Estimation Of Heat Transfer Parameters Research Articles

Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learning

Shell and helically coiled tube heat exchangers (SHCTHEXs) are heat exchangers that only take up a small space and enable greater heat transfer area compared to traditional models. Information on 21 different SHCTHEXs obtained from catalog was considered for the modeling. Two other artificial neural network structures have been created to forecast the heat transfer coefficient, pressure drop, Nusselt number, and performance evaluation criteria values as outputs. In contrast, tubing and coil diameters, Reynolds and Dean numbers, curvature ratio, and mass flow rate are designed as inputs. In the network structures with 105 data points, 70% of the data was used for training, 15% for validation, and 15% for the testing stages. The Levenberg-Marquardt procedure was evaluated as the training algorithm in multi-layer perceptron network models. The coefficient of determination was as higher than 0.99. The mean deviation was less than 0.01%. The results show that the created artificial neural network structures can acqurately estimate the outputs.

Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

As a typical coolant in a radiator, a combination of water and ethylene glycol is often used. Since it has a lower thermal conductivity, the addition of nano particles can increase the performance of the coolant which aids in dwindling the weight and size of the radiator. This paper deals with effect of various input variables like flow rate, inlet temperature of the coolant and Vol% of nano particles (NP) on a radiator’s heat transfer parameters like heat transfer rate (Q), convective heat transfer co-efficient (h), Reynolds number (Re), Nusselt number (Nu) and friction factor (ff) were estimated using optimization methods through Taguchi, ANOVA and ANN. Initially Experimental trials were carried to evaluate heat transfer parameters of a radiator under forced convection by changing inlet temperature, flow rate and NP addition of a coolant. Base fluid water and EG were taken in the ratio of 70:30. To that, Nano particles of graphene were dispersed in the base fluid in the range of 0.1–0.3 Vol%. Coolant inlet temperature, flow rate and addition of nano particles were considered as input parameters (I/P). TheL27 orthogonal array was used as Design of Experiments (DOE). Q, h, Nu, Re and ffwere selected as performance variables. In addition to that, an ANOVA test through Minitab 15 was employed to evaluate the performance parameters in order to estimate each variable and its contribution in percentage. It was established that parameters like Q, h and Nu were largely influenced by the inlet temperature of a coolant, where Re and ff were impacted by flow rate. Estimation of heat transfer parameters like Q, h, Nu, Re and ff were done by using MATLAB through Artificial Neural Networks (ANN) and were found to be in good agreement with experimental data.

Estimation of heat transfer parameters by using trained POD-RBF and Grey Wolf Optimizer

The article presents a numerical model for estimation of heat transfer parameters, e.g. thermal conductivity and convective coefficient, in two-dimensional solid bodies under steady-state conduction. This inverse problem is stated as an optimization problem, in which input is reference temperature data and the output is the design variables, i.e. the thermal properties to be identified. The search for optimum design variables is conducted by using a recent heuristic method, namely Grey Wolf Optimizer. During the heuristic search, direct heat conduction problem has to be solved several times. The set of heat transfer parameters that lead to smallest error rate between computed temperature field and reference one is the optimum output of the inverse problem. In order to accelerate the process, the model order reduction technique Proper-Orthogonal-Decomposition (POD) is used. The idea is to express the direct solution (temperature field) as a linear combination of orthogonal basis vectors. Practically, a majority of the basis vectors can be truncated, without losing much accuracy. The amplitude of this reduced-order approximation is then further interpolated by Radial Basis Functions (RBF). The whole scheme, named as trained POD-RBF, is then used as a surrogate model to retrieve the heat transfer parameters.

A quasi-steady state model for predicting the heating requirements of conventional greenhouses in cold regions

A time-dependent, quasi-steady state thermal model (GREENHEAT) based on the lumped estimation of heat transfer parameters of greenhouses has been developed to predict the hourly heating requirements of conventional greenhouses. The model was designed to predict the hourly heating requirements based on the input of greenhouse indoor environmental control parameters, physical and thermal properties of crops and construction materials, and hourly weather data including temperature, relative humidity, wind speed, and cloud cover. The model includes all of the heat transfer parameters in greenhouses including the heat loss for plant evapotranspiration, and the heat gain from environmental control systems. Results show that the predicted solar radiation data from the solar radiation sub-model are a reasonable fit with the data from the National Solar Radiation Database (NSRDB). Thermal analysis indicates environmental control systems could reduce 13–56% of the total heating requirements over the course of a year in the study greenhouse. During the winter season, the highest amount of greenhouse heat is lost due to conduction and convection, and the heat used for evapotranspiration is dominant in the summer. Finally, the model was validated with actual heating data collected from a commercial greenhouse located in Saskatoon, and the results show that the model satisfactorily predicts the greenhouse heating requirements.

