scholarly journals Automatic Strain Gauge Balance Design Optimization Approach and Implementation Based on Integration of Software

2020 ◽  
Vol 20 (1) ◽  
pp. 22-34
Author(s):  
Guangwei Xiang ◽  
Peng Mi ◽  
Guoqing Yi ◽  
Chao Wang ◽  
Wei Liu
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Network Architecture ◽  
High Performance ◽  
Lightweight Design ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Balance Design ◽  
Design Variables ◽  
Tunnel Strain

AbstractThe traditional wind tunnel strain balance design cycle is a manual iterative process. With the experience and intuition of the designer, one solution that meets the design requirements can be selected among a small number of design solutions. This paper introduces a novel software integration-based automatic balance design optimization system (ABDOS) and its implementation by integrating professional design knowledge and experience, stepwise optimization strategy, CAD-CAE software, self-developed scripts and tools. The proposed two-step optimization strategy includes the analytical design process (ADP) and the finite element method design process (FEDP). The built-in optimization algorithm drives the design variables change and searches for the optimal structure combination meeting the design objectives. The client-server based network architecture enables local lightweight design input, task management, and result output. The high-performance server combines all design resources to perform all the solution calculations. The development of more than 10 balances that have been completed and a case study show that this method and platform significantly reduce the time for design evaluation and design-analysis-redesign cycles, assisting designers to comprehensively evaluate and improve the performance of the balance.

Multiobjective Design Optimization of Microchannel Cooling System Using High Performance Thermal Vias in LTCC Substrates

2013 ◽  
Vol 10 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Aparna Aravelli ◽  
Singiresu S. Rao ◽  
Hari K. Adluru
Keyword(s):  
Heat Transfer ◽  
Design Optimization ◽  
High Performance ◽  
Cooling System ◽  
Optimization Technique ◽  
Optimization Theory ◽  
Thermal Design ◽  
Design Variables ◽  
Microchannel Cooling ◽  
Multiobjective Design

Increased heat generation in semiconductor devices for demanding applications leads to the investigation of highly efficient cooling solutions. Effective options for thermal management include passing of cooling liquid through the microchannel heat sink and using highly conductive materials. In the author's previous work, experimental and computational analyses were performed on LTCC substrates using embedded silver vias and silver columns forming microchannels. This novel technique of embedding silver vias along with forced convection using a coolant resulted in higher heat transfer rates. The present work investigates the design optimization of this cooling system (microheat exchanger) using systems optimization theory. A new multiobjective optimization problem was formulated for the heat transfer in the LTCC model using the log mean temperature difference (LMTD) method of heat exchangers. The goal is to maximize the total heat transferred and to minimize the coolant pumping power. Structural and thermal design variables are considered to meet the manufacturability and energy requirements. Pressure loss and volume of the silver metal are used as constraints. A hybrid optimization technique using sequential quadratic programming (SQP) and branch and bound method of integer programming has been developed to solve the microheat exchanger problem. The optimal design is presented and sensitivity analysis results are discussed.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

scholarly journals Integrated uncertainty techniques in aircraft derivative design optimization.

2021 ◽  
Author(s):  
Hyeong-Uk Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Design Process ◽  
Aircraft Design ◽  
Collaborative Optimization ◽  
Aircraft Manufacturing ◽  
Analysis Tools ◽  
Design Variables ◽  
The Cost ◽  
Aircraft Conceptual Design

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

Towards Next Generation of High-Performance Radio Frequency Passive Components for Beyond-5G, 6G and Super-IoT – Application of Response Surface Method to the Optimization of RF-MEMS Reconfigurable Power Attenuators

2021 ◽  
Author(s):  
Jacopo Iannacci ◽  
Girolamo Tagliapietra ◽  
Alessio Bucciarelli
Keyword(s):  
Radio Frequency ◽  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
High Performance ◽  
Rf Mems ◽  
Optimization Approach ◽  
Scattering Parameter ◽  
Passive Components ◽  
Surface Method

