Multi-Objective Modeling of Leading-Edge Serrations Applied to Low-Pressure Axial Fans

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Till M. Biedermann ◽  
M. Reich ◽  
C. O. Paschereit
Keyword(s):  
Low Pressure ◽  
High Accuracy ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Latin Hypercube Design ◽  
Axial Fan ◽  
Multi Objective ◽  
Target Values ◽  
Leading Edges

Abstract A novel modeling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters (IP) are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3-octave bands, proving the ability of the model to generalize. A metaheuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selected pairs of target values. This is particularly important for reconciling opposing trends, such as the noise reduction capability and aerodynamic performance. The chosen optimization strategy also allows for a customized design of serrated leading edges, tailored to the specific operating conditions of the axial fan.

Multi-Objective Modelling of Leading Edge Serrations Applied to Low-Pressure Axial Fans

2020 ◽  
Author(s):  
Till M. Biedermann ◽  
M. Reich ◽  
C. O. Paschereit
Keyword(s):  
Low Pressure ◽  
High Accuracy ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Latin Hypercube Design ◽  
Axial Fan ◽  
Multi Objective ◽  
Target Values ◽  
Leading Edges

Abstract A novel modelling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3rd-octave bands, proving the ability of the model to generalize. A meta-heuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selected pairs of target values. This is particularly important for reconciling opposing trends, such as the noise reduction capability and aerodynamic performance. The chosen optimization strategy also allows for a customized design of serrated leading edges, tailored to the specific operating conditions of the axial fan.

Genetic Algorithm Based Optimization of Cutting Parameters in Drilling of Composite Materials

2010 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Genetic Algorithm ◽  
Composite Materials ◽  
Removal Rate ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Machining Process ◽  
Graphical Form ◽  
Optimization Approach ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Composite materials provide distinctive advantages in manufacture of advanced products because of attractive features such as high strength and light weight, but on the other hand machining of composite materials is difficult to carry out due to the anisotropic and non-homogeneous structure of composites and to the high abrasiveness of their reinforcing constituents. This typically results in damage being introduced into the workpiece and in very rapid wear development in the cutting tool. Conventional machining process such as drilling can be applied to composite materials, provided proper tool design and operating conditions are adopted. In this paper, A genetic algorithm (GA) based optimization procedure has been developed to optimize two factors, material removal rate; and delamination factor, using multi-objective function model with a weighted approach for the productivity, and superficial quality. An a posteriori approach was used to obtain a set of optimal solutions. An application sample was developed and its results were analyzed for several different production conditions. Finally, the obtained outcomes were arranged in graphical form and analyzed to make the proper decision for different process preferences. This paper also remarks the advantages of multi-objective optimization approach over the single-objective one.

Modern Compressor Aerodynamic Blading Process Using Multi-Objective Optimization

2006 ◽  
Author(s):  
Akin Keskin ◽  
Amit Kumar Dutta ◽  
Dieter Bestle
Keyword(s):  
High Efficiency ◽  
Axial Compressor ◽  
Design Flow ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Flow Conditions ◽  
Multi Objective ◽  
Blade Section ◽  
The Individual

Aerodynamic design of an axial compressor is a challenging design task requiring a compromise between contradicting requirements like wide operating range, high efficiency, low number of stages and high surge margin. Therefore, the design process is typically subdivided into a sequence of subproblems where the blading design is a key process. According to flow conditions, which result from throughflow calculations on axis-symmetric stream surfaces, 2-dimensional blade profiles have to be designed, which then may be stacked along a radial stacking line in order to find the 3D-blade geometry. The design of the blade sections is rather time consuming due to many iterations with different programs. Usually a geometry generation tool is used to describe the blade sections which are then evaluated by a blade-to-blade CFD solver. The quality of a single blade section is typically characterized by the overall loss at design flow conditions and the working range determined by an amount of loss increase due to incidence variation. The aerodynamic performance of the final airfoils and thus of the whole compressor depends significantly on the design of the individual blade sections. In this investigation an automated multi-objective optimization strategy is developed to find best blade section geometries with respect to loss and working range. The multi-objective optimization approach provides Pareto-optimal compromise solutions at reasonable computational costs outperforming a given Rolls-Royce datum design which has been ‘optimized’ manually by a human design engineer.

