  Next generation architectures necessitate a shift away from traditional
workflows in which the simulation state is saved at prescribed frequencies for
post-processing analysis. While the need to shift to in~situ workflows has been
acknowledged for some time, much of the current research is focused on static
workflows, where the analysis that would have been done as a post-process is
performed concurrently with the simulation at user-prescribed frequencies.
Recently, research efforts are striving to enable adaptive workflows, in which
the frequency, composition, and execution of computational and data
manipulation steps dynamically depend on the state of the simulation. Adapting
the workflow to the state of simulation in such a data-driven fashion puts
extremely strict efficiency requirements on the analysis capabilities that are
used to identify the transitions in the workflow. In this paper we build upon
earlier work on trigger detection using sublinear techniques to drive adaptive
workflows. Here we propose a methodology to detect the time when sudden heat
release occurs in simulations of turbulent combustion. Our proposed method
provides an alternative metric that can be used along with our former metric to
increase the robustness of trigger detection. We show the effectiveness of our
metric empirically for predicting heat release for two use cases.
