6 research outputs found
Probing the compound effect of spatially varying intrinsic defects and doping on mechanical properties of hybrid graphene monolayers
No full textDoping in pristine 2D materials brings about the advantage of modulating wide range of mechanical properties simultaneously. However, intrinsic defects (such as Stone-Wales and nanopore) in such hybrid materials are inevitable due to complex manufacturing and synthesis processes. Besides that, defects and irregularities can be intentionally induced in a pristine nanostructure for multi-synchronous modulation of various multi-functional properties. Whatever the case may be, in order to realistically analyse a doped graphene sheet, it is of utmost importance to investigate the compound effect of doping and defects in such 2D monolayers. Here we present a molecular dynamics based investigation for probing mechanical properties (such as Young's modulus, post-elastic behaviour, failure strength and strain) of doped graphene (C14 and Si) coupling the effect of inevitable defects. Spatial sensitivity of defect and doping are systematically analyzed considering different rational instances. The study reveals the effects of individual defects and doping along with their possible compounded influences on the failure stress, failure strain, Young's modulus and constitutive relations beyond the elastic regime. Such detailed mechanical characterization under the practically relevant compound effects would allow us to access the viability of adopting doped graphene in various multifunctional nanoelectromechanical devices and systems in a realistic situation.</p
Effect of Temperature on the Fracture Strength of Perfect and Defective MonoLayered Graphene
No full textDevelopment of Data-Driven Predictive Framework for Nanofluid-based Solar Thermal Collector: A Machine Learning Approach
No full textThe conventional approach for solar collector design often requires performing large-scale experimentations or computationally intensive simulations which hinders the comprehensive screening and optimization of process design. This creates a strong rationale for developing a computationally efficient framework capable of leveraging a relatively small number of samples to generate a machine-learning model with sufficiently high fidelity. In this regard, the present study aims to integrate the concepts of random sampling, Gaussian process(GP), and Bayesian optimization for developing a computationally efficient data-driven framework for capturing the complete continuous domain of the parametric variation and predicting the desired performance measure. The proposed framework is rigorously tested at different stages with the help of unknown samples (out-of-fold test samples) to ensure the sound generalization capability of the constructed model. The model assessment revealed that the increase in sample size for training the GP model from 35 samples to 105 samples resulted in ≈ 56% reduction in root mean square error (RMSE), which further reduces to ≈ 96.5% after performing Bayesian optimization based hyperparameter tuning. The proposed framework will be extremely helpful in designing the highly efficient nanofluid-based solar thermal collector, by preventing the need of performing large-scale experimentations/simulations for screening purpose
Sustainable design of solar chimney power plants: A hybrid neural network approach for thermo-economic optimization
No full textThe optimal design of geometrical features in solar chimney power plants enhances performance but often increases costs, creating a need for economical design approaches. This study proposes an artificial intelligence-driven multi-objective optimization framework for thermoeconomic solar chimney power plant design, integrating numerical simulations with neural networks and genetic algorithms. The investigation considered a high-dimensional input feature space consisting of collector inlet height, collector diameter, chimney diameter, chimney height, and solar radiation, modeling their effects on system performance to develop high-fidelity neural networks for predicting actual power, overall efficiency, and total cost targeting Manzanares plant conditions. Numerical simulations using finite volume methods were conducted with ANSYS, generating comprehensive datasets based on 136 sets of geometrical parameters. The developed neural networks are deployed as objective functions in a multi-objective genetic algorithm framework for performing Pareto optimality that simultaneously maximizes power and efficiency while minimizing cost. The optimization study yielded a remarkable improvement in both power and efficiency, with power output increasing by a factor of 3.82 and efficiency rising by 4 times, all while maintaining almost same cost as the reference plant. Further analysis showed that power generation was 3.65 times higher, and efficiency 3.55 times greater, at just 87 % of the cost of the reference plant. Notably, a 10 % higher investment resulted in a substantial gain—power was enhanced by 4.51 times and efficiency improved by 5.73 times. These gains were achieved through a strategic design approach that involved enlarging the collector and chimney diameters, while reducing the chimney height. This approach enables rapid exploration of complex design spaces that would be computationally prohibitive using traditional computational fluid dynamics-based optimization methods and can be extended for optimizing any solar chimney-based energy system
Optimization of hybrid solar chimney power plants (HSCPPs): A review of multi-objective approaches
No full textA novel comparative study of machine learning surrogate models for solar chimney (SC) plant performance evaluation: Thermo-physical insights
No full textThis study analyzes the thermophysical performance of a conventional Manzanares solar chimney (SC) plant for finding the optimal configuration by varying three key design parameters, namely inlet height of the collector, diameter, and divergence of the chimney at different irradiation levels (400–1000 W/m2). A numerical solver based on finite volume methods is employed to run simulations. Additionally, multiple machine learning surrogate models were evaluated to identify the most effective approach for performance prediction. The analyses of three geometric parameters leads to optimum design values, which vary with solar irradiation. For a solar intensity of 1000 W/m2, the most efficient collector inlet height is about 0.2 m providing a ∼116 % power increase compared to the standard Manzanares plant. The optimal inlet height increases to 1.0 m at lower irradiation (400 W/m2). It is determined that under any irradiation conditions, chimney diameter increase beyond ∼45 m leads to a negligible improvement in power generation. This power generation is ∼318 % more compared to the Manzanares plant at 1000 W/m2 at this chimney diameter. The optimal outlet chimney diameter is approximately 15 m, which maximizes power generation for the Manzanares model at all incident solar radiation levels, resulting in a ∼52 % enhancement in performance compared to the standard Manzanares plant of straight chimney. Additionally, a comparative analysis of machine learning (ML) models, including decision trees, linear regression, artificial neural networks (ANN), support vector machines (SVM), and Gaussian process regression (GPR), demonstrates the superior predictive accuracy and robustness of GPR models. The iterative evaluation of GPR models using a Monte Carlo cross-validation approach confirms their reliability, with 25 iterations resulting in a mean R2 magnitude of 0.9 and 95 % lower RMSE values compared to other ML techniques, regardless of the chimney responses considered
