Constrained Multi-Objective Optimization of a Hydraulic Flywheel Accumulator

2013 ◽  
Author(s):  
Kyle G. Strohmaier ◽  
James D. Van de Ven
Keyword(s):  
Energy Storage ◽  
Design Parameters ◽  
Optimization Approach ◽  
Total System ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Important Means ◽  
Energy Independence ◽  
Hydraulic Accumulator ◽  
Velocity Constraints

Improving energy storage technology is an important means of addressing concerns over fossil fuel scarcity and energy independence. Traditional hydraulic accumulator energy storage, though favorable in power density, durability, cost, and environmental impact, suffers from relatively low energy density and a pressure-dependent state of charge. The hydraulic flywheel accumulator concept utilizes both pneumatic and kinetic energy domains by employing a rotating pressure vessel. This paper describes a mathematical model of the hydraulic flywheel accumulator and presents the results of a multi-objective optimization of the associated design parameters. The two optimization objectives are to minimize the total system mass and minimize the total energy converted between the pneumatic and kinetic domains during operation. These objectives are pursued by varying five design parameters: accumulator radius, wall thickness, and length; end cap length; and maximum angular velocity. Constraints on combinations of these design parameters are imposed by material stress, as well as the energy capacity required to complete a specified drive cycle. This optimization approach can be used to guide the design of a hydraulic flywheel accumulator for a variety of different applications.

A Parametric Optimization Approach of an Induction Heating System for Energy Consumption Reduction

2017 ◽  
Author(s):  
Paolo Cicconi ◽  
Anna Costanza Russo ◽  
Mariorosario Prist ◽  
Francesco Ferracuti ◽  
Michele Germani ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Induction Heating ◽  
Heating System ◽  
High Energy ◽  
Design Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Multi Objective ◽  
Induction Heating System

Nowadays, electromagnetic high-frequency induction is very used for different non-contact heating applications such as the molding process. Every molding process requires the preheating and the thermal maintenance of the molds, to enhance the filling phase and the quality of the final products. In this context, an induction heating system, mostly, is a customized equipment. The design and definition of an induction equipment depends on the target application. This technology is highly efficient and performant, however it provides a high-energy consumption. Therefore, optimization strategies are very suitable to reduce energy cost and consumption. The proposed paper aims to define a method to optimize the induction heating of a mold in terms of time, consumption, and achieved temperature. The proposed optimization method involves genetic algorithms to define the design parameters related to geometry and controller. A test case describes the design of an induction heating system for a polyurethane molding process, which is the soles foaming. This case study deals with the multi-objective optimization of parameters such as the geometrical dimensions, the inductor sizing, and the controller setting. The multi-objective optimization aims to reduce the energy consumption and to increase the wall temperature of the mold.

A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
H. Maral ◽  
C. B. Şenel ◽  
K. Deveci ◽  
E. Alpman ◽  
L. Kavurmacıoğlu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tip Clearance ◽  
Design Parameters ◽  
Leakage Flow ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Tip Leakage Flow ◽  
Tip Leakage ◽  
Multi Objective ◽  
Blade Tip

Abstract Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The “squealer” design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.

scholarly journals Automated Design Optimization of a Mono Tiltrotor in Hovering and Cruising States

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1155
Author(s):  
Lifang Zeng ◽  
Jianxin Hu ◽  
Dingyi Pan ◽  
Xueming Shao
Keyword(s):  
Design Parameters ◽  
Optimization Approach ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Optimal Point ◽  
Propulsive Efficiency ◽  
Multi Objective ◽  
Coaxial Rotor ◽  
Design Variables ◽  
Single Objective

A mono tiltrotor (MTR) design which combines concepts of a tiltrotor and coaxial rotor is presented. The aerodynamic modeling of the MTR based on blade element momentum theory (BEMT) is conducted, and the method is fully validated with previous experimental data. An automated optimization approach integrating BEMT modeling and optimization algorithms is developed. Parameters such as inter-rotor spacing, blade twist, taper ratio and aspect ratio are chosen as design variables. Single-objective (in hovering or in cruising state) optimizations and multi-objective (both in hovering and cruising states) optimizations are studied at preset design points; i.e., hovering trim and cruising trim. Two single-objective optimizations result in different sets of parameter selections according to the different design objectives. The multi-objective optimization is applied to obtain an identical and compromised selection of design parameters. An optimal point is chosen from the Pareto front of the multi-objective optimization. The optimized design has a better performance in terms of the figure of merit (FM) and propulsive efficiency, which are improved by 7.3% for FM and 13.4% for propulsive efficiency from the prototype, respectively. Further aerodynamic analysis confirmed that the optimized rotor has a much more uniform load distribution along the blade span, and therefore a better aerodynamic performance in both hovering and cruising states is achieved.

scholarly journals Multi-Objective Optimization of a Battery-Supercapacitor Hybrid Energy Storage System Based on the Concept of Cyber-Physical System

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1801
Author(s):  
Chenyun Pan ◽  
Shengyu Tao ◽  
Hongtao Fan ◽  
Mengyao Shu ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Cyber Physical System ◽  
Multi Objective Optimization ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Control Scheme

Optimal operation of energy storage systems plays an important role in enhancing their lifetime and efficiency. This paper combines the concepts of the cyber–physical system (CPS) and multi-objective optimization into the control structure of the hybrid energy storage system (HESS). Owing to the time-varying characteristics of HESS, combining real-time data with physical models via CPS can significantly promote the performance of HESS. The multi-objective optimization model designed in this paper can improve the utilization of supercapacitors, reduce energy consumption, and prevent the state of charge (SOC) of HESS from exceeding the limitation. The new control scheme takes the characteristics of the components of HESS into account and is beneficial in reducing battery short-term power cycling and high discharge currents. The rain-flow counting algorithm is applied for battery life prediction to quantify the benefits of the HESS under the control scheme proposed. A much better power-sharing relationship between the supercapacitor and the lithium–ion battery (LiB) can be observed from the SIMULINK results and the case study with our new control scheme. Moreover, compared to the traditional low-pass filter control method, the battery lifetime is quantifiably increased from 3.51 years to 10.20 years while the energy efficiency is improved by 1.56%.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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