What is Machine learning?
Machine learning is an application of artificial intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed automatically. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
What is the Design of the Experiment (DOE)?
Design of Experiments (DOE) is also referred to as Designed Experiments or Experimental Design – is defined as the systematic procedure carried out under controlled conditions to discover an unknown effect, test or establish a hypothesis, or illustrate a known product. It involves determining the relationship between input factors affecting a process and its output. It helps to manage process inputs to optimize the result.
Sir Ronald A. Fisher first introduced the method in the 1920s and 1930s. Design of Experiment is a powerful data collection and analysis tool used in various experimental situations. It allows manipulating multiple input factors and determining their effect on the desired output (response). By changing numerous inputs simultaneously, DOE helps to identify significant interactions that may be missed when experimenting with only one factor at a time. We can investigate all possible combinations (full factorial) or only a portion of the possible combinations (fractional factorial).
A well-planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. However, this “one factor at a time” (OFAT) approach to process knowledge is inefficient compared to changing multiple factor levels simultaneously.
As was mentioned previously, there are multiple approaches to DOE. OFAT, Full and Fractional factorial, Taguchi and Response surface methodology (RSM). However, the RSM proves to be the best approach among the existing methods for DOE.
What is Response Surface Methodology (RSM)?
Response Surface methodology, or RSM for short, is a set of mathematical methods determining the relationship between one or more response variables and several independent (studied) variables. This method was introduced by Box and Wilson in 1951 and is still used today as an experimental design tool. RSM is a set of statistical techniques and applied mathematics for building experimental models. The goal in RSM is to optimize the response (output variable) affected by several independent variables (input variables). An experiment is a series of tests called executions. In each experiment, changes are made to the input variables to determine the causes of the response variable’s changes.
In response level designs, constructing response procedure models is an iterative process. As soon as an approximate model is obtained, it is tested by the good-fit method to see if the answer is satisfactory. If the answer is not confirmed, the estimation process starts again, and more experiments are performed. In designing experiments, the goal is to identify and analyze the variables affecting the outputs with the least number of experiments. This method achieves the best response surface by discovering each design variable’s optimal response level. In designing experiments, the goal is to identify and analyze the variables affecting the outputs with the least number of experiments.
What is Optimization in ANSYS Fluent?
Optimization is the process of acquiring the best answer for a selected parameter. Using ANSYS, one can perform two types of Optimization. 1-Direct Optimization, 2-Indirect Optimization. These methods will result in the same answer but with different steps. Direct Optimization predicts the behavior of a system without any intermediary step. In contrast, indirect Optimization needs the data obtained by the RSM to provide the user with the correct mathematical function for predicting the system behavior.
MR-CFD Machine learning/Optimization Services
With several years of experience simulating a wide range of problems in various CFD fields using ANSYS Fluent software, the MR-CFD team is ready to offer extensive modeling, meshing, and simulation services. Simulation Services for Machine learning/Optimization are categorized as follows:
- Direct and Indirect optimization process for different fluidic systems
- ML models for different projects to predict the behavior of the related system
- Calculating different mathematical correlations for predicting the behavior of various types of systems
- Obtaining the optimal state of a process inside a system for reducing the overall costs
- Designing various equipment for maximum efficiency
You may find the related products in the categories mentioned above in our CFD shop by clicking on the following link:
Our services are not limited to the mentioned subjects. The MR-CFD team is ready to undertake different and challenging projects in the Machine learning/Optimization field ordered by our customers. You can consult with our experts freely and without charge at first, and then order your project by sending the problem details to us using the following address.
By entrusting your project to the MR-CFD team, you will not only receive the related project’s files (Geometry, Mesh, Fluent files). Also, you will be provided with an extensive tutorial video demonstrating how you can create the geometry, mesh, and define the needed settings in the Fluent software all by yourself. And these all come with post-technical support from the MR-CFD team.