Abstract The emerging paradigms of Beyond-5G, 6G and Super-IoT will demand for Radio Frequency (RF) passive components with pronounced performance, and RF-MEMS technology, i.e. Microsystem-based RF passives, is a good candidate to meet such a challenge. As known, RF-MEMS have a complex behavior, that crosses different physical domains (mechanical; electrical; electromagnetic), making the whole design optimization and trimming phases particularly articulated and time consuming. In this work, we propose a novel design optimization approach based on the Response Surface Method (RSM) statistical methodology, focusing the attention on a class of RF-MEMS-based programmable step power attenuators. The proposed method is validated both against physical simulations, performed with Finite Element Method (FEM) commercial software tools, as well as experimental measurements of physical devices. The case study here discussed features 3 DoFs (Degrees of Freedom), comprising both geometrical and material parameters, and aims at optimizing the RF performance of the MEMS attenuator in terms of attenuation (S21 Scattering parameter) and reflection (VSWR – Voltage Standing Wave Ratio). When validate, the proposed RSM-based method allows avoiding physical FEM simulations, thus making the design optimization considerably faster and less complex, both in terms of time and computational load.

A MultiDisciplinary Optimization Approach for Sizing Vertical Lift Aircraft

2021 ◽  
Author(s):  
Ananth Sridharan ◽  
Bharath Govindarajan
Keyword(s):  
Optimal Solution ◽  
Multidisciplinary Optimization ◽  
Shape Design ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Clock Time ◽  
Element Analysis ◽  
Analysis And Design ◽  
Design Variables ◽  
Vertical Lift

This paper presents an approach to reframe the sizing problem for vertical-lift unmanned aerial vehicles (UAVs) as an optimization problem and obtains a weight-optimal solution with up to two orders of magnitude of savings in wall clock time. Because sizing is performed with higher fidelity models and design variables from several disciplines, the Simultaneous Analysis aNd Design (SAND) approach from fixed-wing multidisciplinary optimization literature is adapted for the UAV sizing task. Governing equations and disciplinary design variables that are usually self-contained within disciplines (airframe tube sizes, trim variables, and trim equations) are migrated to the sizing optimizer and added as design variables and (in)equality constraints. For sizing consistency, the iterative weight convergence loop is replaced by a coupling variable and associated equality consistency constraint for the sizing optimizer. Cruise airspeed is also added as a design variable and driven by the sizing optimizer. The methodology is demonstrated for sizing a package delivery vehicle (a lift-augment quadrotor biplane tailsitter) with up to 39 design variables and 201 constraints. Gradient-based optimizations were initiated from different starting points; without blade shape design in sizing, all processes converged to the same minimum, indicating that the design space is convex for the chosen bounds, constraints, and objective function. Several optimization schemes were investigated by moving combinations of relevant disciplines (airframe sizing with finite element analysis, vehicle trim, and blade aerodynamic shape design) to the sizing optimizer. The biggest advantage of the SAND strategy is its scope for parallelization, and the inherent ability to drive the design away from regions where disciplinary analyses (e.g., trim) cannot find a solution, obviating the need for ad hoc penalty functions. Even in serial mode, the SAND optimization strategy yields results in the shortest wall clock time compared to all other approaches.

scholarly journals Integrated uncertainty techniques in aircraft derivative design optimization.

2021 ◽  
Author(s):  
Hyeong-Uk Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Design Process ◽  
Aircraft Design ◽  
Collaborative Optimization ◽  
Aircraft Manufacturing ◽  
Analysis Tools ◽  
Design Variables ◽  
The Cost ◽  
Aircraft Conceptual Design

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

Structural Optimization of Fir-Tree Root and Groove for Turbine Blade With Superellipse and P-Norm Aggregation Function

2019 ◽  
Author(s):  
Deqi Yu ◽  
Xiaojuan Zhang ◽  
Jiandao Yang ◽  
Kai Cheng ◽  
Ming Li
Keyword(s):  
Stress Concentration ◽  
Design Optimization ◽  
Finite Size ◽  
Large Family ◽  
Peak Stress ◽  
Aggregation Function ◽  
Optimization Approach ◽  
Optimization Constraints ◽  
Design Variables ◽  
Family Of Curves

