Topology Optimization via Fictitious Physical Model for securing visibility and consideration to shorten computation time

2020 ◽  
Vol 2020.30 (0) ◽  
pp. 2106
Author(s):  
Eri ITO ◽  
Shinji NISHIWAKI ◽  
Kazuhiro IZUI ◽  
Takayuki YAMADA
Keyword(s):  
Topology Optimization ◽  
Physical Model ◽  
Computation Time

Accelerating Climate Model Computation by Neural Networks: A Comparative Study

2021 ◽  
Author(s):  
Maha Mdini ◽  
Takemasa Miyoshi ◽  
Shigenori Otsuka
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Statistical Model ◽  
Physical Model ◽  
Climate Model ◽  
Numerical Models ◽  
Computation Time ◽  
High Accuracy ◽  
Physical Models ◽  
Computer Resources

<p>In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.  In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.</p>

A Comparative Study of the Formulations for Topology Optimization of Compliant Mechanisms

2008 ◽  
Author(s):  
Sangamesh R. Deepak ◽  
M. Dinesh ◽  
Deepak Sahu ◽  
Salil Jalan ◽  
G. K. Ananthasuresh
Keyword(s):  
Topology Optimization ◽  
Comparative Study ◽  
Compliant Mechanisms ◽  
Computation Time ◽  
Initial Guess ◽  
Benchmark Problems ◽  
Topology Optimization Problem ◽  
Linear Elastic ◽  
Frame Element ◽  
Force Direction

The topology optimization problem for the synthesis of compliant mechanisms has been formulated in many different ways in the last 15 years, but there is not yet a definitive formulation that is universally accepted. Furthermore, there are two unresolved issues in this problem. In this paper, we present a comparative study of five distinctly different formulations that are reported in the literature. Three benchmark examples are solved with these formulations using the same input and output specifications and the same numerical optimization algorithm. A total of 35 different synthesis examples are implemented. The examples are limited to desired instantaneous output direction for prescribed input force direction. Hence, this study is limited to linear elastic modeling with small deformations. Two design parameterizations, namely, the frame element based ground structure and the density approach using continuum elements, are used. The obtained designs are evaluated with all other objective functions and are compared with each other. The checkerboard patterns, point flexures, the ability to converge from an unbiased uniform initial guess, and the computation time are analyzed. Some observations are noted based on the extensive implementation done in this study. Complete details of the benchmark problems and the results are included. The computer codes related to this study are made available on the internet for ready access.

Numerical Analysis of a Compressor Type of Dehumidifier: (I) Fluid Flow

2017 ◽  
Vol 25 (02) ◽  
pp. 1750011 ◽  
Author(s):  
Xuan Quang Duong ◽  
Jae Dong Chung
Keyword(s):  
Physical Model ◽  
High Performance ◽  
Three Dimensional ◽  
Computation Time ◽  
Object A ◽  
Porous Model ◽  
Two Factors ◽  
Fin Tube ◽  
Dimensional Simulation ◽  
Actual Shape

A three-dimensional simulation of a compressor dehumidifier was conducted by applying a porous model for condenser and evaporator, and a moving reference frame for the fan. A physical model was simulated for the unit cell of the actual shape of a fin-tube, and the parameters of viscous resistance and initial resistance were obtained. With these values, the porous model showed close agreement with the physical model within a reasonable computation time. A uniform flow across the evaporator and the condenser is desirable for high performance of the dehumidifier. Surface averaged velocity, standard deviation of velocity, and uniformity were chosen as indicators of the design object. A case study showed that two factors, (i) reducing the space between the evaporator and the condenser and (ii) introducing a cover to reduce the by-passing air flow, have the strongest influence on the air distribution in this dehumidifier.

Topology Optimization for Diffusive Heat Transfer in a Domain with Internal Heat Generation using Optimality Criteria

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
T. Anupkumar ◽  
Noble Sharma ◽  
A. Srinath ◽  
G. Satya Dileep
Keyword(s):  
Heat Transfer ◽  
Topology Optimization ◽  
Objective Function ◽  
Heat Dissipation ◽  
Heat Conduction Problem ◽  
Internal Heat ◽  
Computation Time ◽  
Optimality Criteria ◽  
Gradient Field ◽  
Optimal Topology

The standard volume to point heat conduction problem is used to determine the optimal topology that maximises the heat transfer. Unlike the constructed theory, the present investigation considers heat dissipation potential function as the objective function, whose gradient field is taken as the criterion for allocation of the limited amount of high conductivity material over the domain. The considered domain is rectangular in geometry, where all its sides are insulated except a centrally located patch on one of the sides, which is maintained at a constant temperature. FEM is used to compute the temperature and it is supplied to the topology optimization algorithm to determine the distribution of material with varied thermal conductivity over the domain. Grid independency is performed for five different grid sizes varying from 10x10 to 90x90. The variation of computation time and objective function with mesh refinement is reported.

