scholarly journals Computational Design Optimization for S-Ducts

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 36 ◽  
Author(s):  
Alessio D’Ambros ◽  
Timoleon Kipouros ◽  
Pavlos Zachos ◽  
Mark Savill ◽  
Ernesto Benini
Keyword(s):  
Design Space ◽  
Computational Design ◽  
Free Form ◽  
Design Parameters ◽  
Heuristic Optimization ◽  
Objective Functions ◽  
Pressure Losses ◽  
Optimization Study ◽  
Free Form Deformation ◽  
Computational Fluid Dynamics Cfd

In this work, we investigate the computational design of a typical S-Duct that is found in the literature. We model the design problem as a shape optimization study. The design parameters describe the 3D geometrical changes to the shape of the S-Duct and we assess the improvements to the aerodynamic behavior by considering two objective functions: the pressure losses and the swirl. The geometry management is controlled with the Free-Form Deformation (FFD) technique, the analysis of the flow is performed using steady-state computational fluid dynamics (CFD), and the exploration of the design space is achieved using the heuristic optimization algorithm Tabu Search (MOTS). The results reveal potential improvements by 14% with respect to the pressure losses and by 71% with respect to the swirl of the flow. These findings exceed by a large margin the optimality level that was achieved by other approaches in the literature. Further investigation of a range of optimum geometries is performed and reported with a detailed discussion.

scholarly journals Computational design of an automotive twist beam

2016 ◽  
Vol 3 (3) ◽  
pp. 215-225 ◽  
Author(s):  
Benki Aalae ◽  
Habbal Abderrahmane ◽  
Mathis Gael
Keyword(s):  
Pareto Front ◽  
Computational Design ◽  
Free Form ◽  
Linear Elastic ◽  
Free Form Deformation ◽  
Automotive Part ◽  
Normal Boundary ◽  
3D Shapes ◽  
Industrial Problem ◽  
Multicriteria Optimization Problem

Abstract In recent years, the automotive industry has known a remarkable development in order to satisfy the customer requirements. In this paper, we will study one of the components of the automotive which is the twist beam. The study is focused on the multicriteria design of the automotive twist beam undergoing linear elastic deformation (Hooke's law). Indeed, for the design of this automotive part, there are some criteria to be considered as the rigidity (stiffness) and the resistance to fatigue. Those two criteria are known to be conflicting, therefore, our aim is to identify the Pareto front of this problem. To do this, we used a Normal Boundary Intersection (NBI) algorithm coupling with a radial basis function (RBF) metamodel in order to reduce the high calculation time needed for solving the multicriteria design problem. Otherwise, we used the free form deformation (FFD) technique for the generation of the 3D shapes of the automotive part studied during the optimization process. Highlights We model the automotive twist beam. We developed an algorithm to solve multicriteria optimization problem. We solve our industrial problem with our developed algorithm. New profiles of the twist beam are proposed.

Adjoint-Based Optimization of a Modern Jet-Engine Fan Blade

2020 ◽  
Author(s):  
Andrea Giugno ◽  
Shahrokh Shahpar ◽  
Alberto Traverso
Keyword(s):  
Design Space ◽  
Characteristic Curve ◽  
Free Form ◽  
Design Parameters ◽  
Jet Engine ◽  
Discrete Adjoint ◽  
Active Design ◽  
Fan Blade ◽  
Adjoint Gradient ◽  
Real Test

Abstract A Multi-point Approximation Method (MAM) coupled with adjoint is presented to increase the efficiency of a modern jet-engine fan blade. The study performed makes use of Rolls-Royce in-house suite of codes and its discrete adjoint capability. The adjoint gradient is used along with MAM to create a Design Of Experiment to enhance the optimization process. A generalized Free-Form Deformation (FFD) technique is used to parametrize the geometry, creating a design space of 180 parameters. The resulting optimum blade at design conditions is then evaluated at off-design conditions to produce the characteristic curve, which is compared with real test data. Finally, a preliminary Active Design Subspace (ADS) representing the fan efficiency is created to evaluate the robustness of the objective function in respect to the most significant design parameters. The ADS allows to collapse a large design space of the order of hundreds parameters to the few most important variables, measuring their contribution. This map is valuable in many respects to the fan designers and manufacture engineers to identify any ridges where the performance may deteriorate rapidly, hence a more robust part of the design space can easily be visualized and identified.