Determination of heat transfer parameters by use of finite integral transform and experimental data for regular geometric shapes

This article offers a study on estimation of heat transfer parameters (coefficient and thermal diffusivity) using analytical solutions and experimental data for regular geometric shapes (such as infinite slab, infinite cylinder, and sphere). Analytical solutions have a broad use in experimentally determining these parameters. Here, the method of Finite Integral Transform (FIT) was used for solutions of governing differential equations. The temperature change at centerline location of regular shapes was recorded to determine both the thermal diffusivity and heat transfer coefficient. Aluminum and brass were used for testing. Experiments were performed for different conditions such as in a highly agitated water medium (T = 52 °C) and in air medium (T = 25 °C). Then, with the known slope of the temperature ratio vs. time curve and thickness of slab or radius of the cylindrical or spherical materials, thermal diffusivity value and heat transfer coefficient may be determined. According to the method presented in this study, the estimated of thermal diffusivity of aluminum and brass is 8.395 × 10−5 and 3.42 × 10−5 for a slab, 8.367 × 10−5 and 3.41 × 10−5 for a cylindrical rod and 8.385 × 10−5 and 3.40 × 10−5 m2/s for a spherical shape, respectively. The results showed there is close agreement between the values estimated here and those already published in the literature. The TAAD% is 0.42 and 0.39 for thermal diffusivity of aluminum and brass, respectively.

Comparative Study of Heat Transfer Parameter Estimation Using Inverse Heat Transfer Models of a Trailing Liquid Nitrogen Jet in Welding

Welding induces distortions and stresses in welded structures. A heat sink trailing the weld arc is one among the methods used to reduce the stresses and distortions. Researchers have made use of cooling agents like atomized water and liquid nitrogen (LN2) jet as trailing heat sinks. As the inclusion of a heat sink alters the temperature distribution, estimation of heat removal due to cooling jet is a prerequisite for the study of the effects of the heat sink on stresses and distortions. Analytical methods for the estimation of heat transfer parameters are complicated due to nonuniform cooling by the impinging jet, boiling heat transfer of cooling jet, stray fluid particles on the work-piece surface, and the sharp localized temperature gradient on the work piece due to weld heat input. Therefore, an inverse heat transfer (IHT) method is adapted to estimate the heat transfer parameters due to heat sink. Experiments were conducted using automated tungsten inert gas welding apparatus on a low-carbon steel work piece with an LN2 trailing jet, and transient temperature history was recorded at select points. A finite-element method is used as a solution procedure for IHT method. The heat transfer parameters are estimated by comparing the temperature history at a point from the experiment with the temperature history of the same point in the finite-element model by an iterative method. The heat transfer coefficient, heat flux of LN2 jet, and effective radius of the jet are estimated using two-dimensional and three-dimensional models.

Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation

A new non-sequential technique is proposed for the estimation of effective heat transfer parameters using radial temperature profile measurements in a gas–liquid co-current downflow through packed bed reactors (often referred to as trickle bed reactors). Orthogonal collocation method combined with a new optimization technique, differential evolution (DE) is employed for estimation. DE is an exceptionally simple, fast and robust, population based search algorithm that is able to locate near-optimal solutions to difficult problems. The results obtained from this new technique are compared with that of radial temperature profile (RTP) method. Results indicate that orthogonal collocation augmented with DE offer a powerful alternative to other methods reported in the literature. The proposed technique takes less computational time to converge when compared to the existing techniques without compromising with the accuracy of the parameter estimates. This new technique takes on an average 10 s on a 90 MHz Pentium processor as compared to 30 s by the RTP method. This new technique also assures of convergence from any starting point and requires less number of function evaluations.

Transfert thermique dans un réacteur catalytique à lit fixe à température de paroi constante

Three bidimensional pseudo-homogeneous models are considered for estimation of heat transfer parameters in a fixed bed catalytic reactors. The Peclet and Biot numbers are obtained by identification of experimental and calculated temperatures profiles in a pilot plant reactor. Particular attention is drawn to sensivity of identification to experimental variables. The results obtained from the one parameter model proposed by Dewash and Froment are compared with experimental data, and influence of axial dispersion in catalytic bed is studied by comparison of different models.

Estimation of heat transfer parameters in packed beds from radial temperature measurements

A computational technique for estimation of heat transfer parameters in packed beds from radial temperature measurements has been developed. The model equations are solved by a modified orthogonal collocation method. The computing time required for estimation of a set of parameters, a few milliseconds, is orders of magnitude smaller than values reported in recent literature.

Some representations of variations with application to optimization and sensitivity analysis

Representations for the first variation of a class of functionals of the solution of elliptic boundary value problems are given. These may be used for sensitivity analysis and optimization studies. Several engineering applications are discussed. These include some applications to structural optimization as well as the estimation of heat transfer parameters from experimental measurements. The useful numerical representations which follow from the continuous derivation are discussed.