Fluid dynamics, CFD, science, HPC and industry views
Fit for purpose CFD
Understandably, a lot of effort goes in to making sure CFD gives accurate predictions, and of course there's a lot of scope for figuring out what accurate enough actually looks like. In this blog we'll discuss what we mean by fit for purpose CFD and how that cuts through a lot of work to get to answers that are useful in the shortest possible time.
For anything but the most straight forward of flows (in which case you may as well figure it out with pencil and paper), CFD takes some time and effort to provide useful output due to the complexities of the geometry involved, turbulence, multiple phases and so on. The important question is how much of that complexity is important you, and to know that you need ti be really clear about what information you are trying to get from a simulation. You might be faced with a flow induced vibration problem at certain points in your piping network and want to simulate how making some different pipe geometry choices will affect the vibration frequency. The flow is unsteady, turbulent and multiphase and you face some tricky choices of which models to use, and you don't really have time for thorough validation - you need to show your model captures the existing vibration frequency straight out of the oven and can therefore predict geometry changes effectively. If the phases in the flow are well dispersed, it may be sensible to model the flow as single phase with a representative density, removing the multiphase physics and simplifying the choice and implementation of turbulence model. The simulation can now deliver predictions in the timescales required. Now, the critical part here is to go back and look at the simulation data and analyse it to see if the single phase assumptions holds - are there any characteristics of the flow that suggest it will no longer be well dispersed, in the critical areas? Of course, if you don't match the measured vibration frequency then you know you've missed something, but if you do you need to be sure it's a well founded agreement, and that when you simulate the modified pipework you go back and check your data again for the validity of the assumptions.
For us, this is the essence of fit for purpose CFD - making helpful assumptions and constantly checking the data to see if they hold, and if they don't, working out how much they affect the information being used from the model - asking and understanding "what physics are important here?". In order to answer this it's critical to know whether the CFD is expected to give accurate quantitative information or qualitative shifts. In the vibration example above, the requirement might be to increase the vibration frequency above the structural resonance frequency, in which case a qualitative shift would suffice. It might be to increase the frequency away from the first structural resonance but not enough to run into the next mode, in which case the results must be sufficiently quantitatively accurate.
In the product development cycle, simulation delivers the best value early on, in the concept development and assessment phases where varied physical prototype testing is tricky and costly. A reduced set of physical tests linked to CFD through specific validation points allows the simulation to be well validated and deliver important performance characteristics while design philosophies are evolving. The timescales here are more favourable for detailed validation and the importance of real data for this purpose is often well appreciated given the nature of the decisions being taken early in the design cycle. Nonetheless, it's unlikely that the most physically complete simulation will be run for all design considerations, especially for complex products where the full set of physics would require long simulate times and extensive hardware performance. These days the cloud provides the hardware, but product development timescales are always pressured, so being able to run sub-models for specific aspects makes a lot of sense. Here our approach of constantly assessing the physics in light of the required information works well with regular referral to and modification of the experimental programme.
CFD is often required to solve problems later on in the design cycle, when architecture decisions have been made and there are some performance issues to resolve. This is challenging for simulation as the problems are often not well addressed by experiment, hence the requirement for simulation to provide some additional information not available from test. The opportunities for validation here are limited as it's the extra information from CFD that is required. Timescales are often tight as it's a problem solving scenario and fixes are required fast. The broad and flexible validation link to experimental programmes that's well placed in concept design and development often doesn't fit here. In this case its critical to understand exactly what information is required and use that to assess what physics are likely to be important and tailor the CFD to provide answers in as short a time as possible. It's in these sorts of cases where our experience in physics selection and continuous analysis adds real value to enable CFD to provide answers to difficult questions in challenging timescales.
If you have requirements for effective simulation applied at any stage of your product development process get in touch and we'll use our experience of fit for purpose CFD to help.