Aerodynamic Design Optimization of an Axial Flow Compressor Stator Using Parameterized Free-Form Deformation

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Richard Amankwa Adjei ◽  
WeiZhe Wang ◽  
YingZheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Flow Field ◽  
Design Optimization ◽  
Operating Conditions ◽  
Free Form ◽  
Aerodynamic Design ◽  
Objective Functions ◽  
Aerodynamic Design Optimization ◽  
Design Variables ◽  
Free Form Deformation

AbstractThis paper describes an aerodynamic design optimization of a highly loaded compressor stator blade using parameterized free-form deformation (FFD). The optimization methodology presented utilizes a B-spline-based FFD control volume to map the blade from the object space to the parametric space via transformation operations in order to perturb the blade surface. Coupled with a multi-objective genetic algorithm (MOGA) and a Gaussian process-based response surface method (RSM), a fully automated iterative loop was used to run the optimization on a fitted correlation function. A weighted average reduction of 6.1% and 36.9% in total pressure loss and exit whirl angle was achieved, showing a better compromise of objective functions with smoother blade shape than other results obtained in the open literature. Data mining of the Pareto set of optimums revealed four groups of data interactions of which some design variables were found to have skewed scatter relationship with objective functions and can be redefined for further improvement of performance. Analysis of the flow field showed that the thinning of the blade at midspan and reduction in camber distribution were responsible for the elimination of the focal-type separation vortex by redirecting the secondary flow in an axially forward direction toward the midspan and near the hub endwall downstream. Furthermore, the reduction in exit whirl angle especially at the shroud was due to the mild bow shape which generated radial forces on the flow field thereby reducing the flow diffusion rate at the suction surface corner. This effect substantially delayed or eliminated the formation of corner separation at design and off-design operating conditions. Parameterized FFD was found to have superior benefits of smooth surface generation with low number of design variables while maintaining a good compromise between objective functions when coupled with a genetic algorithm.

Preliminary Aero Engine Life Assessment via Techno-Economic Environmental Risk Analysis

2013 ◽  
Author(s):  
Rukshan Navaratne ◽  
William Camilleri ◽  
Esmail Najafi ◽  
Vishal Sethi ◽  
Pericles Pilidis
Keyword(s):  
Environmental Risk ◽  
Parametric Study ◽  
Design Space ◽  
Design Parameters ◽  
Trade Off ◽  
Engine Efficiency ◽  
Fuel Burn ◽  
Optimization Study ◽  
Environmental Risk Analysis ◽  
Engine Life

Significant progress has been made towards the improvement of engine efficiency through the increase in overall pressure ratio (OPR) and reduction in specific thrust (SFN). The implications of engine design extend beyond thermodynamics and should include the consideration of multi-disciplinary aspects related to operation, emissions, lifing and cost. This paper explores the relationship between fuel burn and engine life across the design space of a typical aircraft engine integrated system. In this context the Cranfield University Techno-economic Environmental Risk Analysis (TERA) methodology allows for the assessment of environmental and economic risk when the design of an engine system is at its conceptual stage. It is essentially a multi-disciplinary optimization framework which can be used for design space exploration. Such an approach is necessary in order to assess the trade-off between asset life and powerplant efficiency at the preliminary stage of the design process. A parametric study was conducted in order to assess the sensitivity of major design parameters on engine life and specific fuel consumption (SFC) for a given engine type. The principal failure modes of creep, fatigue and oxidation, were considered for engine life estimation. In addition an optimization study was carried out in order to investigate the trade-off between fuel burn and engine life as Time Between Overhaul (TBO). This was accomplished by integrating aircraft performance, engine performance and lifing models in the TERA Framework. An increase in turbine entry temperature (TET) is required to maintain efficiency at OPR. However, as TET has a strong influence on engine life there is an important trade-off to be made against engine efficiency. The parametric study outlined in this work explores the design space both with respect to engine life as well as efficiency. The optimization study showed that a penalty of 1.42kg additional fuel is required per additional hour of TBO. The fuel penalty is a consequence of sub-optimal design parameters with respect to engine efficiency and is applicable for the presented engine aircraft combination.

scholarly journals OPTIMISATION OF HULL FORM OF OCEAN-GOING TRAWLER

Brodogradnja ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 33-46
Author(s):  
Cheng Zhao ◽  
◽  
Wei Wang ◽  
Panpan Jia ◽  
Yonghe Xie ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Free Form ◽  
Deformation Method ◽  
Nsga Ii ◽  
Hull Form ◽  
Free Form Deformation ◽  
Before And After ◽  
Computational Fluid Dynamics Cfd ◽  
Bulbous Bow ◽  
Aided Design