Multistage topology optimization of induction heating apparatus in time domain electromagnetic field with magnetic nonlinearity

2019 ◽  
Vol 38 (3) ◽  
pp. 1009-1022
Author(s):  
Hiroshi Masuda ◽  
Yoshifumi Okamoto ◽  
Shinji Wakao
Keyword(s):  
Finite Element ◽  
Topology Optimization ◽  
Induction Heating ◽  
Time Domain ◽  
Computation Time ◽  
Design Domain ◽  
Element Mesh ◽  
Content Type ◽  
Actual Application ◽  
Application Model

Purpose The purpose of this paper is to solve efficiently the topology optimization (TO) in time domain problem with magnetic nonlinearity requiring a large-scale finite element mesh. As an actual application model, the proposed method is applied to induction heating apparatus. Design/methodology/approach To achieve TO with efficient computation time, a multistage topology is proposed. This method can derive the optimum structure by repeatedly reducing the design domain and regenerating the finite element mesh. Findings It was clarified that the structure derived from proposed method can be similar to the structure derived from the conventional method, and that the computation time can be made more efficient by parameter tuning of the frequency and volume constraint value. In addition, as a time domain induction heating apparatus problem of an actual application model, an optimum topology considering magnetic nonlinearity was derived from the proposed method. Originality/value Whereas the entire design domain must be filled with small triangles in the conventional TO method, the proposed method requires finer mesh division of only the stepwise-reduced design domain. Therefore, the mesh scale is reduced, and there is a possibility that the computation time for TO can be shortened.

Faster prediction of wildfire behaviour by physical models through application of proper orthogonal decomposition

2016 ◽  
Vol 25 (11) ◽  
pp. 1181 ◽  
Author(s):  
Elisa Guelpa ◽  
Adriano Sciacovelli ◽  
Vittorio Verda ◽  
Davide Ascoli
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Physical Model ◽  
Computational Cost ◽  
Computation Time ◽  
Order Reduction ◽  
Orthogonal Decomposition ◽  
Physical Models ◽  
Reduction Techniques ◽  
Rate Of Spread ◽  
Proper Orthogonal

Physical models of wildfires are of particular interest in fire behaviour research and have applications in firefighting, rescue and evacuation. However, physical models present a challenge as a result of the large computational resources they often require, especially for the analysis of large areas or when multiple scenarios are investigated. The objective of this paper is to explore the opportunity to reduce the computation time requested by physical wildfire models through application of a model order reduction technique, specifically the proper orthogonal decomposition (POD) technique. POD is here applied to a simple one-dimensional physical model. The full physical model for illustration of the concept is first tested with experimental data to check its ability to simulate wildfire behaviour; it is then reduced using the POD technique. It is shown that the reduced model is able to simulate fire propagation with only small deviations in results in comparison with the physical model (~6.4% deviation in the rate of spread, ROS) and a drastic reduction (~85%) in computational cost. The results demonstrate the advantages of applying effective reduction techniques to create new generations of fire models based on reduced physical approaches. The potential applicability of POD to more complex models is also discussed.

Design of Compliant Mechanisms for Amplification of Induced Strain Actuators

1999 ◽  
Author(s):  
Shawn Canfield ◽  
Mary I. Frecker
Keyword(s):  
Topology Optimization ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Design Procedure ◽  
Computation Time ◽  
Mechanical Efficiency ◽  
Optimality Criteria ◽  
Optimization Approach ◽  
Detail Design ◽  
Problem Formulations

Abstract The focus of this paper is on designing compliant mechanism amplifiers for piezoelectric actuators using a topology optimization approach. Two optimization formulations are developed: one in which the overall stroke amplification or geometric advantage (GA) is maximized, and another where the mechanical efficiency (ME) of the amplifier is maximized. Two solution strategies are used, Sequential Linear Programming (SLP) and an Optimality Criteria method, and results are compared with respect to computation time and mechanism performance. Design examples illustrate the characteristics of both problem formulations, and physical prototypes have been fabricated as proof of concept. An automated detail design procedure has also been developed which allows the topology optimization results obtained in MATLAB to be directly translated into a neutral 3-D solid geometry format for import into other CAE programs.

Topology Optimization Taking Into Account Geometrical Constraint of No-Closed Hole for Additive Manufacturing Based on Fictitious Physical Model Concept

2021 ◽  
Author(s):  
Takayuki Yamada ◽  
Yuki Noguchi
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Physical Model ◽  
Optimization Method ◽  
Geometrical Constraint ◽  
The Finite Element Method ◽  
Model Concept ◽  
Optimization Framework ◽  
Proposed Model ◽  
Topology Optimization Method

Abstract This paper proposes a topology optimization method that considers the geometrical constraint of a non-closed hole for additive manufacturing based on the fictitious physical model concept. First, the basic topology optimization concept and level set-based method are introduced. Second, the concept of a fictitious physical model for geometrical constraint in the topology optimization framework is discussed. Then, the model for the geometrical constraint of a non-closed hole for additive manufacturing is proposed. Numerical examples are provided to validate the proposed model. In addition, topology optimization considering this geometrical constraint is formulated, and topology optimization algorithms are constructed using the finite element method. Finally, optimization examples are provided to validate the proposed topology optimization method.

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