In its truest essence, MDO means optimizing a design, within a set design space, across multiple disciplines by computer. For those unfamiliar with it, a good example from the automotive industry is a body-in-white (BIW). Many disciplines are involved in the design of a car body, each with their own objectives and requirements. For example, a stylist might suggest a certain shape that looks good but is aerodynamically inefficient. Or the crash safety team could propose a certain material that helps to achieve a 5 star crash rating more easily but might be poor from an NVH perspective. And so on….so the key in MDO is not necessarily optimizing the performance of a particular discipline but rather trading and balancing the competing requirements of individual disciplines to optimize the overall system and find the best compromise.
In MDO, we can distinguish single-objective (e.g. minimize weight) and multi-objective optimization (e.g. minimize weight and maximize strength). In case of multi-objective optimization, the key is finding the designs that comprise the pareto frontier. This is where MDO frameworks come into play. If the optimization problem is small enough, i.e. one discipline, one objective and only a handful of independent design variables, an experienced engineer may be able to ‘manually’ optimize the design. Most real world applications however are more complex, thus necessitating smart algorithms to explore the design space and quickly narrow down to the region of interest, and ultimately finding the (set of) optimal design(s).
An example of a single-objective and multi-objective optimization is shown below.
Mathematically, a sensitivity is a partial derivative of some measure of the performance of a product, such as the aerodynamic drag or the stress at a point, with respect to a parameter representing a physical property of the product or its environment. In this context sensitivity analysis provides a measure of the effect of small perturbations, local to the initial design. Read more in this NAFEMS pamphlet.
As the complexity of design solutions increases it becomes difficult to define the"worst case scenario” and identify the appropriate reserve margin for any given situation. This results in situations where designs are over- or under-designed. The use of UQ techniques addresses these issues directly. Read more in this NAFEMS pamphlet.
Read this article on the benefits of server-based MDO compared to desktop solutions, with some lessons learned from the AFRL EXPEDITE program!
This paper presents the roadmap for the development of the new wingtip for the EMBRAER 175 aircraft, and how MultiDisciplinary Optimization (MDO) was applied on its definition and design.
Research conducted in parallel with the Air Force Research Laboratory’s (AFRL’s) Expanded Multidisciplinary Design Optimization (MDO) for Effectiveness Based Design Technologies (EXPEDITE) program is presented. Special consideration is given to detailing the design, components, and execution of a modern aircraft conceptual MDO process within the ESTECO modeFRONTIER® and VOLTA® software products.
Multidisciplinary Optimization in Aerospace: Enable Real Change
Higher complexity doesn't imply higher cost
ADAS (Advanced Driving Assistance Systems ) and AD (Autonomous Driving) systems are the next big frontier for automotive companies. The challenge lays in finding the right balance between minimizing the number of accidents and casualties while maximizing the comfort of traveling in complex conditions.
ESTECO President, Carlo Poloni shares some ingredients that have proven to be essential for effective decision making in the digital world.
What good is a revolution if it doesn’t result in useful change? See how companies are achieving measurable improvements in time savings, simulation data…
The CAASE Conference in Cleveland, June 5-7, will feature a wealth of presentations on the challenges and ROI of real-world democratization efforts at a number of companies in the US and elsewhere.