Analytical asssessment of the structural behavior of a specific composite floor system at elevated temperatures using a newly developed hybrid intelligence method

The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm

Performance Evaluation of a Direct Absorption Collector for Solar Thermal Energy Conversion

The solar absorption efficiency of water as a base-fluid can be significantly improved by suspending nanoparticles of various materials in it. This experimental work presents the photo thermal performance of water-based nano-fluids of graphene oxide (GO), zinc oxide (ZnO), copper oxide (CuO), and their hybrids under natural solar flux for the first time. Nanofluid samples were prepared by the two-step method and the photothermal performance of these nanofluid samples was conducted under natural solar flux in a particle concentration range from 0.0004 wt % to 0.0012 wt %. The photothermal efficiency of water-based 0.0012 wt % GO nanofluid was 46.6% greater than that of the other nanofluids used. This increased photothermal performance of GO nanofluid was associated with its good stability, high absorptivity, and high thermal conductivity. Thus, pure graphene oxide (GO) based nanofluid is a potential candidate for direct absorption solar collection to be used in different solar thermal energy conversion applications

Thermal performance enhancement of nanofluids based parabolic trough solar collector (NPTSC) for sustainable environment

Due to rapid industrialization and urbanization, upward rise in carbon emissions in the atmosphere, and depletion of fossil fuel and gas reserves have forced to find alternative renewable energy resources, where solar energy is one of the most promising source. Parabolic trough solar collectors (PTCs) can effectively transfer high temperature in the tube of receiver upto 400 °C. In this study, Computational Fluid Dynamics (CFD) analysis is used to analyse the effect of multiple working fluids on efficiency of the PTC. Two different types of nanofluids used for analyising the thermal efficiency of PTC through CFD simulations, are Alumina and Copper-oxide nanofluids. The concentration of Copper Oxide and Alumina was kept to 0.01% in the nanofluids. The efficiency for PTC is calculated at two different mass flow rates i.e., 0.0112 Kg/s and 0.0224 Kg/s. The highest efficiency is 13.01 and 13.1% using Al2O3 as nanofluids at 0.0112 Kg/s and 0.0224 Kg/s flow rates, while CuO has an efficiency of 13.92% and 14.79% for these flow rates. The behaviour of absorber tube material on temperature distribution for steel, copper and aluminum as absorber tube material was also investigated. Changing the material from steel to copper and aluminum increased the outlet temperature of the fluid. The maximum output temperature was achieved for copper is 311 K while steel and aluminum showed lower temperature of 307 K and 308 K of the fluid at the outlet. Furthermore, the impact of the receiver tube's length on the working fluid's temperature is also studied. Copper Oxide nanofluid has higher temperature at the outlet for both mass flow rates as compared to alumina nanofluid. Accordingly, a comparison was made for the CFD results with the experimental findings from literature. The nanofluids based PTCs system is promising method for the sustainable environment applications

The impact of Oligo-miocene basaltic intrusions on the petroleum system in Gulf of Suez rift basin, Egypt: new insights into tikhermal maturity and reservoir quality

In recent years, the petroleum industry has prioritized the exploration of new and unconventional petroleum reservoirs. As a result, this study assessed the significance of basaltic intrusions from two key aspects: their impact on the thermal maturity of pre-rift source rocks and their potentiality as reservoirs. The present study attempts to integrate surface field investigations of basaltic dykes in Wadi Nukhul and Wadi Matulla as surface analogs with petroleum system modeling of pre-rift source rocks containing subsurface basaltic intrusions in the Abu Rudeis-Sidri field. Therefore, the fracture networks were observed in Wadi Nukhul and Wadi Matulla, suggesting that both the basaltic dykes and host rocks have interconnected fractures, which is critical for a high-quality reservoir of the dykes and efficient oil expulsion. As a result, the analysis of burial history, temperature, maturity, generation, transformation ratio, and expelled oil quantity revealed a significantly high value for basaltic intrusions. Moreover, the Abu Rudeis-Sidri field had a good petroleum system with thermally mature source rocks by basaltic intrusions. Furthermore, the fractured basaltic intrusions presented a high-quality oil reservoir well-sealed by the thick Rudeis Formation. Oil production has doubled since the discovery of this reservoir. This study introduces a novel approach to understanding the distribution pattern of basaltic intrusions in subsurface and surface analogs, which can serve as a model for exploring new potential unconventional basaltic reservoirs in the Gulf of Suez rift basin

The effect of lighting direction/condition on the performance of face recognition algorithms

In this paper, we measure the effect of the lighting direction in facial images on the performance of 2 wellknown face recognition algorithms, an appearance based method and a facial feature based method. We collect hundreds/thousands of facial images of subjects with a fixed pose and under different lighting conditions through a unique facial acquisition laboratory designed specifically for this purpose. Then we present a methodology for automatically detecting the lighting direction of different face images based on statistics derived from the image. We also detect if there is any glare regions in some lighting directions. Finally we determine the most reliable lighting direction that will lead to a good quality/high performance facial image from both techniques based on our experiments with the acquired data. 1