Multidisciplinary Optimization (MDO) is the branch of engineering that uses optimization algorithms to identify solutions that maximize or minimize certain performance parameters, taking into account the constraints and objectives of the system, equipment or process.
The strategy allows engineers to analyze complex systems with a large number of design variables, evaluating a mathematical model in different configurations, and iteratively evolving the solution to the configuration that presents the best performance indicators.
Performance models can contain from simple analytical equations in an Excel spreadsheet, to complex numerical simulations coupled with different software and platforms such as CFD and FEA.
Defining the dimensions of a mechanical part with maximum strength and minimum weight
Determine the best aerodynamic configuration for an aircraft to maximize range and minimize fuel consumption.
Select the best route for a vehicle to take to minimize time to destination.
First, you create a set of initial design variables, called the initial condition. These initial conditions are evaluated by the mathematical model and performance indicators are calculated for each configuration. The optimization algorithm analyzes the performance of each configuration and defines, through performance comparisons, a new set of design variables. This new set of configurations is evaluated again by the performance model, and the process is repeated iteratively until the best solutions are found.
There are several optimization strategies and algorithms, which scan the search universe in different ways such as:
● Genetic Algorithm
● Particle Swarm
● Simulated Annealing
Optimization algorithms can be divided into single or multi-objective:
Evaluate the search universe with respect to a single objective, determined by the cost function. This objective can be to maximize or minimize a given performance indicator, or even a combination of performance indicators grouped and weighted together. The result of this analysis is a single combination of design variables that represent the configuration with the best performance, according to the defined cost function.
Evaluate the search universe against two or more goals, and result in a set of settings that perform best against the selected goals. This set of optimal configurations is called Pareto Solution, and is formed by system solutions where, to improve performance in relation to one of the objectives, it is necessary to degrade one or more of the other objectives. An engineering analysis or trade-off is performed to select the most suitable solution among the configurations found in Pareto's solution.
ATS uses iChrome Nexus and Python as optimization platforms. Both have different algorithms implemented, and can be used to connect different software and platforms, creating a performance model according to the needs of the system, equipment or process.
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