Optimization & Uncertainty
Achieve design goals in record time and six sigma quality with our optimization and uncertainty quantification services
What is the best design? How safe or reliable is it? How well does the model predict reality? How much confidence do I have in my answer? These are the questions we help answer.
Computational methods developed in structural mechanics, heat transfer, fluid mechanics, electrodynamics and many other fields of engineering can be an enormous aid to understanding the complex physical systems they simulate. Often, it is desired to use these simulations as virtual prototypes to obtain an acceptable or optimized design for a particular system. Recent advances in artificial intelligence and numerical algorithms now allow for not only single-point predictions, but also for automated determination of system performance improvements throughout the product life cycle.
System performance objectives can be formulated to:
- minimize weight, cost, or defects;
- limit a critical temperature, stress, or vibration response;
- maximize performance, reliability, throughput, reconfigurability, agility, or design robustness
The figure below conceptualizes our optimization process which, as all of our work, is performed in close consultation with our clients in order to understand Key Performance Indicators (KPIs), design constraints and trade-offs.
Predictive models of industrial technologies are challenging because of the large number of coupled physical phenomena that must be addressed. In addition to this intrinsic complexity, model uncertainty must be accounted for in any analysis if the model will be used to facilitate design or operational decisions which may impact safety. Uncertainty analysis investigates the impacts of uncertain inputs on the final output and aims to answer the question, “How do uncertainties in model factors propagate to uncertainties in model outputs?”. Get in touch to discuss how uncertainty analyses can improve your confidence in real world performance expectations.