@article{taassob_kumar_gitushi_ranade_echekki_2024, title={A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements}, volume={429}, ISSN={["1879-2138"]}, url={https://doi.org/10.1016/j.cma.2024.117163}, DOI={10.1016/j.cma.2024.117163}, journal={COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING}, author={Taassob, Arsalan and Kumar, Anuj and Gitushi, Kevin M. and Ranade, Rishikesh and Echekki, Tarek}, year={2024}, month={Sep} }
 @article{rather_vallabhuneni_pyrch_barrubeeah_pillai_taassob_castellano_kota_2024, title={Color morphing surfaces with effective chemical shielding}, volume={15}, ISSN={["2041-1723"]}, url={https://doi.org/10.1038/s41467-024-48154-y}, DOI={10.1038/s41467-024-48154-y}, abstractNote={Abstract Color morphing refers to color change in response to an environmental stimulus. Photochromic materials allow color morphing in response to light, but almost all photochromic materials suffer from degradation when exposed to moist/humid environments or harsh chemical environments. One way of overcoming this challenge is by imparting chemical shielding to the color morphing materials via superomniphobicity. However, simultaneously imparting color morphing and superomniphobicity, both surface properties, requires a rational design. In this work, we systematically design color morphing surfaces with superomniphobicity through an appropriate combination of a photochromic dye, a low surface energy material, and a polymer in a suitable solvent (for one-pot synthesis), applied through spray coating (for the desired texture). We also investigate the influence of polymer polarity and material composition on color morphing kinetics and superomniphobicity. Our color morphing surfaces with effective chemical shielding can be designed with a wide variety of photochromic and thermochromic pigments and applied on a wide variety of substrates. We envision that such surfaces will have a wide range of applications including camouflage soldier fabrics/apparel for chem-bio warfare, color morphing soft robots, rewritable color patterns, optical data storage, and ophthalmic sun screening.}, number={1}, journal={NATURE COMMUNICATIONS}, author={Rather, Adil Majeed and Vallabhuneni, Sravanthi and Pyrch, Austin J. and Barrubeeah, Mohammed and Pillai, Sreekiran and Taassob, Arsalan and Castellano, Felix N. and Kota, Arun Kumar}, year={2024}, month={May} }
 @article{wang_vahabi_taassob_pillai_kota_2024, title={On-Demand, Contact-Less and Loss-Less Droplet Manipulation via Contact Electrification}, ISSN={["2198-3844"]}, DOI={10.1002/advs.202308101}, abstractNote={Abstract}, journal={ADVANCED SCIENCE}, author={Wang, Wei and Vahabi, Hamed and Taassob, Arsalan and Pillai, Sreekiran and Kota, Arun Kumar}, year={2024}, month={Jan} }
 @article{taassob_echekki_2023, title={Derived scalar statistics from multiscalar measurements via surrogate composition spaces}, volume={250}, ISSN={["1556-2921"]}, url={https://doi.org/10.1016/j.combustflame.2023.112641}, DOI={10.1016/j.combustflame.2023.112641}, abstractNote={Multiscalar point and line measurements have provided a wealth of data to extract measured scalars’ statistics in laboratory flames. These measurements are partial, since only a subset of scalars are measured, and carry experimental uncertainty to the degree that derived scalars, such as reaction rates, cannot be adequately evaluated from the raw data alone. In this study, we propose and investigate a method to extract derived scalar statistics via a surrogate composition space. This space is parameterized using principal components (PCs) from principal component analysis (PCA). The resulting low-dimensional manifold based only on statistics from measured scalars is complemented with homogeneous chemistry calculations to recover missing species and evaluate species reaction rates. The method is validated using direct numerical simulation (DNS) data on which Gaussian noise is added to emulate experimental uncertainly and with a subset of major species, a radical OH, and temperature assumed to be known/measured. The results show the proposed procedure is able to recover unmeasured species and predict the species reaction rates.}, journal={COMBUSTION AND FLAME}, author={Taassob, Arsalan and Echekki, Tarek}, year={2023}, month={Apr} }
 @article{taassob_ranade_echekki_2023, title={Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements}, volume={10}, ISSN={["1520-5029"]}, url={https://doi.org/10.1021/acs.energyfuels.3c02410}, DOI={10.1021/acs.energyfuels.3c02410}, abstractNote={We develop a physics-informed neural network (PINN) to evaluate closure terms for turbulence and chemical source terms in the Sandia turbulent nonpremixed flames. The approach relies on temperature, major species, and velocity point measurements to develop closure for the transport of momentum and the thermo-chemical state, through principal components (PCs). The PCs are derived using principal component analysis (PCA) implemented on the measured thermo-chemical scalars. With the solution for the PCs, the number of governing equations and associated closure terms is reduced relative to the solution of the measured species and temperature. The PINNs are trained on two flame conditions, the so-called Sandia flames D and F, and are validated on an additional flame, flame E. In addition to the radial and axial spatial coordinates, the Reynolds number is prescribed as an additional input parameter. A relatively shallow network attached to the PINNs is used to relate the unconditional means of the PCs to the source terms in their transport equations. The results show that PCs, species, the mixture fraction, and the axial and radial velocity components can adequately be represented with PINNs compared to experimental statistics. Moreover, PINNs are able to reconstruct closure terms associated with turbulence and scalar transport, including the averaged PCs’ chemical source terms.}, journal={ENERGY & FUELS}, author={Taassob, Arsalan and Ranade, Rishikesh and Echekki, Tarek}, year={2023}, month={Oct} }