A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simultaneously. Accordingly, a hybrid surrogate model-based multi-objective optimization method with the numerical method for the lightweight and safe design of the SFE canister is proposed. To be specific, the drop analysis model of the SFE canister is firstly established where the finite element method—discrete element method (FEM−DEM) coupled method is integrated to simulate the interaction force between the SFE and canister. Through simulation, the design variables, optimization objectives, and constraints are identified. Then the hybrid radial basis function—response surface method (RBF−RSM) surrogate method is carried out to approximate and simplify the accurate numerical model. A non-dominated sorting genetic algorithm (NSGA-II) is used fo... [more]

In this paper, an improved immune algorithm (IIA) was proposed for the torque ripple reduction optimal design of an interior permanent magnet synchronous motor (IPMSM) for a fuel cell electric vehicle (FCEV) traction motor. When designing electric machines, both global and local solutions of optimal designs are required as design result should be compared in various aspects, including torque, torque ripple, and cogging torque. To lessen the computational burden of optimization using finite element analysis, the IIA proposes a method to efficiently adjust the generation of additional samples. The superior performance of the IIA was verified through the comparison of optimization results with conventional optimization methods in three mathematical test functions. The optimal design of an IPMSM using the IIA was conducted to verify the applicability in the design of practical electric machines.

To achieve high efficiency and low degradation of a polymer electrolyte fuel cell (PEMFC), it is necessary to maintain an appropriate level of humidification in the fuel cell membrane. Thus, membrane humidifiers are typically used in PEMFC systems. Parameter studies are important to evaluate membrane humidifiers under various operating conditions to reduce the amount of physical tests. However, simulative studies are computationally expensive when using detailed models. To reduce the computational cost, surrogate models are set up. In our study, a 3D computational fluid dynamics (CFD) model of a hollow fibre membrane humidifier is presented and validated using measurement data. Based on the results of the validated CFD model, a surrogate model of the humidifier is constructed using proper orthogonal decomposition (POD) in combination with different interpolation methods. To evaluate the surrogate models, their results are compared against reference solutions from the CFD model. Our res... [more]

This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not only reduce computational costs but also to extract dimensionality-reduced features to conserve spatial characteristics. The workflow integrates two different spatial properties within a single convolutional system, and it also achieves accurate reconstruction performance. This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920. The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of... [more]

Using aerodynamic resistance to provide braking force for trains is an economical braking method. It has few components to wear out and requires no energy. But the aerodynamic braking plate will significantly affect train’s aerodynamics behaviors. This paper studies the effect of the braking plates’ layout on the aerodynamic force of head car when a train is running under a crosswind. The results show that the braking plate will not only increase the drag force, but also significantly affect the lift and lateral force of the train’s head car. The installation position of the braking plates will also have a great effect on the aerodynamic force. In order to increase the drag force and weaken other aerodynamic force changes of the head car, we suggest that the first braking plate be arranged at the end of a streamlined shape, and the second braking plate be arranged at the middle of the car body. Compared with trains without braking plates, the head car’s drag force increases by 85.7%, l... [more]

Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.

The application of white box models in digital twins is often hindered by missing knowledge, uncertain information and computational difficulties. Our aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin applications and to explore the fields where surrogate models can be utilised advantageously. In this sense, the paper discusses what types of surrogate models are suitable for different practical problems as well as introduces the appropriate techniques for building and using these models. A number of examples of digital twin applications from both continuous processes and discrete manufacturing are presented to underline the potentials of utilising surrogate models. The surrogate models and model-building methods are categorised according to the area of applications. The importance of keeping these models up to date through their whole model life cycle is also highlighted. An industrial case study is also presented to demonstrate the app... [more]

The installation potential of ground-source heat pump (GSHP) systems has been studied based on the spatial interpolation of numerical simulation results using ground heat exchanger (GHE) models. This study is the first to create an estimation formula for the heat exchange rate (HER) to obtain a solution equivalent to the numerical analysis results considering the average method when supplying three-dimensional (3D) hydrogeological information that affects the HER to a two-dimensional (2D) map. It was found that the main factors affecting the HER were groundwater flow velocity, subsurface temperature, and thermal conductivity. The response surface methodology was utilized to approximate the HER using the above-mentioned three parameters. The estimated HER showed very strong agreement with that calculated by the GHE models. The application of the estimation formula to the simulation of the 3D groundwater flow and heat transport model of the Sendai Plain (Japan) better reflects the hydrog... [more]

