After years nobody is even able to predict if some AWES for electricity production can be viable, and what AWE method could be successful. There is progress in AWE R&D, but the time of utility-scale market is not still came. A lot of parameters shall be considered outside purely technical parameters.
Testing is a good thing, but trying all architectures with variants can take years and years. And now a lot of AWES are tested. So a finer analysis is required.
There is significant and growing data comprising tests and theoretic scientific studies. So modelling, using artificial intelligence (AI), could be a mean to define the outlines of a viable AWES.
Searchers can only explore part of AWE fields, but in compensation providing reliable and complete information on what they study, unlike discussions on AWE forums.
Let us imagine how AWE could progress as posts would argue with orders of magnitude, not even accurate data such as scientific papers.
Let us take an example on “scaling laws in AWES design” https://forum.awesystems.info/t/scaling-laws-in-awes-design/171: all the messages seem correct and interesting, but there are few given orders of magnitude. In the end the consensus is not easy. With a software comprising data enough the orders of magnitude could stand out automatically.
Collaborative projects like on Collaborative engineering projects could be improved by using AI as already evoked.
The technologic progress in China and some other countries comes from artificial intelligence for a large part.