Abstract Due to its finite size and the large centrifugal load, the fir-tree root is highly stressed, which leads to the possible early failure of the gas turbine and steam turbine. To find an optimized fir-tree root is an important issue for the design of the turbine structures. In this paper, a superellipse-based design optimization approach is proposed for the fir-tree root. Rather than the straight line and arc used in literature, the combination of the superellipse curve and line are employed to characterize the fir-tree root since the superellipse curve represents a large family of curves with limited parameters, which makes the design optimization easy and economic. For the design optimization, the objective function is to minimize the peak stress, which is a typical min-max problem with possible severe iterative oscillation and subsequent convergence difficulty. To avoid this problem, a P-norm aggregation function is proposed. The superellipse parameters are defined as design variables, while the stress concentration factor and the stress at root neck are specified as optimization constraints. With the P-series fir-tree root design as example, it is proved that our approach is effective to find the optimized configuration with better stress distribution and lower stress concentration.

Multi-Objective Design Optimization of Multiple Microchannel Heat Transfer Systems Based on Multiple Prioritized Preferences

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Po Ting Lin ◽  
Mark Christian E. Manuel ◽  
Jingru Zhang ◽  
Yogesh Jaluria ◽  
Hae Chang Gea
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Optimal Design ◽  
Pressure Drop ◽  
Design Optimization ◽  
Integrated Circuit ◽  
Pareto Frontier ◽  
User Preferences ◽  
Optimization Strategy ◽  
Design Variables

Accelerated development in the field of electronics and integrated circuit technology further pushed the need for better heat dissipating devices with reduced component dimensions. In the design optimization of microchannel heat transfer systems, multiple objectives must be satisfied but correlations limit the satisfaction levels. End users define their preferences associated with the desired quality/quantity of each parameter and specify the priorities among each preference. In this paper, an optimization strategy based on the prioritized performances is developed to find the optimal design variables for the preferences in three different aspects namely: minimized thermal resistances, minimized pressure drop, and maximized heat flux. The preferences are often fuzzy and correlated but can be modeled mathematically using Gaussian membership functions with respect to different levels of user preferences. The overall performances are maximized to find the most favorable solution on the Pareto frontier. Two different types of single-phase liquid cooling (straight and U-shaped microchannel heat sinks) have been utilized as heat exchangers of electronic chips and made as practical examples for the proposed optimization strategy. The optimal design points vary with respect to the priorities of the preferences. The proposed methodology finds the most favored solution on the Pareto frontiers. It is novel to reveal that the chosen significant factors were maximized with results yielding to lower thermal resistance, lower pressure drop, and higher heat flux in the microchannel heat sink based on the design preferences with different priorities.

scholarly journals Multi-Objective Design Optimization of a Variable Geometry Spray Fuel Injector

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
J. R. Archer ◽  
Tiegang Fang ◽  
Scott Ferguson ◽  
Gregory D. Buckner
Keyword(s):  
Design Optimization ◽  
High Performance ◽  
Pareto Frontier ◽  
Spray Angle ◽  
Design Parameters ◽  
Variable Geometry ◽  
Fuel Injector ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper explores the simulation-based design optimization of a variable geometry spray (VGS) fuel injector. A multi-objective genetic algorithm (MOGA) is interfaced with commercial computational fluid dynamics (CFD) software and high performance computing capabilities to evaluate the spray characteristics of each VGS candidate design. A three-point full factorial experimental design is conducted to identify significant design variables and to better understand possible variable interactions. The Pareto frontier of optimal designs reveals the inherent tradeoff between two performance objectives—actuator stroke and spray angle sensitivity. Analysis of these solutions provides insight into dependencies between design parameters and the performance objectives and is used to assess possible performance gains with respect to initial prototype configurations. These insights provide valuable design information for the continued development of this VGS technology.

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