Influences of hydrogen addition from different dual- fuel modes on engine behaviors

Compression ignition (CI) engines have good performance but more exhaust emissions. Dual fuel (DF) engines have better performance and lower emissions compared to CI mode. Also, the scarcity of fossil fuels made the researchers to find alternative fuels to power CI engines. Therefore, the present work aims to use hydrogen (H2) and honne oil biodiesel (BHO) to investigate the performance of CI engines in DF mode. Also, it aims to compare the performance of CI engines in various DF modes, namely induction, manifold injection, and port injection. First, the CI engine was fuelled completely by diesel fuel and BHO. The data were gathered when the engine ran at a constant engine speed of 1500 rpm and at 80% load. Second, the CI engine was operated in various DF modes and data were generated. CI engine operation in DF mode was smooth with biodiesel and H2. The brake thermal efficiency (BTE) of 32% and 31.1% was reported with diesel and biodiesel, respectively, for manifold injection due to low energy content and high viscosity of biodiesel. These values were higher than CI mode and other DF modes. Fuel substitution percentage for DF manifold injection was 60% and 57% with diesel and biodiesel, respectively. Smoke, hydrocarbon (HC), and carbon monoxide (CO) emissions were lower than conventional mode, but a reverse trend was observed for oxides of nitrogen (NOx) emissions. Heat release rate (HRR) and peak pressure (PP) were higher than conventional mode due to the fast combustion rate of hydrogen. The shortest ignition delay (ID) period was noticed for traditional diesel fuel, but it was longer for BHO biodiesel due to its higher viscosity and lower cetane number. On the contrary, the presence of hydrogen led to an increment in the combustion duration (CD) owing to the scarcity of oxygen in CD. Consequently, the paper clearly showed that the injection way of hydrogen plays a respectable role in the engine characteristics.https://wileyonlinelibrary.com/journal/ese3hj2023Mechanical and Aeronautical Engineerin

Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS‐ARAS and COPRAS‐ARAS

Traditional Multi‐Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision‐making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS‐ARAS and COPRAS‐ARAS were applied to solve a real‐time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate th

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function

Modeling and computational experiment to obtain optimized neural network for battery thermal management data

The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 have experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function

Thermal Onsets of Viscous Dissipation for Radiative Mixed Convective Flow of Jeffery Nanofluid across a Wedge

The current analysis discusses Jeffery nanofluid’s thermally radiative flow with convection over a stretching wedge. It takes into account the Brownian movement and thermophoresis of the Buongiorno nanofluid model. The guiding partial differential equations (PDEs) are modified by introducing the symmetry variables, leading to non-dimensional ordinary differential equations (ODEs). To solve the generated ODEs, the MATLAB function bvp4c is implemented. Examined are the impacts of different flow variables on the rate of transmission of heat transfer (HT), temperature, mass, velocity, and nanoparticle concentration (NC). It has been noted that the velocity and mass transfer were increased by the pressure gradient factor. Additionally, the thermal boundary layer (TBL) and nanoparticle concentration are reduced by the mixed convection (MC) factor. In order to validate the present research, the derived numerical results were compared to previous findings from the literature while taking into account the specific circumstances. It was found that there was good agreement in both sets of data

Multi-disciplinary computational investigations on asymmetrical failure factors of disc brakes for various CFRP materials: a validated approach

Finite element analyses (FEA) are flexible and advanced approaches, which are utilized to address difficult problems of aerospace materials that exhibit both structural symmetrical and structural asymmetrical characteristics. Frictional behavior effects are used as a crucial element in this multidisciplinary study, and other structural, and thermal properties are computed using FEA. Primary lightweight materials such as glass fiber reinforced polymer (GFRP), carbon fiber reinforced polymer (CFRP), Kevlar fiber reinforced polymer (KFRP), titanium alloy, tungsten carbide, steel alloys, and advanced lightweight materials, such as silicon carbide (SiC) mixer, based on aforesaid materials underwent comprehensive investigations on an aircraft disc brake, two-wheeler disc brake, and ASTM general rotating test specimen (G-99). Standard boundary conditions, computational sensitivity tests, and theoretical validations were conducted because the working nature of FEA may impair output dependability. First, FEA calculations were performed on a standard rotating disc component with two separate material families at various rotational velocities such as 400 RPM, 500 RPM, 600 RPM, 800 RPM, and 10 N of external frictional force. Via tribological experiments, frictional force and deformation of FEA outcomes were validated; the experimental outcomes serve as important boundary conditions for real-time simulations. Second, verified FEA was extended to complicated real-time applications such as aircraft disc brakes and automobile disc brakes. This work confirms that composite materials possess superior properties to conventional alloys for aircraft and vehicle disc brakes