Multi Objective Optimization of Direct Coupling Photovoltaic-Electrolyzer Systems Using Imperialist Competitive Algorithm

2014 ◽  
Author(s):  
Azadeh Maroufmashat ◽  
Farid Sayedin ◽  
Sourena Sattari
Keyword(s):  
Energy Transfer ◽  
Hydrogen Production ◽  
Minimum Energy ◽  
Imperialist Competitive Algorithm ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Competitive Algorithm ◽  
Pem Electrolyzer

Photovoltaic-electrolyzer systems are one of the most promising alternatives for obtaining hydrogen from a renewable energy source. Determining size and the operational conditions are always a key issue while coupling directly renewable electricity sources to PEM electrolyzer. In this research, the multi objective optimization approach based on an imperialist competitive algorithm (ICA), which is employed to optimize the size and the operating conditions of a directly coupled photovoltaic (PV)-PEM electrolyzer. This allows the optimization of the system by considering two different objectives, including, minimization of energy transfer loss and maximization of hydrogen generation. Multi objective optimization of PV/EL system predicts a maximum hydrogen production of 7930 gr/yr for energy transfer loss of 16.48 kWh/yr and minimum energy transfer loss of 5.21 kWh/yr at a hydrogen production rate of 7760 gr/yr for a the given location and the PV module.

A New Energy Optimization Strategy for Pumping Operation in Water Distribution Systems

2012 ◽  
Author(s):  
Dhafar Al-Ani ◽  
Saeid Habibi
Keyword(s):  
Distribution System ◽  
Energy Cost ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Water Distribution Systems ◽  
Energy Optimization ◽  
Operating Conditions ◽  
Optimization Strategy ◽  
Multi Objective

As time goes on, more and more operating-modes based on changing demand profiles will be compiled to enrich the range of feasible solutions for a water distribution system. This implies the conservation of energy consumed by a water pumping station and improves the ability for energy optimization. Another important goal was improving safety, reliability, and maintenance cost. In this paper, three important goals were addressed: cost-effectives, safety, and self-sustainability operations of water distribution systems. In this work, the objective functions to optimize were total electrical energy cost, maintenance costs, and reservoir water level variation while preserving the service provided to water clients. To accomplish these goals, an effective Energy Optimization Strategy (EOS) that manages trade-off among operational cost, system safety, and reliability was proposed. Moreover, the EOS aims at improving the operating conditions (i.e., pumping schedule) of an existing network system (i.e., with given capacities of tanks) and without physical changes in the infrastructure of the distribution systems. The new strategy consisted of a new Parallel Multi-objective Particle Swarm optimization with Adaptive Search-space Boundaries (P-MOPSO-ASB) and a modified EPANET. This has several advantages: obtaining a Pareto-front with solutions that are quantitatively equally good and providing the decision maker with the opportunity to qualitatively compare the solutions before their implementation into practice. The multi-objective optimization approach developed in this paper follows modern applications that combine an optimization algorithm with a network simulation model by using full hydraulic simulations and distributed demand models. The proposed EOS was successfully applied to a rural water distribution system, namely Saskatoon West. The results showed that a potential for considerable cost reductions in total energy cost was achieved (approximately % 7.5). Furthermore, the safety and the reliability of the system are preserved by using the new optimal pump schedules.