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wak... [more]

This study developed a data-driven surrogate model based on a deep neural network (DNN) to evaluate gas−liquid multiphase flow occurring in horizontal pipes. It estimated the liquid holdup and pressure gradient under a slip condition and different flow patterns, i.e., slug, annular, stratified flow, etc. The inputs of the surrogate modelling were related to the fluid properties and the dynamic data, e.g., superficial velocities at the inlet, while the outputs were the liquid holdup and pressure gradient observed at the outlet. The case study determined the optimal number of hidden neurons by considering the processing time and the validation error. A total of 350 experimental data were used: 279 for supervised training, 31 for validating the training performance, and 40 unknown data, not used in training and validation, were examined to forecast the liquid holdup and pressure gradient. The liquid holdups were estimated within less than 8.08% of the mean absolute percentage error, while... [more]

Two-dimensional design and analysis issues on the meridional surface, which is important in the preliminary design procedure of compressors, are highly dependent on the accuracy of empirical models, such as the prediction of total pressure loss model and turning flow angle. Most of the widely used models are derived or improved from experimental data of some specific cascades with low-loading and low-speed airfoil types. These models may work for most conventional compressors but are incapable of accurately estimating the performance for some specific cases like transonic compressors. The errors made by these models may mislead the final design results. Therefore, surrogate models are developed in this work to reduce the errors and replace the conventional empirical models in the through-flow calculation procedure. A group of experimental data considering a two-stage transonic compressor is used to generate the airfoils database for training the surrogate models. Sensitivity analysis i... [more]

In recent decades, building design and operation have been an important field of study, due to the significant share of buildings in global primary energy consumption and the time that most people spend indoors. As such, multiple studies focus on aspects of building energy consumption and occupant comfort optimization. The scientific community has discerned the importance of operation optimization through retrofitting actions for on-site building energy systems, achieved by the use of simulation techniques, surrogate modeling, as well as the guidance of existing building performance and indoor occupancy standards. However, more knowledge should be attained on the matter of whether this methodology can be extended towards the early stages of thermal system and/or building design. To this end, the present study provides a building thermal system design optimization methodology. A data set of minimum thermal system power, for a typical range of building characteristics, is generated, acco... [more]

It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods. These approaches use various mesh techniques to discretize a complicated geometry and eventually convert governing equations into finite-dimensional algebraic systems. To date, many attempts have been made by exploiting machine learning to solve flow problems. However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems and demonstrated its robustness in generating reliable solutions. Instead of feeding neural networks large labeled data, we exploited the known physical laws and incorporated this physics into a neural network to relax the strict requirement of big data and improve p... [more]

Global optimization of industrial plant configurations using organic Rankine cycles (ORC) to recover heat is becoming attractive nowadays. This kind of optimization requires structural and parametric decisions to be made; the number of variables is usually high, and some of them generate disruptive responses. Surrogate models can be developed to replace the main components of the complex models reducing the computational requirements. This paper aims to create, evaluate, and compare surrogates built to replace a complex thermodynamic-economic code used to indicate the specific cost (US$/kWe) and efficiency of optimized ORCs. The ORCs are optimized under different heat sources conditions in respect to their operational state, configuration, working fluid and thermal fluid, aiming at a minimal specific cost. The costs of 1449.05, 1045.24, and 638.80 US$/kWe and energy efficiencies of 11.1%, 10.9%, and 10.4% were found for 100, 1000, and 50,000 kWt of heat transfer rate at average tempera... [more]

In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computa... [more]

Permanent-magnet linear motors (PMLMs) are widely used in various fields of industrial production, and the optimization design of the PMLM is increasingly attracting attention in order to improve the comprehensive performance of the motor. The primary problem of PMLM optimization design is the establishment of a motor model, and this paper summarizes the modeling of the PMLM electromagnetic field. First, PMLM parametric modeling methods (model-driven methods) such as the equivalent circuit method, analytical method, and finite element method, are introduced, and then non-parametric modeling methods (data-driven methods) such as the surrogate model and machine learning are introduced. Non-parametric modeling methods have the characteristics of higher accuracy and faster computation, and are the mainstream approach to motor modeling at present. However, surrogate models and traditional machine learning models such as support vector machine (SVM) and extreme learning machine (ELM) approac... [more]

