Having explained the background on the most critical aspects of the phenomenon under investigation in this thesis, it is now time to give a general overview of the whole approach followed and the methodology used in order to try and solve the problem, hence answering our main research question. The methodology described below have been shaped based on selected literature studies that are referred above for tensegrity systems simulations, FFD simulation of wind flow in outdoor spaces (Chronis et al., 2011) and pedestrian wind comfort criteria as well as a general understanding of the issue itself developed during literature review. This work aims to propose a method that could be a useful tool for researchers and urban planners who are dealing with the real city environmental problem of “wind funnel effect” in existing outdoor spaces and it tries to give an answer improving pedestrian wind comfort criteria introducing tensegrity systems as the design tool to address it.
The first step in methodology followed is to create an environment that models the tensegrity unit and implement Genetic Algorithms (GA) in order to find the optimum unit with regards to effectiveness in deflecting the wind. Following that, next step is to create an environment that recreates the tunneling effect as accurate as possible using Fast Fluid Dynamics. Having both our design tool (tensigrity unit) and our environment (simulation exhibiting the tunneling effect) available, next step is to try and actually address the phenomenon. The approach followed in this step is to work on specific areas facing the most problematic locations and implement GA in order to find optimal tensegrity structure that would solve the issue. What we are actually after in this step is find what are the “windbreak features” (height, width, porosity) the tensegrity structure should have in order to resolve the tunneling effect. Final step is to actually build the structure with features obtained from previous step using tensegrity units from initial step. All steps are thoroughly explained in below sections (Fig_22).
Many Software tools were used in this process but main work has been implemented in processing.js environment. Majority of other tools were used mainly during the process of understanding how wind behaves in a computational environment in general as well understand the phenomenon itself. Tools used consist of Maya 3D computer graphics, Ecotect Analysis and Vassari. As already explained above other tools usage was complementary, as the whole process and simulations were implemented in Processing.js.
The first step in methodology followed is to create an environment that models the tensegrity unit and implement Genetic Algorithms (GA) in order to find the optimum unit with regards to effectiveness in deflecting the wind. Following that, next step is to create an environment that recreates the tunneling effect as accurate as possible using Fast Fluid Dynamics. Having both our design tool (tensigrity unit) and our environment (simulation exhibiting the tunneling effect) available, next step is to try and actually address the phenomenon. The approach followed in this step is to work on specific areas facing the most problematic locations and implement GA in order to find optimal tensegrity structure that would solve the issue. What we are actually after in this step is find what are the “windbreak features” (height, width, porosity) the tensegrity structure should have in order to resolve the tunneling effect. Final step is to actually build the structure with features obtained from previous step using tensegrity units from initial step. All steps are thoroughly explained in below sections (Fig_22).
Many Software tools were used in this process but main work has been implemented in processing.js environment. Majority of other tools were used mainly during the process of understanding how wind behaves in a computational environment in general as well understand the phenomenon itself. Tools used consist of Maya 3D computer graphics, Ecotect Analysis and Vassari. As already explained above other tools usage was complementary, as the whole process and simulations were implemented in Processing.js.
Fig_22