What are the latest aerodynamic optimization methods for aircraft design?

2 Surrogate Modeling

Surrogate modeling is the use of simplified mathematical models to approximate the relationship between the design variables and the objective function of an optimization problem. Surrogate models can reduce the computational cost and time of optimization by replacing the expensive and complex evaluations of the original problem with cheaper and faster ones. Surrogate models can be based on various techniques, such as polynomial regression, artificial neural networks, kriging, or radial basis functions. Surrogate models can also be updated and refined as more data becomes available from the original problem.

3 Multi-Objective Optimization

Multi-objective optimization is the process of finding the optimal trade-offs between two or more conflicting objectives of an optimization problem. For example, an aircraft designer may want to minimize the drag and maximize the lift of an aircraft, but these two objectives are usually inversely related. Multi-objective optimization can help the designer find a set of solutions that are Pareto-optimal, meaning that no solution can improve one objective without worsening another. Multi-objective optimization can also help the designer visualize and analyze the trade-offs and preferences among the objectives.

4 Gradient-Based Optimization

Gradient-based optimization is the use of mathematical methods that rely on the gradient or the derivative of the objective function to find the optimal solution of an optimization problem. Gradient-based methods can be fast and efficient, especially for smooth and convex problems. However, gradient-based methods can also be sensitive to noise, local optima, and initial conditions. Gradient-based methods can be classified into two types: first-order methods, which use only the first derivative of the objective function, and second-order methods, which use both the first and second derivatives.

5 Evolutionary Algorithms

Evolutionary algorithms are a class of optimization methods that are inspired by the principles of natural evolution, such as selection, mutation, crossover, and reproduction. Evolutionary algorithms can explore a large and diverse search space and find multiple optimal or near-optimal solutions. Evolutionary algorithms can also handle nonlinear, noisy, and discontinuous problems. However, evolutionary algorithms can also be computationally intensive and require tuning of several parameters. Evolutionary algorithms can be divided into several categories, such as genetic algorithms, differential evolution, particle swarm optimization, and ant colony optimization.

6 Adjoint-Based Optimization

Adjoint-based optimization is a technique that combines CFD and gradient-based methods to efficiently compute the sensitivity of the objective function to the design variables. Adjoint-based optimization uses the adjoint equation, which is derived from the original CFD equation, to calculate the gradient of the objective function with respect to all the design variables in one step. Adjoint-based optimization can significantly reduce the computational cost and time of optimization, especially for problems with a large number of design variables. However, adjoint-based optimization can also be complex and challenging to implement and require high accuracy and stability of the CFD solver.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?