Analysis of Temperature Profiles in Longitudinal Fin Designs by a Novel Neuroevolutionary Approach

Abstract

Real application problems in physics, engineering, economics, and other disciplines are often modeled as differential equations. Classical numerical techniques are computationally expensive when we require solutions to our mathematical problems with no prior information. Hence, researchers are more interested in developing numerical methods that can obtain better solutions with fewer efforts and computational time. Heuristic algorithms are considered suitable candidates for such type of problems. In this research, we have developed a new neuroevolutionary algorithm that combines the power of feed-forward artificial neural networks (ANNs) and a modern metaheuristic, the Symbiotic Organism Search (SOS) algorithm. With our new approach, we have analyzed the simultaneous surface convection and radiation during heat transfer in different models of fins/ heat exchangers. Longitudinal fins are considered with concave parabolic, rectangular and trapezoidal shapes. We have analyzed our problem in two scenarios and six sub-cases. Our solutions are of high quality, with minimum residual errors in all cases. We have established the quality of our results by calculating values of different performance indicators like Root-mean-square error (RMSE), Absolute error (AE), Generational distance (GD), Mean absolute deviation (MAD), Nash-Sutcliffe efficiency (NSE), Error in Nash-Sutcliffe efficiency (ENSE). Statistical and graphical analysis of our results suggests that our approach is suitable for handling real application problems. We have compared our results with state-of-the-art results, and the outcome of our analysis points to the superiority of our approach. INDEX TERMS Approximate solutions, Artificial neural networks, Heat transfer analyses, Longitudinal fins, Metaheuristics, Symbiotic organism search optimizer.

Analysis of Beam‐Column Designs by Varying Axial Load with Internal Forces and Bending Rigidity Using a New Soft Computing Technique

“…Figure 11. e result of MAD, TIC, and ESNE for equation (24) is shown in Figures 2 and 3. Normal provability plots for fitness evaluation, MAD, TIC, and ENSE, are shown in Figures 7-10, respectively.…”

“…In electrical engineering, several methodologies are used to solve complex optimization problems [23]. Optimal design and temperature distribution of heat fin is solved by using hybridization of artificial neural networks and metaheuristic algorithms [24,25] and, furthermore, oscillatory behavior of heart beat [26]. Most of the real-application problems are highly nonlinear ODEs and provide less information about the continuity and differentiability of resulting solutions in solution space.…”

Analysis of Multi-Phase Flow Through Porous Media for Imbibition Phenomena by Using the LeNN-WOA-NM Algorithm

“…To examine the performance of the proposed algorithm (LeNN-WOA-NM) in calculating the saturation of (water) injected fluid into oil during water flooding process, the performance indicators like Mean Absolute Deviation (MAD), Theil's inequality coefficient (TIC) and Error in Nash Sutcliffe Efficiency (ENSE) are implemented [51]. The formulation of these indices are given as,…”

“…The optimal design of heat fins is proposed in [36]. A study of temperature distribution in heat fins is carried out by using a hybrid of the Cuckoo Search (CS) algorithm and Artificial Neural Network architecture [37], [38]. Neuro-fuzzy modeling is used to predict the summer precipitation in targeted metrological sites [39].…”

Analysis of Third-Order Nonlinear Multi-Singular Emden–Fowler Equation by Using the LeNN-WOA-NM Algorithm

“…In recent times, ANNs are used as universal function approximation procedures to develop stochastic numerical techniques. Due to their strength and stability, they are widely used for the solutions of variety of real world problems including multi-phase flow through porous media for imbibition phenomena [24], longitudinal heat transformation fins model [25], [26], Beam-Column designs [27], Optimal Model Selection for Regression [28], fractional models of damping material [26], nonlinear dusty plasma system [29], corneal Model for Eye Surgery [30] and temperature profile of porous fin model [31]. A plant propagation algorithm (PPA) and its modified version were developed to solve design engineering problems [32]- [35].…”