Strengths and weaknesses of solution mapping

Here we examine the strengths and weaknesses of the key features of SM, those that have implications to the practice of the technique. We begin with the strengths  

The decoupling offers an additional beneht of storing the surrogates for their subsequent reuse. Considering the computational speed of optimization with algebraic surrogates, this can enable on-the-fly operation, in support of a collaborative analysis and development of chemical reaction models. Such protocols were indeed developed by the GRI-Mech project team [1] and are planned for PrIMe [46]. 

The use of direct numerical simulations for development of surrogate models allows one to consider any observable property or a combination of them as a modeled response. For instance, in addition to species concentrations, typical of most studies, SM can handle with a similar ease also induction times, peak properties (e.g., location, height, width), peak relative positions or their ratios, or peak ratios in different experiments, e.g., testing the effect of a mixture additive. Likewise, any parameter or combination of them can serve as optimization variables. Examples may include, in addition, the typically discussed rate constants [30], parameters of rate coefficient expressions (such as activation energies) [2], and species enthalpies of formation [1,4]. 

The response-surface technique outlined above becomes less practical for a number of active variables exceeding --20. How can one deal with a significantly larger number of parameters Before 

Currently, one of the most developed, hence most illustrative, examples of practical application of SM is provided by the GRI-Mech project [1]. In its latest release, the GRI-Mech 3.0 dataset is comprised of 53 chemical species and 325 chemical reactions (with a combined set of 102 active variables), and 77 peer-reviewed, well-documented, widely trusted experimental observations obtained in high-quality laboratory measurements, carried out under different physical manifestations and different conditions (such as temperature, pressure, mixture composition, and reactor conhguration). The experiments have relatively simple geometry, leading to reliably modeled transport of mass, energy, and momentum. Typical experiments involve flow-tube reactors, stirred reactors, shock tubes, and laminar premixed flames, with outcomes such as ignition delay, flame speed, and various species concentration properties (location of a peak, peak value, relative peaks, etc.). 

---