A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs multilayer feedforward artificial neural network (ANN) with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network (CNN) fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. We detail the hyperparameters optimization of these models using the grid search algorithm

Frame invariant neural network closures for Kraichnan turbulence

Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization (or closure) models for subgrid scale (SGS) processes using physical insights and mathematical approximations, they remain imperfect and can lead to inaccurate predictions. In recent years, machine learning has been successful in extracting complex patterns from high-resolution spatio-temporal data, leading to improved parameterization models, and ultimately better coarse grid prediction. However, the inability to satisfy known physics and poor generalization hinders the application of these models for real-world problems. In this work, we propose a frame invariant closure approach to improve the accuracy and generalizability of deep learning-based subgrid scale closure models by embedding physical symmetries directly into the structure of the neural network. Specifically, we utilized specialized layers within the convolutional neural network in such a way that desired constraints are theoretically guaranteed without the need for any regularization terms. We demonstrate our framework for a two-dimensional decaying turbulence test case mostly characterized by the forward enstrophy cascade. We show that our frame invariant SGS model (i) accurately predicts the subgrid scale source term, (ii) respects the physical symmetries such as translation, Galilean, and rotation invariance, and (iii) is numerically stable when implemented in coarse-grid simulation with generalization to different initial conditions and Reynolds number. This work builds a bridge between extensive physics-based theories and data-driven modeling paradigms, and thus represents a promising step towards the development of physically consistent data-driven turbulence closure models

Dip coating of forsterite-hydroxyapatitie-poly (ɛ-caprolactone) nanocomposites on Ti6Al4Vsubstrates for its corrosion prevention

522-528Titanium and titanium alloys are extensively used in biomedical, cardiac and cardiovascular applications for their superb properties, such as good fatigue strength, low modulus, machinability, formability, corrosion resistance and biocompatibility. However, titanium and its alloys do not meet the majority of all clinical necessities. Due to these reasons, surface modification is frequently performed to enhance the mechanical, biological and chemical properties of titanium and alloys. In this work, nanocomposites coating of poly(ɛ-caprolactone)/hydroxyapatite/forsterite (PCL/HA/F) have been successfully deposited on the Ti6Al4V substratesby dip coating at room temperature. The coatings are prepared with various concentrations of forsterite/hydroxyapatite nanopowder (2, 4, 6 and 8 wt.%) with a fixed concentration of PCL (4 wt.%) and thus coated Ti6Al4V substrates are examined for corrosion resistance. PCL/Hydroxyapatite/Forsterite coatings are characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), which clearly showed the formation of nanocomposites. Potentiodynamic polarization curves and electrochemical impedance spectroscopy (EIS) are used to investigate corrosion behavior of the coated substrates, which portrayed that the composite coating of PCL/HA/F substantially enhanced the corrosion resistance of Ti6Al4V alloy

Dip coating of forsterite-hydroxyapatitie-poly (ɛ-caprolactone) nanocomposites on Ti6Al4Vsubstrates for its corrosion prevention

Titanium and titanium alloys are extensively used in biomedical, cardiac and cardiovascular applications for their superb properties, such as good fatigue strength, low modulus, machinability, formability, corrosion resistance and biocompatibility. However, titanium and its alloys do not meet the majority of all clinical necessities. Due to these reasons, surface modification is frequently performed to enhance the mechanical, biological and chemical properties of titanium and alloys. In this work, nanocomposites coating of poly(ɛ-caprolactone)/hydroxyapatite/forsterite (PCL/HA/F) have been successfully deposited on the Ti6Al4V substratesby dip coating at room temperature. The coatings are prepared with various concentrations of forsterite/hydroxyapatite nanopowder (2, 4, 6 and 8 wt.%) with a fixed concentration of PCL (4 wt.%) and thus coated Ti6Al4V substrates are examined for corrosion resistance. PCL/Hydroxyapatite/Forsterite coatings are characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), which clearly showed the formation of nanocomposites. Potentiodynamic polarization curves and electrochemical impedance spectroscopy (EIS) are used to investigate corrosion behavior of the coated substrates, which portrayed that the composite coating of PCL/HA/F substantially enhanced the corrosion resistance of Ti6Al4V alloy

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.publishedVersio

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.publishedVersio

Endoscopic removal of intrauterine contraceptive device embedded into detrusor muscle of urinary bladder: our experience of two cases

Migration of intrauterine contraceptive device (IUD) into urinary bladder is a rare event, presenting as irritative lower urinary tract symptoms; we present two cases of migrated IUD into urinary bladder and embedded inside the detrusor muscle of bladder. Both patients were assessed by ultrasonography and computed tomography. Both patients were successfully treated by endoscopic approach via per urethral route. One patient was having embedded vertical arm of IUD which was pulled using forceps and second patient was having embedded horizontal arm of IUD in detrusor muscle which was treated by taking mucosal incision with help of Collin’s knife followed by pulling IUD with help of forceps. There was no evidence of fistula or any other complication. We would like to conclude that endoscopic removal of IUD embedded into detrusor muscle is safe, feasible alternative to open surgery without any further risk of fistula formation

PRA Techniques in Agriculture: Common Diagraming and Mapping Tools

PRA techniques and tools commands paramount importance in bottom up approach of planning and implementation of agricultural programmes. The following paper describes the various PRA techniques used in the study such as Transect, Mobility map, Timeline and Time trend. Apart from PRA, other methods of data collection such as use of semi-structured interview schedule, direct observation and focus group methods were also used to elicit information from the villagers

Matrix Ranking- An important PRA tool to assess farmers preferences and priorities

Matrix ranking is an important PRA tool to assess and study the preferences of farmers for a particular technology over others, with respect to crop or animal based technologies. The preferences and criteria for the same are also studied in the process. The following study gives a first-hand idea of farmers’ relative preferences for different varieties of rice, mustard, tomato, chilli, garden pea, fish and lac hosts. The results of the matrix ranking for different varieties of rice revealed that, the variety “Arize 6444” was the most preferred one followed by “Abhishek”. “Pusa Mahak” was the leading variety of mustard followed by Pusa Bold. Swarna Sampada is more preferred tomato variety among the farmers. Among fish, “Rohu” was widely preferred by the respondents due to its higher yield, more market demand, resistance to water quality and higher market price