Truncation Error Based Mesh Optimization

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Charles W. Jackson ◽  
Christopher J. Roy ◽  
Christopher R. Schrock
Keyword(s):  
Truncation Error ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Discretization Error ◽  
Two Dimensions ◽  
Optimization Techniques ◽  
Local Source ◽  
Mesh Optimization ◽  
Design Variables ◽  
Mesh Equidistribution

Abstract Truncation error is used to drive mesh adaptation in order to reduce the discretization error in solutions to a variety of 1D and 2D flow problems. The adaptation is performed using r-adaptation to move the mesh nodes in the domain in an attempt to reduce the truncation error since it is the local source of discretization error. Here, we present a new set of r-adaptation methods called mesh optimization along with three different ways of performing this type of adaptation. Each of these techniques uses a finite difference gradient-based local optimization technique with different sets of design variables to create a mesh that minimizes a functional based on truncation error. These new truncation error based mesh optimization techniques are compared to a more common truncation error based mesh equidistribution technique. Some observations on the performance and behavior of the different adaptation methods and best practices for their use are presented. All of the optimization methods are shown to reduce the truncation error one or two orders of magnitude and the discretization error by roughly one order of magnitude for the 1D problems tested. In two dimensions, the optimization-based adaptation methods are able to reduce the discretization error by up to a factor of seven. Mesh equidistribution achieved similar levels of improvement for much less cost compared to the mesh optimization techniques.

scholarly journals A Lightweight Algorithm to Optimize Power Consumption in Sensor Node through Parametric Tuning Of Communication Interface

2019 ◽  
Vol 8 (9) ◽  
pp. 2967-2973
Keyword(s):  
Sensor Node ◽  
Optimization Technique ◽  
Cluster Head ◽  
Region Of Interest ◽  
Parameter Tuning ◽  
Optimization Methods ◽  
Research Problem ◽  
Optimization Techniques ◽  
Communication Cost ◽  
Sensor Nodes

Wireless sensor network is widely used to monitor sparsely generated events through a centralized system. The coverage provided by the network to monitor region of interest vary and rely on the system components of sensor node. While monitoring events from cluster head or sink through sensor nodes located at a distant, it experiences increased communication cost due to prone errors which are proportional to losses due to distance and interferences. Biotelemetry application is reviewed for the derivation of the research problem which is worked upon in the present paper. The animals move around in their habitat performing various activities which are to be monitored. The sensing tags are mounted on the animals to study the behaviour of the animals. These animals roam around in their habitat generating variation in the interferences caused for communication. Due to wide variations, the inefficiency of the sensor node in communication results in draining battery rapidly and thereby life of sensor node. So in order to improve lifetime the optimization at the sensor node is very essential to keep network alive. The technique that performs optimization needs to be lightweight in terms of processing required as well as tuning of parameter which are required for model based optimization methods. In the paper, we proposed a technique which is lightweight and can dynamically optimize operating state. It is done by adaptively configuring communication parameters to patch losses and conserve energy to enhance Sensor node lifetime. The optimization technique proposed, does parameter tuning to minimize the communication cost in sensor node through efficient search method. The results are compared with traditionally employed technique with static setting and Online Greedy Optimization Algorithm. NI LabVIEW is used to do simulation of the model and for estimating the effect of parameter reconfiguration upon application of optimization techniques

Minimum Weight Design of a Car Trunk Deck-Lid Subject to Overall Stiffness Constraints

1982 ◽  
Vol 104 (4) ◽  
pp. 831-836 ◽  
Author(s):  
H. A. Du ◽  
S. C. Tang
Keyword(s):  
Minimum Weight ◽  
Optimization Technique ◽  
Design Procedure ◽  
Optimization Techniques ◽  
Structural Components ◽  
Minimum Weight Design ◽  
Design Constraints ◽  
Design Variables ◽  
Practical Design ◽  
Weight Design

A design procedure for a car trunk deck-lid using an approximate optimization technique is presented. Selecting the deck-lid gages and deck-lid inner panel configuration as design variables and overall stiffnesses as constraints, a possible weight reduction of 20 percent is demonstrated, compared with the base production deck-lid design. Although other practical design constraints might not allow one to achieve this goal, the potential value of optimization techniques is clearly demonstrated by this study. It is concluded that it could be useful to develop and apply such a procedure to components such as hoods, deck-lids, doors, and fenders, which are isolatable as structural components.