Multi-objective optimization of vehicle seat suspension with friction under random excitation

2021 ◽  
pp. 095440702110555
Author(s):  
Rongkang Luo ◽  
Peibao Wu ◽  
Jiabin Luo ◽  
Zhichao Hou ◽  
Le He ◽  
...  
Keyword(s):  
Linear Model ◽  
Human Body ◽  
Random Excitation ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Seat Suspension ◽  
Model Linearization ◽  
Vehicle Seat

A seat suspension contributes greatly to vehicle ride comfort as a result of direct contact with the human body. Friction in a seat suspension produces strong non-smooth nonlinearity in seat dynamics, which makes the simulation-based optimization on the seat suspension’s performance time-consuming. This study tries to address parameter optimization on a vehicle seat suspension with the friction force in an analytical approach. A two degrees of freedom model is firstly established for the human body-seat system with friction and subjected to bandlimited random excitation. The nonlinear model is converted into an equivalent linear model by using Gaussian linearization. The dynamic responses of the linear model have then derived analytically and validated by Monte Carlo simulations. Based on the analytical solution, a multi-objective optimization strategy is proposed for the seat suspension. The acceleration of the human body and the suspension travel are chosen as the objective indexes to evaluate seat performance. Simulation results show that the proposed optimization strategy is efficient, where a global optimum is guaranteed owing to the analytical expression of the objective function. The optimization approach taking advantage of model linearization can be applied to similar mechanical systems with friction.

A Unified Control Framework for Traction Machine Drive Using Linear Parameters Varying-Based Field-Oriented Control

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Athar Hanif ◽  
Qadeer Ahmed ◽  
Aamer Iqbal Bhatti ◽  
Giorgio Rizzoni
Keyword(s):  
Operating Conditions ◽  
Linear Matrix ◽  
Field Oriented Control ◽  
Optimization Approach ◽  
Direct Drive ◽  
Multi Objective ◽  
The Road ◽  
Control Framework ◽  
Conflicting Objectives ◽  
The Stability

Abstract The performance of an electrified powertrain in extreme operating conditions is greatly compromised. This is due to the fact that meeting the road loads, ensuring efficient powertrain operation, and minimizing the loss of lifetime (aging) of an electric machine are three essential but conflicting targets. In this paper, a unified multi-objective linear parameters varying (LPV)-based field-oriented control (FOC) framework is proposed to solve the problem of conflicting objectives mentioned above. The outer loop in a unified control framework generates the reference currents using linear matrix inequality (LMI)-based torque and flux controllers. Moreover, optimal flux is estimated using LPV observer to ensure efficient machine operations. In the inner loop of a unified control framework, the multi-objective controller (MOC) is synthesized by selecting optimal weighting functions using LMI-based convex optimization approach. The stability of the proposed unified control framework is also analyzed. The effectiveness of the proposed unified control framework is tested for a direct drive electrified powertrain of a three-wheeled vehicle commonly found in urban transportation for Asian countries. The urban driving schedule-based simulation results confirm that the lifetime of traction machine can be enhanced by appropriate control framework design without compromising its performance.

scholarly journals Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 778 ◽  
Author(s):  
Clara M. Ionescu ◽  
Constantin F. Caruntu ◽  
Ricardo Cajo ◽  
Mihaela Ghita ◽  
Guillaume Crevecoeur ◽  
...  
Keyword(s):  
Predictive Control ◽  
Wind Farm ◽  
Cost Effective ◽  
Optimality Criteria ◽  
Operating Conditions ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Efficiency Cost

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.

scholarly journals Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning

2021 ◽  
Author(s):  
Harshal D. Akolekar ◽  
Fabian Waschkowski ◽  
Yaomin Zhao ◽  
Roberto Pacciani ◽  
Richard D. Sandberg
Keyword(s):  
Machine Learning ◽  
Shear Stress ◽  
Wall Shear Stress ◽  
Low Pressure ◽  
Stress Profile ◽  
Transition Model ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wall Shear

Existing Reynolds Averaged Navier-Stokes based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this study, a novel framework based on computational fluids dynamics driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model.

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