Fluid Dynamic Design Optimization of the Intake of a Small Turbojet

2006 ◽  
Author(s):  
Riccardo Amirante ◽  
Luciano A. Catalano ◽  
Andrea Dadone ◽  
Vito S. E. Daloiso ◽  
Dario Manodoro
Keyword(s):  
Design Optimization ◽  
Electricity Generation ◽  
Fluid Dynamic ◽  
Optimization Procedure ◽  
Black Box ◽  
High Load ◽  
Computational Time ◽  
Design Criteria ◽  
Dynamic Design ◽  
Gradient Based

This paper proposes an efficient gradient-based optimization procedure for black-box simulation codes and its application to the fluid-dynamic design optimization of the intake of a small-size turbojet, at high load and zero flight speed. Two simplified design criteria have been considered, which avoid to simulate the flow in any turbojet components other than the intake itself. Both design optimizations have been completed in a computational time corresponding to that required by eight flow analyses and have provided almost coincident optimal profiles for the intake. The flow fields computed with the original and the optimal profiles are compared to demonstrate the flow pattern improvements that can be theoretically achieved. Finally, the original and the optimal profiles have been mounted on the same small-size turbojet and experimentally tested, to assess the resulting improvements in terms of overall performances. All numerical and experimental results can be obviously extended to the intake of a microturbine for electricity generation.

scholarly journals Analysis of a Nondestructive Evaluation Technique for Defect Characterization in Magnetic Materials Using Local Magnetic Measurements

2010 ◽  
Vol 2010 ◽  
pp. 1-18 ◽  
Author(s):  
Stephane Durand ◽  
Ivan Cimrák ◽  
Peter Sergeant ◽  
Ahmed Abdallh
Keyword(s):  
Inverse Problem ◽  
Magnetic Measurements ◽  
Optimization Procedure ◽  
Computational Time ◽  
Defect Characterization ◽  
Proper Position ◽  
Sensitivity Equation ◽  
Small Perturbations ◽  
Gradient Based ◽  
Local Measurements

We study a method for nondestructive testing of laminated strips of a nonlinear magnetic material. Based on local measurements of the magnetic induction at the surface, we are able to reconstruct the proper position of defects inside the material, by solving an inverse problem. This inverse problem is solved by minimizing a suitable cost function using a gradient-based optimization procedure. Calculation of the gradient is done either by the standard method of small perturbations or by solving the sensitivity equation. The latter method yields a significant reduction of the computational time. The validity of the proposed algorithm is confirmed by experimental results.

scholarly journals Thermo-fluid dynamic design optimization of a concentric tube heat exchanger

2019 ◽  
Vol 14 (2) ◽  
pp. JFST0011-JFST0011 ◽  
Author(s):  
Tomohiro HIRANO ◽  
Mitsuo YOSHIMURA ◽  
Koji SHIMOYAMA ◽  
Atsuki KOMIYA
Keyword(s):  
Heat Exchanger ◽  
Design Optimization ◽  
Fluid Dynamic ◽  
Dynamic Design ◽  
Tube Heat Exchanger

scholarly journals pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles

Aerospace ◽  
2019 ◽  
Vol 6 (8) ◽  
pp. 87 ◽  
Author(s):  
Eric S. Hendricks ◽  
Justin S. Gray
Keyword(s):  
Design Optimization ◽  
Research Effort ◽  
Finite Difference Methods ◽  
Thermodynamic Cycle ◽  
Optimization Techniques ◽  
Computational Time ◽  
Propulsion Systems ◽  
Analysis Tools ◽  
Design Variables ◽  
Gradient Based

Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis tools. This need poses a challenge from a propulsion modeling standpoint because current state-of-the-art thermodynamic cycle analysis tools are not well suited to integration into vehicles level models or to the application of efficient gradient-based optimization techniques that help to counteract the increased computational costs. Therefore, the objective of this research effort was to investigate the development a new thermodynamic cycle analysis code, called pyCycle, to address this limitation and enable design optimization of these new vehicle concepts. This paper documents the development, verification, and application of this code to the design optimization of an advanced turbofan engine. The results of this study show that pyCycle models compute thermodynamic cycle data within 0.03% of an identical Numerical Propulsion System Simulation (NPSS) model. pyCycle also provides more accurate gradient information in three orders of magnitude less computational time by using analytic derivatives. The ability of pyCycle to accurately and efficiently provide this derivative information for gradient-based optimization was found to have a significant benefit on the overall optimization process with wall times at least seven times faster than using finite difference methods around existing tools. The results of this study demonstrate the value of using analytic derivatives for optimization of cycle models, and provide a strong justification for integrating derivatives into other important engineering analyses.