scholarly journals Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

2008 ◽  
Vol 15 (3-4) ◽  
pp. 257-272 ◽  
Author(s):  
Felipe A.C. Viana ◽  
Valder Steffen Jr. ◽  
Marcelo A.X. Zanini ◽  
Sandro A. Magalhães ◽  
Luiz C.S. Góes
Keyword(s):  
Inverse Problem ◽  
Shock Absorber ◽  
Model Updating ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Initial Guess ◽  
Landing Gear ◽  
Design Variables ◽  
Non Linear ◽  
The Difference

This work deals with the application of a nature-inspired optimization technique to solve an inverse problem represented by the identification of an aircraft landing gear model. The model is described in terms of the landing gear geometry, internal volumes and areas, shock absorber travel, tire type, and gas and oil characteristics of the shock absorber. The solution to this inverse problem can be obtained by using classical gradient-based optimization methods. However, this is a difficult task due to the existence of local minima in the design space and the requirement of an initial guess. These aspects have motivated the authors to explore a nature-inspired approach using a method known as LifeCycle Model. In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. The polytropic coefficient of the gas and the damping parameter of the shock absorber are assumed as being unknown: they are considered as design variables. As an illustration, experimental drop test data, obtained under zero horizontal speed, were used in the non-linear landing gear model updating of a small aircraft.

Design of a Micromixer With Herringbone Grooves Using Numerical Optimization Techniques

2007 ◽  
Author(s):  
Mubashshir Ahmad Ansari ◽  
Kwang-Yong Kim
Keyword(s):  
Spatial Data ◽  
Numerical Optimization ◽  
Optimization Technique ◽  
Three Dimensional ◽  
Groove Depth ◽  
Optimization Techniques ◽  
Navier Stokes ◽  
Channel Height ◽  
Design Variables ◽  
Data Statistics

Optimization of a staggered herringbone groove micromixer has been performed by using three-dimensional Navier-Stokes analysis. The analysis of the degree of mixing is performed by the calculation of spatial data statistics. The calculation of the variance of the mass fraction at various nodes on a plane in the channel is used to quantify mixing. A numerical optimization technique is applied to optimize the shape of the grooves on a single wall of the channel. Two design variables, namely, the ratio of the groove depth to channel height and the angle of the groove, are selected for optimization. A mixing index is used as the objective function. The results of the optimization show that the mixing is very sensitive to the shape of the groove which can be used in controlling mixing in microdevices.

Application of Topological Optimization Techniques to Automotive Front Structure Design

2001 ◽  
Author(s):  
Robert R. Mayer
Keyword(s):  
Optimization Technique ◽  
Shell Element ◽  
Optimality Criteria ◽  
Structure Design ◽  
Optimization Techniques ◽  
Design Concept ◽  
Topological Optimization ◽  
Shell Elements ◽  
Front Structure ◽  
Design Variables

Abstract An application of topological optimization techniques to automotive front structure design was considered, for the case of crashworthiness performance. An earlier developed topological optimization technique was used by first deriving an optimality criteria. To develop an optimality criteria, a functional relationship was developed between microstructure design variables, and both strain energy and volume, for shell element structures. Its previous application was the location of holes to lighten a rear longitudinal rail. The present application applies this theory to the actual layout of automotive front structure. The nonlinear code, LS-DYNA, was used for the simulations. An observation of various automotive front structural topologies, sometimes called structural layouts or architectures, reveals that many different design approaches are possible. In order to numerically assess the optimum layout the design space available for front structure design is built up from layers of shell elements, and the method of constructing these models using LS-INGRID is discussed. Some of these elements are deleted at each optimization iteration, resulting in a design concept. The design concept is interpreted as a number of structural load paths, and a unique design was then identified.