This paper proposes a method for optimising the hull form of ocean-going trawlers to decrease resistance and consequently reduce the energy consumption. The entire optimisation process was managed by the integration of computer-aided design and computational fluid dynamics (CFD) in the CAESES software. Resistance was simulated using the CFD solver and STAR-CCM+. The ocean-going trawler was investigated under two main navigation conditions: trawling and design. Under the trawling condition, the main hull of the trawler was modified using the Lackenby method and optimised by NSGA-II algorithm and Sobol + Tsearch algorithm. Under the design condition, the bulbous bow was changed using the free-form deformation method, and the trawler was optimised by NSGA-Ⅱ. The best hull form is obtained by comparing the ship resistance under various design schemes. Towing experiments were conducted to measure the navigation resistance of trawlers before and after optimisation, thus verifying the reliability of the optimisation results. The results show that the proposed optimisation method can effectively reduce the resistance of trawlers under the two navigation conditions.

Modeling Surface Variation and Assessment of its Impact on Aerodynamic Performance in Turbomachinery Applications

2017 ◽  
Author(s):  
Saurya Ranjan Ray ◽  
Ravikanth Avancha ◽  
Sriram Shankaran ◽  
Lyle Dailey
Keyword(s):  
Product Life Cycle ◽  
Design Space ◽  
Correlation Coefficients ◽  
Local Effect ◽  
Free Form ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Critical Dimensions ◽  
Measuring Machine ◽  
The Impact

Analyzing the impact of manufacturing variability on the performance of turbomachinery components supports two critical dimensions in the product life cycle. It provides a technological process to decide on the criterion of acceptability of the deviated manufactured component based on the performance assessment and secondly, provides sensitivity of the geometrical variation to the component performance to build a robust design space. Currently, non-deterministic methods based on probability distribution function of the design parameters are used to assess the impact of variability, and the output is presented as a set of correlation coefficients linking the geometrical parameters to the component performance. Though these methods provide adequate information on the robustness of the design space, the amount of computational requirement and lack of consideration of the local effect of geometry modification on the performance pose challenges. In the current work, the method presented is based on a variant of radial basis functions to construct the deviated manufactured geometry of the turbine rear frame (TRF) of a modern gas turbine engine for aerodynamic assessment. The approach is used to accurately and rapidly construct a 3D deviation map from a set of discrete measured data using a co-ordinate measuring machine (CMM). Additionally, the developed approach is used as a method of applying free-form deformation in the critical localities on the aerodynamic surface identified using the sensitivity information from an adjoint analysis to re-design the baseline geometry. Aerodynamic analysis is conducted to understand the nature of geometry change due to the manufacturing variation and explain the mechanism of performance change.

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

2021 ◽  
pp. 1-18
Author(s):  
F. Akram ◽  
H. A. Khan ◽  
T. A. Shams ◽  
D. Mavris
Keyword(s):  
Data Mining ◽  
Data Fusion ◽  
Design Space ◽  
Design Parameters ◽  
Design Variables ◽  
Current Design ◽  
Aerial Vehicle ◽  
Hybrid Data ◽  
Computational Fluid Dynamics Cfd ◽  
Cruise Flight

ABSTRACT The research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

scholarly journals The Influence of Parameterisation Setup on the Constrained Adjoint Optimisation of Transonic Fan Blades

2020 ◽  
Author(s):  
Alistair John ◽  
Ning Qin ◽  
Shahrokh Shahpar
Keyword(s):  
Computational Cost ◽  
Free Form ◽  
Design Parameters ◽  
Control Parameters ◽  
Compressor Blades ◽  
Mechanical Integrity ◽  
Free Form Deformation ◽  
Transonic Fan

Abstract The power of adjoint optimisation is that the computational cost of the optimisation is almost independent of the number of design parameters. Thus, hundreds or even thousands of parameters can be used with relatively little increase in cost. But how much benefit does this provide, and is it always beneficial to increase the number of parameters? This work investigates the benefit achieved during the optimisation of transonic fan and compressor blades using various resolutions and layouts of Free Form Deformation grid. A method to constrain thickness throughout the blade span (to maintain mechanical integrity) during the optimisation is implemented and the effect of constraining thickness on the resulting blade designs demonstrated. It is shown that increasing the number of parameters generally leads to improved optimised designs, but that increasing the number of free-form parameters in the axial and circumferential directions is more beneficial than increasing the number of radial control parameters.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

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