mSense is a Python package developed for Multidisciplinary Analysis and Optimization (MDAO). It allows the user to quickly setup and solve MDAO problems, and makes it easy to switch between various Multidisciplinary Analysis (MDA) methods and Multidisciplinary Optimization (MDO) architectures. MSense is written with an object-oriented approach and can be used through its Python Application Programming Interface (API). A basic user guide can be found here
Supported MDO architectures
Currently, the following MDO architectures are implemented in mSense:
- Multidisciplinary Feasible (MDF)
- Individual Discipline Feasible (IDF)
- Collaborative Optimization (CO)
A comparison of the mSense implementation of the above architectures was performed in my diploma thesis, based on Sellar’s problem.
Aerostructural wing optimization example
The simultaneous aerodynamic and structural optimization of aircraft wings is a classic MDO problem. In my diploma thesis, I used mSense to optimize the shape of the ONERA M6 wing (parameterized with volumetric NURBS), along with its structure, which was described using a simple beam finite-element model. The optimization objective was to increase the wing’s lift-to-drag ratio, while respecting a stress constraint, resulting from the structural model. The problem was setup and solved in mSense, using the MDF architecture.
