How do you measure and improve the quality and efficiency of your aerodynamic shape optimization workflow?

1 Define your problem

The first step in any optimization workflow is to define your problem clearly and precisely. What are your design variables, objectives, and constraints? How do you parameterize your geometry and mesh? What are your aerodynamic models and solvers? How do you handle uncertainties and robustness? These are some of the questions that you need to answer before starting the optimization. Having a well-defined problem will help you avoid unnecessary iterations, errors, and inconsistencies, and will also make it easier to compare and evaluate different optimization methods and tools.

2 Choose your method

The next step is to choose the most suitable optimization method for your problem. There are many types of optimization methods, such as gradient-based, gradient-free, surrogate-based, multi-objective, multi-fidelity, and multi-disciplinary. Each method has its own advantages and disadvantages, depending on the characteristics of your problem, such as the number of design variables, the complexity of the objective function, the availability of gradients, the computational cost, and the desired accuracy. You should also consider the trade-off between exploration and exploitation, that is, how much you want to search the design space for new solutions versus how much you want to refine the existing ones.

3 Evaluate your results

The third step is to evaluate your results and check if they meet your expectations and requirements. How do you measure the quality and efficiency of your optimization? There are several metrics that you can use, such as the convergence history, the Pareto front, the hypervolume, the diversity, and the feasibility. These metrics can help you assess how well your optimization method has performed in terms of finding optimal or near-optimal solutions, covering the trade-off space, maximizing the improvement, maintaining the diversity, and satisfying the constraints. You should also verify and validate your results using high-fidelity models and experiments, if possible.

4 Analyze your data

The fourth step is to analyze your data and gain insights from your optimization. How do you understand and interpret your results? How do you communicate and present them to others? There are several tools and techniques that you can use, such as visualization, sensitivity analysis, dimensionality reduction, clustering, and machine learning. These tools and techniques can help you explore and discover patterns, trends, relationships, and anomalies in your data, as well as identify the most important design variables, objectives, and constraints. They can also help you generate and test new hypotheses, improve your models and solvers, and inform your future decisions.

5 Automate your workflow

The fifth step is to automate your workflow and make it more efficient and reliable. How do you streamline and integrate your optimization process? How do you manage and store your data? How do you monitor and control your optimization? There are several software and platforms that you can use, such as scripting languages, workflow engines, databases, cloud services, and web interfaces. These software and platforms can help you automate and standardize your workflow, reduce human errors and manual tasks, optimize the use of computational resources, and enable remote access and collaboration.

6 Update your knowledge

The sixth and final step is to update your knowledge and keep learning from your optimization experience. How do you document and share your findings and lessons learned? How do you keep up with the latest developments and innovations in aerodynamic shape optimization? There are several sources and channels that you can use, such as journals, conferences, books, courses, blogs, podcasts, and social media. These sources and channels can help you stay informed and inspired by the state-of-the-art research and practice in aerodynamic shape optimization, as well as connect and network with other experts and enthusiasts in the field.

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?