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Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression

Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving the solution of nonlinear differential problems, because of the need to query expensive numerical solvers repeatedly. Projection-based reduced order models (ROMs), such as the Galerkin-reduced basis (RB) method, have been

Data-driven analysis of parametrized acoustic systems in the frequency domain

A data-driven method combined with the formulations of boundary integral equations is developed for the frequency-domain analysis of parametrized acoustic systems, arising from the spatial discretization of the linear Helmholtz equation. The method derives surrogate models for the approximation of frequency response functions at selected field points via the construction of neural networks with ra

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the evaluations of high-fidelity (HF) snapshots to recover a reduced basis. Multi-fidelity Gaussian process regression (GPR) is employed to approxima

IoT based microcontroller operated UV germicide system

With the rise of the COVID-19 pandemic across the globe, people have come to understand the requirement of sanitation. However, other than the personal hygiene, sanitization of all the appliances (like mobile phone, wristwatch, wallet, eye wear, etc.) has become very important. Therefore, the requirement of germicides is being increased to sanitize all the appliances. A few germicides comprise che

The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computa

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-rein

Analysis of vitamin D and its metabolites in biological samples – Part I : Optimization and comparison of UHPSFC-MS/MS and UHPLC-MS/MS methods

Fat-soluble vitamin D is an essential bioactive compound important for human health. Insufficient vitamin D levels can result not only in bone disease but also in other disorders, such as cancer, metabolic disorders, and diseases related to poor immune function. The current methods commonly used for vitamin D analysis are often applied to determine the levels of the most abundant metabolite in pla

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions.

Non-intrusive reduced-order modeling for fluid problems : A brief review

Despite tremendous progress seen in the computational fluid dynamics community for the past few decades, numerical tools are still too slow for the simulation of practical flow problems, consuming thousands or even millions of computational core-hours. To enable feasible multi-disciplinary analysis and design, the numerical techniques need to be accelerated by orders of magnitude. Reduced-order mo

Data-driven reduced order modeling for time-dependent problems

A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and the proje

Prognosis of compressive strength of fly-ash-based geopolymer-modified sustainable concrete with ML algorithms

Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly-ash-based geopolymer (FLAG) concrete has been used in the construction industry for more than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete.

Axial Capacity of FRP-Reinforced Concrete Columns : Computational Intelligence-Based Prognosis for Sustainable Structures

Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated

Economic analysis of operation and maintenance costs of hydropower plants

The world is experiencing deep climate changes caused by increased population and rapid urbanization. Hydropower is one of the renewable energy sources that can be used to meet energy demands, but most of the hydropower plants suffer from silt erosion and cavitation problems. Therefore, it is important to decide which parts to be repair or replace, as it affects the Operation and Maintenance (O&am