A permanent magnet actuator (PMA) is a critical device for transforming, transmitting, and protecting electrical energy in renewable energy systems. The reliability of a PMA exerts a direct effect on the operational safety, stability, and reliability of renewable energy systems. An effective fault diagnosis and adjustments for manufacturing processes (MPs) are vital for improving the reliability of a PMA. However, the state-of-the-art fault diagnosis methods are mainly used for single process parameters, extensive sample data, and automated manufacturing systems under real-time monitoring and are not applicable to a PMA with low levels of automation and high human factor-induced uncertainties. This study proposes a novel fault diagnosis approach based on a surrogate model and machine learning for multiple manufacturing processes of a PMA with insufficient training data due to human factor uncertainties. First, a surrogate model that correlated the MP parameters with the output characte... [more]

Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvan... [more]

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is us... [more]

A good turbine casing cooling design should control the thermal stress and maintain a reasonable tip clearance between the turbine blade and the casing. Since the turbine inlet temperature has been increased yearly, the influence of thermal radiation on the temperature of a turbine casing has become more significant. Therefore, the heat transfer characteristics of a turbine casing considering the radiation effect need to be precisely predicted. In this study, a theoretical model is established for describing the heat transfer characteristics of a turbofan casing, and the model’s effectiveness is verified by comparing the numerical and experimental results. Based on the validated model, the effects of single changes of the wall temperature, cooling air temperature, Reynolds number, and surface emissivity on the heat transfer of the casing are discussed. The results show that the increment of cooling air temperature and surface emissivity leads to the enhancement of the average radiative... [more]

In this paper, a methodology based on data-driven models is developed to predict the NOx emissions of an internal combustion engine using, as inputs, a set of ECU channels representing the main engine actuations. Several regressors derived from the machine learning and deep learning algorithms are tested and compared in terms of prediction accuracy and computational efficiency to assess the most suitable for the aim of this work. Six Real Driving Emission (RDE) cycles performed at the roll bench were used for the model training, while another two RDE cycles and a steady-state map of NOx emissions were used to test the model under dynamic and stationary conditions, respectively. The models considered include Polynomial Regressor (PR), Support Vector Regressor (SVR), Random Forest Regressor (RF), Light Gradient Boosting Regressor (LightGBR) and Feed-Forward Neural Network (ANN). Ensemble methods such as Random Forest and LightGBR proved to have similar performances in terms of prediction... [more]

The parametric representation and aerodynamic shape optimization of a three-dimensional circular-to-rectangular transition nozzle designed and built using control lines distributed along the circumferential direction were investigated in this study. A surrogate model based on class/shape transformation, principal component analysis and radial basis neural network was proposed with fewer design parameters for parametric representation and performance parameter prediction of the three-dimensional circular-to-rectangular transition nozzle. The surrogate model was combined with Non-dominated Sorting Genetic Algorithm-II to optimize the aerodynamic shape of the nozzle. The results showed that the surrogate model effectively achieved the parametric representation and aerodynamic shape optimization of the three-dimensional circular-to-rectangular transition nozzle. The geometric dimensions and performance parameters of the parametric reconstructed model were comparable to that of the initial... [more]

In this study, the blade shape of the squirrel-cage fan system inside the range hood was optimized using the surrogate model to improve the maximum volume flow rate. The influence of computational fluid dynamics (CFD) noise was concerned. The regression Kriging model (RKM) was used as a surrogate model to reflect the relationship between the design parameters of the blade and the volume flow rate. The parallel filling criterion after re-interpolation was used to improve the optimization efficiency further and ensure global optimization. Through experimental verification, we found that the relative error between the volume flow rate of the optimal sample of RKM and the experiment was only 0.4%. Compared with the prototype, the maximum volume flow rate of the optimal sample of RKM was increased by 2.9%, and the efficiency under the corresponding working conditions was increased by 2%. RKM was used to predict the velocity field of the volute and impeller exit section to explore the feasib... [more]

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input−output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-r... [more]

This work focuses on the determination of the design and operation parameters of a thermal system depending on the optimization objective set. Its main objective and contribution concern the proposal of a generalized methodological structure involving multiobjective optimization techniques aimed at providing a solution to a practical problem, such as the design and dimensioning of a solar thermal system. The analysis is based on system operation data provided by a dynamic simulation model, leading to the development of multiple surrogate models of the thermal system. The thermal system surrogate models correlate the desired optimization objectives with thermal system design and operation parameters while additional surrogate models of the Pareto frontiers are generated. The implementation of the methodology is demonstrated through the optimal design and operation parameter dimensioning of a solar-assisted geothermal heat pump that provides domestic hot water loads of an office building... [more]