Rainfall forecasting is crucial in the field of meteorology and hydrology However, existing solutions always achieve low prediction accuracy for short-term rainfall forecasting Atmospheric forecasting models perform worse in many conditions Machine learning approaches neglect the influences of physical factors in upstream or downstream regions, which make forecasting accuracy fluctuate in different areas To improve the overall forecasting accuracy for short-term rainfall, this paper proposes a novel solution called D ynamic R egional C ombined short-term rainfall F orecasting approach (DRCF) using Multi-layer Perceptron (MLP) First, Principal Component Analysis (PCA) is used to reduce the dimension of thirteen physical factors, which serves as the input of MLP Second, a greedy algorithm is applied to determine the structure of MLP The surrounding sites are perceived based on the forecasting site Finally, to solve the clutter interference which is caused by the extension of the perception range, DRCF is enhanced with several dynamic strategies Experiments are conducted on data from 56 real-world meteorology sites in China, and we compare DRCF with atmospheric models and other machine learning approaches The experimental results show that DRCF outperforms existing approaches in both threat score (TS) and root mean square error (RMSE)

Short-Term Rainfall Forecasting Using Multi-Layer Perceptron

In this new world of fluid technologies, hybrid nanofluid has become a productive subject of research among scientists for its potential thermal features and abilities, which provides an excellent result as compared to nanofluids in growing the rate of heat transport. Our purpose here is to introduce the substantial influences of magnetic field on 2D, time-dependent and stagnation point inviscid flow of couple stress hybrid nanofluid around a rotating sphere with base fluid is pure blood, [Formula: see text] as the nanoparticles. To translate the governing system of partial differential equations and the boundary conditions relevant for computation, some suitable transformations are implemented. To obtain the analytical estimations for the corresponding system of differential expression, the innovative Optimal Homotopy Analysis Method is used. The characteristics of hybrid nanofluid flow patterns, including temperature, velocity and concentration profiles are simulated and analyzed in detail due to the variation in the evolving variables. Detailed research is also performed to investigate the influences of relevant constraints on the rates, momentum and heat transport for both [Formula: see text] and [Formula: see text]. One of the many outcomes of this analysis, it is observed that increasing the magnetic factor will decelerate the hybrid nanofluid flow velocity and improve the temperature profile. It may also be demonstrated that by increasing the Brownian motion factor, significant improvement can be made in the concentration field of hybrid nanofluid. The increase in the nanoparticle volume fraction from 0.01 to 0.02 in the case of the hybrid nanofluid enhances the thermal conductivity from 5.8 to 11.947% and for the same value of the nanoparticle volume fraction in the case of nanofluid enhance the thermal conductivity from 2.576 to 5.197%.

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Mixed convection stagnation point flow of the blood based hybrid nanofluid around a rotating sphere.

Surface temperature is an important parameter in clinical diagnosis, equipment state control, and environmental monitoring fields. The Fiber Bragg Grating (FBG) temperature sensor possesses numerous significant advantages over conventional electrical sensors, thus it is an ideal choice to achieve high-accuracy surface temperature measurements. However, the effects of the ambient temperature and installation types on the measurement of surface temperature are often overlooked. A theoretical analysis is implemented and a thermal transfer model of a surface FBG sensor is established. The theoretical and simulated analysis shows that both substrate strain and the temperature difference between the fiber core and hot surface are the most important factors which affect measurement accuracy. A surface-type temperature standard setup is proposed to study the measurement error of the FBG temperature sensor. Experimental results show that there are two effects influencing measurement results. One is the “gradient effect”. This results in a positive linear error with increasing surface temperature. Another is the “substrate effect”. This results in a negative non-linear error with increasing surface temperature. The measurement error of the FBG sensor with single-ended fixation are determined by the gradient effect and is a linear error. It is not influenced by substrate expansion. Thus, it can be compensated easily. The measurement errors of the FBG sensor with double-ended fixation are determined by the two effects and the substrate effect is dominant. The measurement error change trend of the FBG sensor with fully-adhered fixation is similar to that with double-ended fixation. The adhesive layer can reduce the two effects and measurement error. The fully-adhered fixation has lower error, however, it is easily affected by substrate strain. Due to its linear error and strain-resistant characteristics, the single-ended fixation will play an important role in the FBG sensor encapsulation design field in the near future.

https://www.mdpi.com/1424-8220/16/7/975/pdf

Model Study of the Influence of Ambient Temperature and Installation Types on Surface Temperature Measurement by Using a Fiber Bragg Grating Sensor.

Adverse effects of wastewater on the hygiene of human and circumstances is a major issue in society. Appropriate refining systems with high efficiency is required to treat the wastewater. Sewage treatment plant plays a major and important role in conserving incredible nature of the environment. Microbiologically Induced Corrosion (MIC) is an important phenomenon in sewage structures which causes the deterioration of infrastructures. Huge capital has been spent and efforts have been made on wastewater treatment infrastructure to increase operating efficiency and reliability of compliance. The investments in reimbursement and maintenance of sewer structures upsurge with an increase in the rate of MIC. The focus of this review is to describe MIC in sewer structure and the factors influencing the corrosion such as the generation of Sulfuric acid (H2SO4), Relative Humidity (RH), pH of the concrete structure and temperature. Modern developments in the design of Fiber Optical Sensors (FOSs) for observing the parameters including pH, Hydrogen Sulfide (H2S), RH and temperature will be discussed.