Design optimization of large-scale structures with sequential linear programming

2002 ◽  
Vol 216 (8) ◽  
pp. 799-811 ◽  
Author(s):  
L Lamberti ◽  
C Pappalettere
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Large Scale ◽  
Optimization Technique ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Truss Structures ◽  
Large Set ◽  
Sequential Linear Programming

Design optimization of complex structures entails tasks that oppose the usual constraints on time and computational resources. However, using optimization techniques is very useful because it allows engineers to obtain a large set of designs at low computational cost. Among the different optimization methods, sequential linear programming (SLP) is very popular because of its simplicity and because linear solvers (e.g. Simplex) are easily available. In spite of the inherent theoretical simplicity, well-coded SLP algorithms may outperform more sophisticated optimization methods. This paper describes the experience obtained in the design optimization of large-scale truss structures and beams with SLP-based algorithms. Sizing and configuration problems of structures under multiple loading conditions with up to 1000 design variables and 3500 constraints are considered. The relative performance and merits of some SLP-based algorithms are compared and the efficiency of an advanced SLP-based algorithm called ILEAML (improved linearization error amplitude move limits) is tested. ILEAML is also compared to the sequential quadratic programming (SQP) method, which is considered by theoreticians as probably the best theoretically founded optimization technique.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals A Sequential Approach for Aerodynamic Shape Optimization with Topology Optimization of Airfoils

2021 ◽  
Vol 26 (2) ◽  
pp. 34
Author(s):  
Isaac Gibert Martínez ◽  
Frederico Afonso ◽  
Simão Rodrigues ◽  
Fernando Lau
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Volume Fraction ◽  
Optimization Techniques ◽  
Free Form ◽  
Aerodynamic Shape Optimization ◽  
Sequential Approach ◽  
Airfoil Surface ◽  
Aerodynamic Shape ◽  
Design Variables

The objective of this work is to study the coupling of two efficient optimization techniques, Aerodynamic Shape Optimization (ASO) and Topology Optimization (TO), in 2D airfoils. To achieve such goal two open-source codes, SU2 and Calculix, are employed for ASO and TO, respectively, using the Sequential Least SQuares Programming (SLSQP) and the Bi-directional Evolutionary Structural Optimization (BESO) algorithms; the latter is well-known for allowing the addition of material in the TO which constitutes, as far as our knowledge, a novelty for this kind of application. These codes are linked by means of a script capable of reading the geometry and pressure distribution obtained from the ASO and defining the boundary conditions to be applied in the TO. The Free-Form Deformation technique is chosen for the definition of the design variables to be used in the ASO, while the densities of the inner elements are defined as design variables of the TO. As a test case, a widely used benchmark transonic airfoil, the RAE2822, is chosen here with an internal geometric constraint to simulate the wing-box of a transonic wing. First, the two optimization procedures are tested separately to gain insight and then are run in a sequential way for two test cases with available experimental data: (i) Mach 0.729 at α=2.31°; and (ii) Mach 0.730 at α=2.79°. In the ASO problem, the lift is fixed and the drag is minimized; while in the TO problem, compliance minimization is set as the objective for a prescribed volume fraction. Improvements in both aerodynamic and structural performance are found, as expected: the ASO reduced the total pressure on the airfoil surface in order to minimize drag, which resulted in lower stress values experienced by the structure.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

Structural Analysis and Optimal Design of Lightweight Skate for Loading and Unloading

2013 ◽  
Vol 785-786 ◽  
pp. 1258-1261
Author(s):  
In Pyo Cha ◽  
Hee Jae Shin ◽  
Neung Gu Lee ◽  
Lee Ku Kwac ◽  
Hong Gun Kim
Keyword(s):  
Topology Optimization ◽  
Structural Analysis ◽  
Optimal Design ◽  
Optimization Techniques ◽  
Normal Operation ◽  
Stress And Strain ◽  
Initial Model ◽  
Design Variables ◽  
Model Set ◽  
Main Frame

Topology optimization and shape optimization of structural optimization techniques are applied to transport skate the lightweight. Skate properties by varying the design variables and minimize the maximum stress and strain in the normal operation, while reducing the volume of the objective function of optimal design and Skate the static strength of the constraints that should not degrade compared to the performance of the initial model. The skates were used in this study consists of the main frame, sub frame, roll, pin main frame only structural analysis and optimal design was performed using the finite element method. Simplified initial model set design area and it compared to SM45C, AA7075, CFRP, GFRP was using the topology optimization. Strength does not degrade compared to the initial model, decreased volume while minimizing the stress and strain results, the optimum design was achieved efficient lightweight.

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