Integrated Taguchi-GRA-RSM optimization and ANN modelling of thermal performance of zinc oxide nanofluids in an automobile radiator

Impact of different input variables on the thermal performance features of an automobile radiator was investigated, statistically analyzed, and optimized using the powerful technique-Taguchi's grey relational analysis (GRA) and Response surface methodology (RSM). Polyethylene glycol (PEG) nanofluids containing ZnO nanoparticles of various volume concentrations (0.2%–0.6%) were used. 1-Butyl 3-methylimidazolium bromide [C4mim][Br] ionic liquid was added to reduce particle accumulation and increase nanofluid dispersion. The mini radiator used was an unmixed crossflow type. The analyses were carried out at various flow rates. Thermal performance parameters like Nusselt number (Nu), heat transfer coefficient (htc), pressure drop, and pumping power were optimized by using weighted grey relational grade, depending on the experiments designed using Taguchi's Experiment Design. ANN modelling was used to get a better prediction of the non-linear form of critical data. Optimized Nu, htc, pressure drop, and pumping power were obtained for different combination of Re, air velocity, and nanofluid concentration to maximize. The htc estimated by ANN is found to be reasonably consistent with the experimental findings. From the research findings, it is also inferred that heat transfer enhancement does occur in radiators employing nanofluids but at the expense of the pressure drop and pumping power

Thermal modelling and characteristic evaluation of electric vehicle battery system

Thermal modelling of Li-ion battery system used in electric/hybrid electric vehicles is carried out. The main highlight of this study is the numerical modelling of Li-ion battery using finite volume method with the adoption of realistic coupled heat and fluid flow process. Thermal characters of the battery system are analyzed with the change in parameters like flow Reynolds number, battery internal heat generation, the length to width ratio, and the conduction-convection related parameter at the interface of battery and air. In detail analysis of maximum temperature, heat flux variations, Nusselt number, friction coefficient, coolant temperature, and velocity distributions are carried out. Finally, the effect of channel width formed between two parallel placed battery cells is also carried. The numerical analysis performed reveals that the length to width ratio of battery does not impact the thermal performance of battery. At lower Reynolds number, the conduction-convection parameter plays a significant role in reduction of battery temperature avoiding thermal stresses developed. Channel spacing has a prominent role in variation of battery and coolant temperature. The battery surface temperature is largely affected by parameters considered. However, it is at higher input of conduction-convection parameter, Reynolds number, and channel spacing the thermal variation remains insignificant

Tensile and wear properties of repetitive corrugation and straightened Al 2024 alloy: an experimental and RSM approach

Repetitive Corrugation and Straightening (RCS) on sheet geometries causes Cyclic Plastic Deformation, resulting in potential improvements of mechanical characteristics in metals and alloys. In this study, sample sheets of Al 2024 are subjected to severe plastic deformation with specially designed corrugated rollers to generate heterogeneous repeated plastic deformation at room temperature. The material shows enhanced properties under severe plastic deformation, with 5.07% increase in tensile strength, compared to unprocessed material. Maximum tensile strength was observed at annealed temperature of 150 °C is of about 3.49% increase in tensile strength over other temperature conditions. A wear study was carried out by considering the processed sheet that yields high tensile strength (annealed at 150 °C) by varying process parameters like sliding distance, load and sliding velocity as per design of experiments. In comparison to all other combinations, the wear resistance was shown to be better with a sliding distance of 6000 m, a load of 9.81 N, and a sliding velocity of 1.45 m s ^−1 . The Response Surface Methodology (RSM) approach was adopted for comparing purpose, the experimental findings are found to be more similar to the RSM approach’s outcomes