A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.
A data–informed approach is presented with the objective of quantifying errors and uncertainties in the functional forms of turbulence closure models. The approach creates modeling information from higher-fidelity simulations and experimental data. Specifically, a Bayesian formalism is adopted to infer discrepancies in the source terms of transport equations. A key enabling idea is the transformation of the functional inversion procedure (which is inherently infinite-dimensional) into a finite-dimensional problem in which the distribution of the unknown function is estimated at discrete mesh locations in the computational domain. This allows for the use of an efficient adjoint-driven inversion procedure. The output of the inversion is a full-field of discrepancy that provides hitherto inaccessible modeling information. The utility of the approach is demonstrated by applying it to a number of problems including channel flow, shock-boundary layer interactions, and flows with curvature and separation. In all these cases, the posterior model correlates well with the data. Furthermore, it is shown that even if limited data (such as surface pressures) are used, the accuracy of the inferred solution is improved over the entire computational domain. The results suggest that, by directly addressing the connection between physical data and model discrepancies, the field inversion approach materially enhances the value of computational and experimental data for model improvement. The resulting information can be used by the modeler as a guiding tool to design more accurate model forms, or serve as input to machine learning algorithms to directly replace deficient modeling terms.
A data-driven approach to the modeling of turbulent and transitional flows is proposed in this work, with the goal of developing more robust and accurate closure models. The key idea is to (i) infer the functional form of deficiencies in known closure models by applying inverse problems to computational and experimental data, (ii) use machine learning to reconstruct the improved functional forms, and (iii) to inject the improved functional forms in simulations to obtain more accurate predictions. The inverse modeling step, on its own, can yield valuable insight to the modeler, essentially converting data to information. The machine learning step is a tool to convert information into modeling knowledge. Representative examples are used to describe the methodology and to demonstrate its viability. The first example investigates the modeling of a non-equilibrium turbulent boundary layer, and the second involves the modeling of bypass transition to turbulence. Evidence from these problems emphasizes the utility of the proposed approach in offering new routes to closure modeling in general computational physics disciplines.
The ongoing worldwide pandemic due to COVID-19 has created awareness toward ensuring best practices to avoid the spread of microorganisms. In this regard, the research on creating a surface which destroys or inhibits the adherence of microbial/viral entities has gained renewed interest. Although many research reports are available on the antibacterial materials or coatings, there is a relatively small amount of data available on the use of antiviral materials. However, with more research geared toward this area, new information is being added to the literature every day. The combination of antibacterial and antiviral chemical entities represents a potentially path-breaking intervention to mitigate the spread of disease-causing agents. In this review, we have surveyed antibacterial and antiviral materials of various classes such as small-molecule organics, synthetic and biodegradable polymers, silver, TiO2, and copper-derived chemicals. The surface protection mechanisms of the materials against the pathogen colonies are discussed in detail, which highlights the key differences that could determine the parameters that would govern the future development of advanced antibacterial and antiviral materials and surfaces.
In this study, we demonstrate how increasing the number of crystallization trials can help crystallize polymorphs which may not be obtained in a fewer number of trials due to statistical reasons. Crystallization experiments were conducted using patterned substrates of self-assembled monolayers (SAMs) with solutions of 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (known as ROY for its red, orange, and yellow crystals) in dimethylsulfoxide (DMSO). The patterned bifunctional surface was immersed and slowly withdrawn from undersaturated solutions. The solution preferentially wetted the metallic islands, and as the solvent evaporated, ROY crystals exclusively nucleated on the lyophilic metallic islands. Raman microscopy was utilized to characterize the crystalline form on each metallic island. In one of the experiments, over 10 000 islands were analyzed, and we calculated the probability of crystallizing a particular polymorph on an island. We were able to crystallize six of the seven stable polymorphs of ROY using this method, including form YT04, which to the best of our knowledge, has never been obtained from solution crystallization.
Aim:Cognitive deficits have been presupposed to be endophenotypic markers in bipolar disorder, but few studies have ascertained the cognitive deficits in healthy relatives of bipolar disorder patients. The aim of the present study was to assess the cognitive functions of first-degree relatives of patients with bipolar disorder and compare them with healthy controls.Methods: Ten first-degree apparently healthy relatives of patients with bipolar disorder were compared with 10 age-and education-matched control subjects on computer-based cognitive tests.Results: As compared to the control group, the relatives group performed significantly poorly on tests for executive function and vigilance, while on the test for working memory the performance was not significantly different on most of the parameters. Conclusions:Executive functioning and vigilance could be potential markers of the endophenotype in bipolar patients.
Polymorph screening studies of sulfathiazole, mefenamic acid, flufenamic acid, and ROY were carried out using a semi-automated apparatus. Cooling crystallization and slurry aging experiments were conducted with varying process conditions and a selection of 16 diverse solvents to find as many polymorphic forms as possible. Results yielded four out of five polymorphs of sulfathiazole, both polymorphs and a solvate of mefenamic acid, four out of the seven stable forms of ROY, as well as the two most commonly encountered polymorphs and a solvate of flufenamic acid. The results obtained in this study were compared with a novel high throughput method based on patterned substrates of self-assembled monolayers. 17,32,38 It was shown that in the case of sulfathiazole and mefenamic acid the same number of polymorphs were obtained using the two approaches. In the case of ROY, the semi-automated approach was not able to produce three of the forms found using the patterned self-assembled monolayers (SAMs) method. These three forms were found in fewer than 1% of approximately 10 000 experiments performed using the high throughput approach and thus will be very difficult to find in the 58 experiments performed using the semi-automated approach. Results of this study demonstrate that the simple semi-automated approach of ∼60 experiments described in this work is suitable for early stage polymorph screening as it was able to reproduce effectively the diversity of polymorphs in model compounds.
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