what influences pericyclic geometry, and how changes in geometry impacts bearing loads. Specifically the loading of bearings that must deliver power from the input shaft to the nutating and rotating gears of the system were of primary concern. A comprehensive look at dynamic loads generated by nutating bodies was performed. Methods to address these dynamic loads via application of counterbalances and deviation from conventional pericyclic transmission designs were utilized to negate the dynamic moment of concern. Finally a static solver was used to determine the bearing loads with updated component geometries and mass moment of inertias that included the required counterbalances.

Pericyclic↗
DOE OSTI2023

Design Space Exploration for Novel Reduced-Vortex Turbine Rotors Using Free Vortex Wake Methods

In this work, we explore the use of mid-fidelity free vortex wake (FVW) tools, namely NREL's cOnvecting LAgrangian Filaments (OLAF) tool, to design and analyze wind turbine rotor blades. The goal of the exercise is to determine whether designs exist that out-perform traditional blade designs, which are optimized using the Blade-Element Momentum (BEM) method. We find that using design of experiments to generate simple modifications of the blade tip, OLAF predicts higher performance in terms of power coefficient for redesigned blades that are non-optimal according to BEM. We then use optimization methods with OLAF in the loop to attempt to automatically find OLAF-optimal designs. We analyze the results of these optimizations, demonstrating that some roadblocks exist before OLAF and FVW tools can be used to automatically find FVW-optimal blade designs that might exceed the performance of BEM-optimal blade designs.

blade design↗
DOE OSTI2021

Design Space Exploration of Ferroelectric Tunnel Junction Toward Crossbar Memories

We perform a simulation-based analysis on the potential of emerging ferroelectric tunnel junctions (FTJs) as a memory device for crossbar arrays. Though FTJs are promising due to their low power switching characteristics compared to other emerging technologies, the greatest challenge for FTJs is the tradeoff between integration density and read performance. Our analysis highlights the need to co-optimize the ferroelectric thickness of the FTJ and read/write voltages to achieve proper functionality at large array sizes. Our analysis shows that FTJ-based crossbar achieves 93% higher sense margin at isoread power of 116 nW (per bit), but this FTJ design comes at a cost of 9.28× higher write power at isowrite time of 250 ns. In response, we study the potential tradeoffs of design points outside the feasible region to understand what device characteristics are desired to overcome such challenges.

Jao, Nicholas↗