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In this investigation, a data-driven turbulence closure framework is introduced and deployed for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the subgrid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a priori and a posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability-density-function-based validation of subgrid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for subgrid quantity inference. In addition, it is also observed that some measure of a posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.

In the present era where frontal sinus surgery is synonymous with functional endoscopic sinus surgery, we present a case series comprising six cases of varied frontal sinus pathology that were managed with an external approach using an osteoplastic flap technique. The study was carried out in the ENT Department of Calcutta National Medical College, a tertiary care center in Kolkata. A unilateral osteoplastic flap approach was adopted in three cases, of which two were reported as osteomas and one was that of inverted papilloma. Bicoronal osteoplastic flap was raised in another group, which included a case of communited fracture of the anterior wall of the frontal sinus with obvious cosmetic facial deformity, a case of fracture of the posterior wall of the frontal sinus with traumatic cerebrospinal fluid rhinorrhea with pneumoencephalus, and a case of plasmacytoma of the frontal sinus. The mean follow-up period ranged from 1 to 3 years. There was no recurrence of disease and no significant postoperative complications have been reported during the follow-up period so far.

This paper describes a new stochastic control methodology for nonlinear affine systems subject to bounded parametric and functional uncertainties. The primary objective of this method is to control the statistical nature of the state of a nonlinear system to designed (attainable) statistical properties (e.g., moments). This methodology involves a constrained optimization problem for obtaining the undetermined control parameters, where the norm of the error between the desired and actual stationary moments of state responses is minimized subject to constraints on moments corresponding to a stationary distribution. To overcome the difficulties in solving the associated Fokker–Planck equation, generally experienced in nonlinear stochastic control and filtering problems, an approximation using the direct quadrature method of moments is proposed. In this approach, the state probability density function is expressed in terms of a finite collection of Dirac delta functions, and the partial differential equation can be converted to a set of ordinary differential equations. In addition to the above mentioned advantages, the state process can be non-Gaussian. The effectiveness of the method is demonstrated in an example including robustness with respect to predefined uncertainties and able to achieve specified stationary moments of the state probability density function.

The aim of this study was to present a unique case report of myoepithelial carcinoma arising from the external auditory canal and presenting as a huge tympanomastoid mass along with a review of the literature. A 52-year-old woman presented with a large periauricular swelling of a 3-year duration with a recent increase in size over the last 3 months along with pain and bleeding from the mass. The patient was evaluated by clinical examination, haematological and biochemical tests, and computed tomographic and MRI scan of the tympanomastoid region. An incision biopsy was performed before definitive management was initiated. Computed tomographic scan and MRI were suggestive of a large tympanomastoid mass without any intracranial extension. The incisional biopsy report was suggestive of invasive adenocarcinoma. En-bloc excision of the mass including lateral temporal bone resection along with ipsilateral selective (levels I, II and III) neck dissection was performed under general anaesthesia. The resultant defect was reconstructed by a rotational scalp flap. Immunohistochemistry and histopathology of the excised specimen proved the diagnosis of myoepithelial carcinoma. After surgery, the patient was treated with adjuvant radiotherapy. At 1½ years of follow-up, the patient was doing fine, without any recurrence of disease. Myoepithelial carcinoma of the ear has rarely been reported in the literature. Histopathology along with immunohistochemistry is the mainstay of diagnosing of this unusual lesion. Meticulous planning and proper execution of optimum surgical excision is the primary treatment modality, which should be supplemented with radiation therapy. Long-term disease-free survival, although rare, can be achieved as in the present case report.

The characteristics of effectiveness and heat transfer for a row of jets impinging on a corrugated surface were experimentally investigated. The Reynolds number, Re which was calculated based on the exit velocity and the hydraulic diameter of the nozzle, was 20000 and the jet-to-target spacing, L, was varied from 2 to 10 times the jet diameter, d. The spacing between adjacent jets, Sx, was kept constant at 4d. The actual angle of impingement and jet-to-target distance were different for different jets in the row due to the corrugated surface. This results in difference in the interaction of walljets of adjacent jets, which inturn influence the local distribution of effectiveness and Nusselt number

The physical mechanisms underlying the dynamics of the dissipation of passive scalar fluctuations with a uniform mean gradient in stationary isotropic turbulence are studied using data from direct numerical simulations (DNS), at grid resolutions up to 5123. The ensemble-averaged Taylor-scale Reynolds number is up to about 240 and the Schmidt number is from ⅛ to 1. Special attention is given to statistics conditioned upon the energy dissipation rate because of their important role in the Lagrangian spectral relaxation (LSR) model of turbulent mixing. In general, the dominant physical processes are those of nonlinear amplification by strain rate fluctuations, and destruction by molecular diffusivity. Scalar dissipation tends to form elongated structures in space, with only a limited overlap with zones of intense energy dissipation. Scalar gradient fluctuations are preferentially aligned with the direction of most compressive strain rate, especially in regions of high energy dissipation. Both the nature of this alignment and the timescale of the resulting scalar gradient amplification appear to be nearly universal in regard to Reynolds and Schmidt numbers. Most of the terms appearing in the budget equation for conditional scalar dissipation show neutral behaviour at low energy dissipation but increased magnitudes at high energy dissipation. Although homogeneity requires that transport terms have a zero unconditional average, conditional molecular transport is found to be significant, especially at lower Reynolds or Schmidt numbers within the simulation data range. The physical insights obtained from DNS are used for a priori testing and development of the LSR model. In particular, based on the DNS data, improved functional forms are introduced for several model coefficients which were previously taken as constants. Similar improvements including new closure schemes for specific terms are also achieved for the modelling of conditional scalar variance.

Journal of Fluid Mechanics , vol. 433 (2001), pp. 29–60

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various deep neural network architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equations, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly non-intrusive, since any prior information about the underlying governing equations is not required for generating the reduced order model. Our \\textit{a posteriori} analysis shows that the proposed data-driven approach is remarkably accurate, and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.

- and -cytoplasmic-actin are nearly indistinguishable in their amino acid sequence, but are encoded by different genes that play non-redundant biological roles. The key determinants that drive their functional distinction are unknown. Here we tested the hypothesis that - and -actin functions are defined by their nucleotide, rather than their amino acid sequence, using targeted editing of the mouse genome. Although previous studies have shown that disruption of -actin gene critically impacts cell migration and mouse embryogenesis, we demonstrate here that generation of a mouse lacking -actin protein by editing -actin gene to encode -actin protein, and vice versa, does not affect cell migration and/or organism survival. Our data suggest that the essential in vivo function of -actin is provided by the gene sequence independent of the encoded protein isoform. We propose that this regulation constitutes a global silent code mechanism that controls the functional diversity of protein isoforms.

Unpublished and misreported studies make it difficult to determine the true value of a treatment. Peter Doshi and colleagues call for sponsors and investigators of abandoned studies to publish (or republish) and propose a system for independent publishing if sponsors fail to respond