On Noise Control in Turbomachinery Using an Automated Multidisciplinary Design Optimization System

2005 ◽  
Author(s):  
Uyigue Idahosa ◽  
Vladimir Golubev
Keyword(s):  
Design Optimization ◽  
High Speed ◽  
High Efficiency ◽  
Optimization Procedure ◽  
General Aviation ◽  
Response Analysis ◽  
Efficiency Performance ◽  
Gradient Based ◽  
Geometry Parameterization ◽  
Turbomachinery Design

In this work, we review our recent efforts to develop and apply an expanding database of aerodynamic and aeroacoustic prediction technologies for exploring new conceptual designs of propulsion system turbomachinery components optimized for high-efficiency performance with minimum noise radiation. In this context, we first discuss construction of our automated, distributed, industry-like multi-disciplinary design optimization (MDO) environment used in all the studies. The system was developed on the basis of commercially available optimization modules, and involves a user-friendly interface that provides an easy link to user-supplied response analysis modules. We address various issues in the automated optimization procedure with focus on turbomachinery design, including proper geometry parameterization, algorithms selection, and transparent interconnections between different elements of the optimization process. In a benchmark study testing the performance of the system in application to aero/acoustic optimization, we consider a problem of optimal blade design to minimize fan noise, a dominant source of sound radiation both in high-speed fan applications (such as high-bypass-ratio turbofans, propellers of turboprop and IC engines in general aviation, and helicopter rotors) and low-speed ones (including applications in automotive, computer, air-conditioning and other industries). Two approaches are investigated, with the first relying on commercial CFD software coupled with an unstructured mesh generator, and the second employing a panel-based aerodynamic code integrated with an integral acoustic solver. Success of various optimization algorithms (including gradient-based and evolutionary) in finding global minima of the objective function for a noise metric in both unconstrained and constrained optimization processes is examined.

Fluid-dynamic design optimization of hydraulic proportional directional valves

2013 ◽  
Vol 46 (10) ◽  
pp. 1295-1314 ◽  
Author(s):  
Riccardo Amirante ◽  
Luciano Andrea Catalano ◽  
Carlo Poloni ◽  
Paolo Tamburrano
Keyword(s):  
Design Optimization ◽  
Fluid Dynamic ◽  
Dynamic Design

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

scholarly journals Parameterization, Modeling, and Validation in Real Conditions of an External Gear Pump

2021 ◽  
Vol 13 (6) ◽  
pp. 3089
Author(s):  
Miquel Torrent ◽  
Pedro Javier Gamez-Montero ◽  
Esteban Codina
Keyword(s):  
Working Conditions ◽  
Test Bench ◽  
Fluid Dynamic ◽  
Graph Model ◽  
Field Tests ◽  
Black Box ◽  
Gear Pump ◽  
Volumetric Behavior ◽  
External Gear Pump ◽  
Standard Tests

This article presents a methodology for predicting the fluid dynamic behavior of a gear pump over its operating range. Complete pump parameterization was carried out through standard tests, and these parameters were used to create a bond graph model to simulate the behavior of the unit. This model was experimentally validated under working conditions in field tests. To carry this out, the pump was used to drive the auxiliary movements of a drilling machine, and the experimental data were compared with a simulation of the volumetric behavior under the same conditions. This paper aims to describe a method for characterizing any hydrostatic pump as a “black box” model predicting its behavior in any operating condition. The novelty of this method is based on the correspondence between the variation of the parameters and the internal changes of the unit when working in real conditions, that is, outside a test bench.

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