An Isogeometric Cloth Simulation Based on Fast Projection Method

A novel continuum-based fast projection scheme is proposed for cloth simulation.Cloth geometry is described by NURBS,and the dynamic response is modeled by a displacement-only Kirchhoff-Love shell element formulated directly on NURBS geometry.The fast projection method,which solves strain limiting as a constrained Lagrange problem,is extended to the continuum version.Numerical examples are studied to demonstrate the performance of the current scheme.The proposed approach can be applied to grids of arbitrary topology and can eliminate unrealistic over-stretching efficiently if compared to spring-based methodologies.

Research on Clothing Simulation Design Based on Three-Dimensional Image Analysis

Traditional clothing design models based on adaptive meshes cannot reflect.To solve this problem,a clothing simulation design model based on 3D image analysis technology is established.The model uses feature extraction and description of image evaluation parameters,and establishes the mapping relationship between image features and simulation results by using the optimal parameter values,thereby obtaining a three-dimensional image simulation analysis environment.On the basis of this model,by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes,the cutting form and actual cutting effect of clothing are determined to construct a design model.The simulation results show that compared with traditional clothing design models,clothing simulation design based on 3D image analysis technology has a better effect,with the definition of fabric folds increasing by 40%.More striking contrast between light and dark,the resolution increasing by 30%,and clothing details getting a more real manifestation.

Parallel Cloth Simulation Using OpenGL Shading Language

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of physic.In general,the mass–spring system is applied to real-time cloth simulation with three types of springs.However,hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness coefficient.Furthermore,to obtain stable behavior,constraint enforcement is used instead of maintenance of the force of each spring.Constraint force computation involves a large sparse linear solving operation.Due to the large computation,we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of cloth.At the same time,when the length of the spring is stretched or compressed over a defined threshold,adaptive constraint activation and deactivation method deactivates the spring and generate the implicit constraint.Traditional method that uses a serial process of the Central Processing Unit(CPU)to solve the system in every frame cannot handle the complex structure of cloth model in real-time.Our simulation utilizes the Graphic Processing Unit(GPU)parallel processing with compute shader in OpenGL Shading Language(GLSL)to solve the system effectively.In this paper,we design and implement parallel method for cloth simulation,and experiment on the performance and behavior comparison of the mass-spring system,constraint enforcement,and adaptive constraint activation and deactivation techniques the using GPU-based parallel method.

Predicting the clothing insulation through machine learning algorithms:A comparative analysis and a practical approach

Since indoor clothing insulation is a key element in thermal comfort models,the aim of the present study is proposing an approach for predicting it,which could assist the occupants of a building in terms of recommendations regarding their ensemble.For that,a systematic analysis of input variables is exposed,and 13 regression and 12 classification machine learning algorithms were developed and compared.The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain.Outdoor temperature at 6 a.m.,indoor air temperature,indoor relative humidity,comfort temperature and gender were the most relevant features for predicting clothing insulation.When comparing machine learning algorithms,decision tree-based algorithms with Boosting techniques achieved the best performance.The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.