Review of Fiber Optical Sensors and Its Importance in Sewer Corrosion Factor Analysis

This article presents the use of Fiber Bragg Grating (FBG) sensor to measure the temperature induced at the tip of the tool, while micro-face turning of optical surfaces. FBG sensor of 120 μm diameter was mounted near the tip of the tool and the shift in Bragg wavelength due to induced temperature was acquired with the help of an interrogator. The experiments were conducted on a Taylor Hobson DT-250 SPDT machine, over three different optical grade alloys namely Aluminum 6061, OFHC (Oxygen free highly conducting) copper and stainless steel. It was observed that while machining stainless steel, temperature at tool tip was highest and in case of OFHC copper it was lowest. The roughness and waviness of machined optical surfaces were measured using PGI 400 Profilometer. The results confirm that temperature induced in micro cutting and the rate of heat dissipation of work material contributes significantly to optical surface integrity.

Fiber Bragg Grating Sensor for Temperature Measurement in Micro Turning of Optical Surfaces with High Surface Integrity

Purpose – The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.Design/methodology/approach – The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forec...

A new PCA‐ANN algorithm for retrieval of rainfall structure in a precipitating atmosphere

Numerical simulations are performed to determine the heat transfer characteristics of slot jet impingement of air on a concave surface. The purpose of this paper is to investigate the effect of protrusions on the heat transfer by placing semi-circular protrusions on the concave surface at several positions. After identifying appropriate locations where the heat transfer is a maximum, multiple protrusions are placed at desired locations on the plate. The gap ratio, curvature ratio (d/D) and the dimensions of the plate are varied so as to obtain heat transfer data. The curvature ratio is varied first, keeping the concave diameter (D) fixed followed by a fixed slot width (d). A surrogate model based on an artificial neural network is developed to determine optimum locations of the protrusions that maximize the heat transfer from the concave surface.,The scope and objectives of the present study are two-dimensional numerical simulations of the problem by considering all the geometrical parameters (H/d, dp, Re, θ) affecting heat transfer characteristics with the help of networking tool and numerical simulation. Development of a surrogate forward model with artificial neural networks (ANNs) with a view to explore the full parametric space. To quantitatively ascertain if protrusions hurt or help heat transfer for an impinging jet on a concave surface. Determination of the location of protrusions where higher heat transfer could be achieved by using exhaustive search with the surrogate model to replace the time consuming forward model.,A single protrusion has nearly no effect on the heat transfer. For a fixed diameter of concave surface, a smaller jet possesses high turbulence kinetic energy with greater heat transfer. ANN is a powerful tool to not only predict impingement heat transfer characteristics by considering multiple parameters but also to determine the optimum configuration from many thousands of candidate solutions. A maximum increase of 8 per cent in the heat transfer is obtained by the best configuration constituting of multiple protrusions, with respect to the baseline smooth configuration. Even this can be considered as marginal and so it can be concluded that first cut results for heat transfer for an impinging jet on a concave surface with protrusions can be obtained by geometrically modeling a much simpler plain concave surface without any significant loss of accuracy.,The heat transfer during impingement cooling depends on various geometrical parameters but, not all the pertinent parameters have been varied comprehensively in previous studies. It is known that a rough surface may improve or degrade the amount of heat transfer depending on their geometrical dimensions of the target and the rough geometry and the flow conditions. Furthermore, to the best of authors’ knowledge, scarce studies are available with inclusion of protrusions over a concave surface. The present study is devoted to development of a surrogate forward model with ANNs with a view to explore the full parametric space.

Numerical simulations and optimization of impinging jet configuration

In this paper we present a novel, practical approach for carrying out distributed high temperature sensing in boilers. We have demonstrated distributed high temperature sensing using fiber Bragg gratings encapsulated inside a rugged mechanical structure. The encapsulation is designed to not only protect the optical fiber from the harsh environment of the boiler, but also to scale the temperature down to a range over which the gratings are relatively stable. A key aspect of our work is the use of a Bayesian inference technique to retrieve the temperature outside the encapsulation within < 1°C accuracy based on the temperature measured by the fiber Bragg grating (FBG).

Distributed High Temperature Sensing Using Fiber Bragg Gratings

This work reports the results of a study envisaged to benchmark a novel technique for simultaneously estimating the principal thermal conductivities (kx, ky and kz ) of an anisotropic composite medium. The technique essentially involves solving a formulated Inverse Heat Conduction Problem (IHCP) using measured temperature data as input to simultaneously estimate the constant thermal conductivities along the three directions of an anisotropic composite. The IHCP is solved using a hybrid optimization algorithm where the forward simulations are driven by a carefully chosen ANN and the optimization part is accomplished by employing GA. Before accomplishing estimation with actual “measurements”synthetically generated “surrogate”temperatures are used to ascertain the adequacy and robustness of the methodology. An indigenously designed anisotropic material whose effective thermal conductivities in the principal directions are determined using a full scale computational heat transfer analysis is used as a benchmark for validating the technique.

Balaji Chakravarthy

Papers

A new PCA‐ANN algorithm for retrieval of rainfall structure in a precipitating atmosphere

Numerical simulations and optimization of impinging jet configuration

Distributed High Temperature Sensing Using Fiber Bragg Gratings

Benchmarking a novel technique for estimating principal thermal conductivities of ananisotropic composite medium