Multi Design Point Cycle Design Incorporation into the Environmental Design Space

2012 ◽  
Author(s):  
Jeff Schutte ◽  
Jimmy Tai ◽  
Dimitri Mavris
Keyword(s):  
Design Space ◽  
Environmental Design ◽  
Design Point

The Environmental Design Space: Modeling and Performance Updates

2021 ◽  
Author(s):  
Luis Salas Nunez ◽  
Jimmy C. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Design Space ◽  
Environmental Design ◽  
Space Modeling ◽  
And Performance

Cycle Design Exploration Using Multi-Design Point Approach

2012 ◽  
Author(s):  
Jeffrey Schutte ◽  
Jimmy Tai ◽  
Jonathan Sands ◽  
Dimitri Mavris
Keyword(s):  
Gas Turbine ◽  
Design Space ◽  
Traditional Approach ◽  
Gas Turbine Engine ◽  
Operating Conditions ◽  
Feasible Region ◽  
Turbine Engine ◽  
Design Point ◽  
Performance Requirements ◽  
Design Cycle

The focus of this study is to compare the aerothermodynamic cycle design space of a gas turbine engine generated using two on-design approaches. The traditional approach uses a single design point (SDP) for on-design cycle analysis, where off-design cycle analysis must be performed at other operating conditions of interest. A multi-design point (MDP) method performs on-design cycle analysis at all operating conditions where performance requirements are specified. Effects on the topography of the cycle design space as well as the feasibility of the space are examined. The impacts that performance requirements and cycle assumptions have on the bounds and topography of the feasible space are investigated. The deficiencies of a SDP method in determining an optimum gas turbine engine will be shown for a given set of requirements. Analysis will demonstrate that the MDP method, unlike the SDP method, always obtains a properly sized engine for a set of given requirements and cycle design variables, resulting in an increased feasible region of the aerothermodynamic cycle design space from which the optimum performance engine can be obtained.

Optimization and Uncertainty Analysis of a Diesel Engine Operating Point Using CFD

2016 ◽  
Author(s):  
Daniel M. Probst ◽  
Peter K. Senecal ◽  
Peter Z. Qian ◽  
Max X. Xu ◽  
Brian P. Leyde
Keyword(s):  
Diesel Engine ◽  
Optimal Design ◽  
Analytical Model ◽  
Design Space ◽  
Uncertainty Propagation ◽  
Operating Point ◽  
Included Angle ◽  
Design Point ◽  
Injection Duration ◽  
Engine Operating Point

This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a Diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. Three engine parameters were varied around a baseline design to minimize indicated specific fuel consumption (ISFC) without exceeding emissions (NOx and soot) or peak cylinder pressure constraints. An objective merit function was constructed to quantify the strength of designs. The engine parameters were start of injection (SOI), injection duration, and injector included angle. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. Each emulator was used to direct the placement of the next set of DOE points such that they improve the accuracy of the subsequently generated emulator. This refinement was either across the entire design space or a reduced design space that was likely to contain the optimal design point. After sufficient emulator accuracy was achieved, the optimal design point was predicted. A total of 5 sequential DOEs were completed, for a total of 232 simulations. A reduced design region was predicted after the second DOE that reduced the volume of the design space by 96.8%. The final predicted optimum was found to exist in this reduced design region. The sequential DOE optimization was compared to an optimization performed using a GA. The GA was completed using a population of 9 and was run for 71 generations. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. A sensitivity analysis of the design space around the predicted optimum showed that injection duration had the strongest effect on predicted results, while the included angle had the weakest. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. The uncertainty propagation results demonstrated that for the relatively large variations in the input parameters, the expected variation in the ISFC and NOx results were significant. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

Robustness in Optimum Design of Freeform Mechanical Parts

2000 ◽  
Author(s):  
Dulyachot Cholaseuk ◽  
Vijay Srinivasan ◽  
Vijay Modi
Keyword(s):  
Optimum Design ◽  
Design Of Experiment ◽  
Design Space ◽  
Operating Conditions ◽  
Free Form ◽  
Design Point ◽  
Mechanical Parts ◽  
Iterative Design ◽  
Robust Designs ◽  
Successive Iteration

Abstract A method to identify robust designs of mechanical parts with free-form shapes is proposed. For each design, the geometry and operating conditions represent one design point in the design space, with noise altering the design point leading to a change in performance. A shape optimization process is conducted for each example problem. Each successive iteration during the process produces an iterative design point with the final one being the optimum design. Once the process is completed, a design of experiment approach is used to apply noise in order to generate samples around each and every iterative design point. Then a simple statistical method is utilized to analyze the samples in order to evaluate the robustness of each iterative design. The results show that an optimum design is not necessarily robust.

