•The surrogate model constructed by PINN and GCN improves the physical prior knowledge and model's adaptabilities to geometry variations.•The trained model can predict temperature fields in cavities ...with arbitrary heat source shapes, sizes and positions.•The model can accurately predict the physical information at the boundaries.•The model training only requires a small amount of dataset.
This work proposes a novel surrogate model (noted as HCP-PIGN) combining two groups of neural networks: i.e., the physics-informed and the graph convolutional neural networks (noted as PINN and GCN). It aims to tackle the existing challenges: pixelated pre-processing of data and large amounts of training data. For predicting 2D steady-state heat conduction, the GCN acting as the prediction module, considering the interdependence between unstructured and neighboring nodes. The PINN serving as the physical constraint module, embeds governing equations into the neural network’s loss function. The HCP-PIGN model obtains precise predictions with diverse geometries and within milliseconds. The predictive performance of HCP-PIGN was further compared with three network structures: i.e., the physics-informed fully connected neural network (noted as FNN), purely data-driven based FNN, and GCN. The results indicate that HCP-PIGN has the lowest error of temperature field predictions, which are below 3 % and 1.3 % for the max and mean relative errors, respectively. The improvements of 28.1% and 34.6% in accuracy are achieved over the pure data-driven GCN, and the physics-driven FNN, respectively. Therefore, the proposed HCP-PIGN model improves the physical prior knowledge and model’s adaptabilities to geometry variations, resulting in superior performances.
This work focuses on a comprehensive performance enhancement considering hydrogen production, energy consumption of balance of plant (BOP), and thermal safety by optimizing the inlet water ...temperature and flow velocity. First, a multiphysics model is introduced to show that the inlet water temperature and flow velocity can affect the current density as well as the hydrogen production. An overall efficiency is defined to balance the hydrogen production and the energy consumption of BOP. After that, a surrogate model is established based on artificial neural network to replace the multiphysics model because of its huge computational effort. For the thermal safety and performance enhancement, an optimization problem with the maximum membrane temperature as a constraint and the overall efficiency as an objective function is proposed. The differential evolution (DE) strategy with the feasibility rule (FR) is adopted to solve this optimization problem. Finally, the DE-FR-based optimization algorithm is applied to three typical days in Chengdu of China representing summer, winter, and transitional seasons, respectively. Compared to the invariant inlet water flow velocity and temperature, the optimal and time-varying parameters improve their efficiency by relative percentages of 2.68%, 3.79%, and 3.29% for the three typical days, respectively.
•A realistic optimization problem for operating parameters is proposed.•Overall efficiency with BOP’s energy consumption is adopted as objective function.•ANN surrogate model replaces multiphysics model for less computational cost.•Optimal inlet water temperatures and velocities are obtained by DE-FR-based method.•Optimization results are verified to be effective in real PV application scenarios.
Intelligent structural design using generative adversarial networks (GANs) is a revolutionary design approach for building structures. Despite its far‐reaching capability, the data quantity and ...quality may have limited the performance of such a data‐driven network. This study proposes to enhance the objectiveness of training processes by innovatively introducing a surrogate model, Physics Estimator, that informs the generator by appraising the physical behavior of the generated design. Dual loss functions evaluated by a traditional data‐driven discriminator and the Physics Estimator collaboratively foster the physics‐enhanced GAN architecture. We further develop a structural mechanics model to train and optimize the inherent accuracy of the Physics Estimator. The comparative study suggests that the proposed physics‐enhanced GAN can generate structural designs from architectural drawings and specified design conditions 44% better than a data‐driven design method and 90 times faster than a competent engineer.
This paper describes an emulator that uses a Stochastic Variational Gaussian process (SVGP) regression model to parameterize radiation in a numerical weather prediction (NWP) model that ...meteorologically models the Earth's weather system. The computation of radiative processes is very large, accounting for most of the total NWP model computation. Statistical emulators are surrogate models that represent simulators and can overcome the computational limitations of very complex simulators such as radiative processes. Recently, artificial neural network-based radiative transfer emulators have been developed, and in this study, a statistical model, GP, is used to develop a radiative transfer emulator. The GP model has the advantage of calculating the uncertainty of the prediction along with the prediction, so the uncertainty of the prediction can be utilized appropriately. However, the computational complexity of the conventional GP model is very high, making it difficult to apply to large data. To solve this problem, an approximate approach, the SVGP model, was utilized. To further reduce the dimensionality of the input variables, we used a combined neural network and SVGP model. As a result, the SVGP-based radiative physics emulator improved its accuracy by about 20% compared to the artificial neural network emulator. However, the computation speed was about 3 to 9 times slower than the neural network emulator, but it was faster than the computation speed of the NWP model. This suggests that statistical emulators can be used to replace NWP model simulators.
This study introduces the application of a spiral structure in pipeline transportation to enhance the pipeline capacity of a slurry shield circulation system. A numerical model of solid–fluid ...coupling in a pipeline with a double spiral structure was established based on computational fluid dynamics and the discrete element method (CFD–DEM) theory, and its validity was confirmed through comparison with experimental data. The characteristics of the spiral pipeline were evaluated by comparing them with those of a conventional smooth straight pipeline. A multi-objective optimization method for determining the structural parameters of a spiral pipeline in a slurry shield circulation system is proposed using the Kriging surrogate model. The optimized design of a spiral pipeline was demonstrated through a specific project, and the impact of the main structural parameters on the slurry transport characteristics of the pipeline was analyzed. The results indicated that the optimized structural parameters led to an 18.65% increase in the average flow rate of the slurry and a 19.13% reduction in the accumulation of stones in the pipeline.
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•The Spiral pipeline with transverse protrusion is applied to slurry-water circulation pipe.•The slag carrying performance of pipeline was evaluated by CFD–DEM method.•The relationship between the structure parameters and the slag carrying performance of the pipeline is studied.•The optimization process is used to optimize the structural parameters.
Meta-heuristic algorithms, which require a large number of fitness evaluations before locating the global optimum, are often prevented from being applied to computationally expensive real-world ...problems where one fitness evaluation may take from minutes to hours, or even days. Although many surrogate-assisted meta-heuristic optimization algorithms have been proposed, most of them were developed for solving expensive problems up to 30 dimensions. In this paper, we propose a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle swarm optimization algorithm (SL-PSO), where the PSO and SL-PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model. Our experimental results on seven benchmark functions of dimensions 30, 50 and 100 demonstrate that the proposed method is competitive compared with the state-of-the-art algorithms under a limited computational budget.
The accuracy of predicting the behaviour of structure using finite element (FE) depends widely on the precision of the evaluation of the stiffness matrix. In the present article, an attempt has been ...made to evaluate the stiffness matrix of functionally graded (FG) nanoplate using Gaussian process regression (GPR) based surrogate model in the framework of the layerwise theory. The stiffness matrix comprises various matrix terms corresponding to the membrane, membrane-bending, bending-membrane, and bending and shear. Following two different methodologies are adopted for predicting the stiffness matrix at the elemental level, one in which the final elemental stiffness matrix is evaluated, and the second one in which all the matrix terms as stated are evaluated separately using the GPR surrogate model and then are added to get the final stiffness matrix at the elemental level. The effectiveness of both approaches has been worked out by comparing the present results with those available in the literature. Both the proposed methodologies can predict the behaviour of FG nanoplates with good accuracy. However, the second one is found to be outstanding.
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