Effects of Advanced Engine Technology on Open Rotor Cycle Selection and Performance

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Christopher A. Perullo ◽  
Jimmy C. M. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Design Space ◽  
Environmental Design ◽  
Sharp Decrease ◽  
Multidisciplinary Optimization ◽  
Fuel Prices ◽  
Fuel Burn ◽  
Open Rotor ◽  
Institute Of Technology ◽  
Bypass Ratio ◽  
Rotor Model

Recent increases in fuel prices and increased focus on aviation's environmental impacts have reignited focus on the open rotor engine concept. This type of architecture was extensively investigated in previous decades but was not pursued through to commercialization due to relatively high noise levels and a sudden, sharp decrease in fuel prices. More recent increases in fuel prices and increased government pressure from taxing carbon-dioxide production mean the open rotor is once again being investigated as a viable concept. Advances in aero-acoustic design tools have allowed industry and academia to re-investigate the open rotor with an increased emphasis on noise reduction while retaining the fuel burn benefits due to the increased propulsive efficiency. Recent research with conceptual level multidisciplinary considerations of the open rotor has been performed (Bellocq et al., 2010, “Advanced Open Rotor Performance Modeling For Multidisciplinary Optimization Assessments,” Paper No. GT2010-2963), but there remains a need for a holistic approach that includes the coupled effects of the engine and airframe on fuel burn, emissions, and noise. Years of research at Georgia Institute of Technology have led to the development of the Environmental Design Space (EDS) (Kirby and Mavris, 2008, “The Environmental Design Space,” Proceedings of the 26th International Congress of the Aeronautical Sciences). EDS serves to capture interdependencies at the conceptual design level of fuel burn, emissions, and noise for conventional and advanced engine and airframe architectures. Recently, leveraging NASA environmentally responsible aviation (ERA) modeling efforts, EDS has been updated to include an open rotor model to capture, in an integrated fashion, the effects of an open rotor on conventional airframe designs. Due to the object oriented nature of EDS, the focus has been on designing modular elements that can be updated as research progresses. A power management scheme has also been developed with the future capability to trade between fuel efficiency and noise using the variable pitch propeller system. Since the original GE open rotor test was performed using a military core, there is interest in seeing the effect of modern core-engine technology on the integrated open rotor performance. This research applies the modular EDS open rotor model in an engine cycle study to investigate the sensitivity of thermal efficiency improvements on open rotor performance, including the effects on weight and vehicle performance. The results are that advances in the core cycle are necessary to enable future bypass ratio growth and the trades between core operating temperatures and size become more significant as bypass ratio continues to increase. A general benefit of a 30% reduction in block fuel is seen on a 737-800 sized aircraft.

An Optimization Method for Centrifugal Compressor Design Using the Surrogate Management Framework

2011 ◽  
Author(s):  
Kyoung Ku Ha ◽  
Shin Hyoung Kang
Keyword(s):  
Optimal Design ◽  
Centrifugal Compressor ◽  
Design Space ◽  
Optimization Method ◽  
Pattern Search ◽  
Flow Coefficient ◽  
Design Parameters ◽  
Centrifugal Compressors ◽  
Management Framework ◽  
Design Point

A variety of centrifugal compressors are used in various fields of industry these days. The design requirements are more complicated, and it is difficult to determine the optimal design point of a centrifugal compressor. The aim of this study was to propose an efficient optimization method for centrifugal compressors considering the impeller, the vaneless diffuser, and the overhung type volute. The optimization was performed using the surrogate management framework (SMF). The design parameters were the impeller exit radius, the exit blade angle, and the flow coefficient. Sample points in the design space were selected according to the Design of Experiments (DoE) theory. The CFD simulations were executed on the impeller and the diffuser at every sampled point. The volutes were described using a one-dimensional but reliable theory to reduce the simulation time. An approximation model based on the Kriging method was constructed using this dataset. Then, an optimal design point that minimized the objective function was determined in a substitute design space using the pattern search method because of its efficiency and rigorous convergence. The optimization process, underlying methods, and results are described in this paper.

An Effective Approach to Solve Design Optimization Problems With Arbitrarily Distributed Uncertainties in the Original Design Space Using Ensemble of Gaussian Reliability Analyses

2016 ◽  
Vol 138 (7) ◽  
Author(s):  
Po Ting Lin ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Reliability Index ◽  
Design Space ◽  
Optimization Problems ◽  
Performance Measure ◽  
Probabilistic Constraints ◽  
Original Design ◽  
Design Point ◽  
Standard Normal

Reliability-based design optimization (RBDO) algorithms have been developed to solve design optimization problems with existence of uncertainties. Traditionally, the original random design space is transformed to the standard normal design space, where the reliability index can be measured in a standardized unit. In the standard normal design space, the modified reliability index approach (MRIA) measured the minimum distance from the design point to the failure region to represent the reliability index; on the other hand, the performance measure approach (PMA) performed inverse reliability analysis to evaluate the target function performance in a distance of reliability index away from the design point. MRIA was able to provide stable and accurate reliability analysis while PMA showed greater efficiency and was widely used in various engineering applications. However, the existing methods cannot properly perform reliability analysis in the standard normal design space if the transformation to the standard normal space does not exist or is difficult to determine. To this end, a new algorithm, ensemble of Gaussian reliability analyses (EoGRA), was developed to estimate the failure probability using Gaussian-based kernel density estimation (KDE) in the original design space. The probabilistic constraints were formulated based on each kernel reliability analysis for the optimization processes. This paper proposed an efficient way to estimate the constraint gradient and linearly approximate the probabilistic constraints with fewer function evaluations (FEs). Some numerical examples with various random distributions are studied to investigate the numerical performances of the proposed method. The results showed that EoGRA is capable of finding correct solutions in some problems that cannot be solved by traditional methods. Furthermore, experiments of image processing with arbitrarily distributed photo pixels are performed. The lighting of image pixels is maximized subject to the acceptable limit. Our implementation showed that the accuracy of the estimation of normal distribution is poor while the proposed method is capable of finding the optimal solution with acceptable accuracy.

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