Efficient Simulation Model Research Articles

Fast transonic flow prediction enables efficient aerodynamic design

A deep learning framework is proposed for real-time transonic flow prediction. To capture the complex shock discontinuity of transonic flow, we introduce the residual network ResNet and deconvolutional neural networks to learn the nonlinear discontinuity phenomenon in transonic flow, which is affected by the Mach number, angle of attack, Reynolds number, and aerodynamic shape. In our framework, flow field variables on actual grid points are utilized in the neural network training to avoid the interpolation operation and the input of spatial position with a point cloud that is required with traditional convolutional neural networks. To investigate and validate the proposed framework, transonic flows around two-dimensional airfoils and three-dimensional wings are utilized to verify its effectiveness and prediction accuracy. The results prove that the model is able to efficiently learn the transonic flow field under the influence of the Mach number, angle of attack, Reynolds number, and aerodynamic shape. Significantly, some essential physical features, such as shock strength and location, flow separation, and the boundary layer, are accurately captured by this model. Furthermore, it is shown that our framework is able to make accurate predictions of the pressure distribution and aerodynamic coefficients. Thus, the present work provides an efficient and robust surrogate model for computational fluid dynamics simulation that enhances the efficiency of complex aerodynamic shape design optimization tasks and represents a step toward the realization of the digital twin concept.

From single proteins to efficient simulation models of the cytoplasm.

The properties of biomolecules in crowded in-cell environments are governed by interactions that range from repulsive volume exclusions to non-specific transient binding and molecular recognition. Entropic volume exclusion and its impact on biomolecules can be estimated using analytical theory, but transient binding and molecular recognition further depend on electrostatics, hydrogen bonds, van der Waals forces and solvent-mediated interactions. These interactions strongly depend on microscopic details on the molecular scale, which impedes the development of general theories. Instead, computer simulations provide a useful tool to interpret in-cell experiments and to expand our understanding of biomolecular crowding.

Extended UH model and deformation prediction of high-speed railway subgrade

For the rapidity of trains in high-speed railway, it is crucial to guarantee the reliable and safe operation. Strict controls are needed to prevent excessive subgrade deformation. In order to perform more accurate subgrade deformation calculation, the constitutive model used in simulation should demonstrate the small-strain stiffness for uneven settlement calculation, and illustrate time-induced deformation for post-construction deformation. An E-UH model is established based on the original UH model. In the E-UH model, the elastic hysteresis relation is added to describe the initial high stiffness and nonlinear stiffness decay. In addition, the E-UH model also couples the equivalent formula between overconsolidated characteristics and time-induced deformation. Then the effectiveness of this new model is testified by element test prediction. By means of the second development interface of a finite element software, the E-UH model is developed as a subroutine and applied to numerical simulation of subgrade deformation. The simulation represents that the E-UH model is capable of performing deformation prediction during the subgrade construction and post-construction phases, and indicates that considering small-strain stiffness can make the uneven settlement more prominent. This research provides a practical and efficient constitutive model for numerical simulation of the high-speed railway subgrade deformation.

Thermo-hydraulic condition optimization of large-scale complex district heating network: A case study of Tianjin

District heating (DH) networks are indispensable infrastructure for space and domestic heating with high energy efficiency. As the structures of DH networks are gradually becoming complex, efficient and accurate simulation model for the operational optimization of the DH network is crucial. In this paper, an optimization method for the DH network operation is proposed. The method is based on the thermo-hydraulic coupled dynamic model, sequential quadratic programming (SQP) and particle swarm optimization (PSO), which is applied to a large-scale DH network in Tianjin, China. With the proposed method, 6.7%∼11% energy consumption can be reduced, under the operation condition of 80%∼100% design flow rate. The transmission and distribution cost can be reduced with an average of 6.2% at the outdoor temperature ranging from −5 to 5 °C.

Efficient computational framework for image-based micromechanical analysis of additively manufactured Ti-6Al-4V alloy

The increase in additively manufactured (AM) Ti-6Al-4V alloys in high-performance industrial applications has necessitated the development of robust computational models that can aid in their qualification and certification. Physics-based micromechanical models, relating the AM-processed material microstructure and defect state with the overall material response and life, can play an important role in reducing uncertainty in component behavior and increasing acceptance. Motivated by this need, the present paper develops a novel image-based crystal plasticity finite element model (CPFEM) for efficient micromechanical simulation of the additively manufactured Ti-6Al-4V alloy, whose Widmanstätten microstructure is characterized by 12 HCP α lath variants in the parent β grain. A unique feature of this work is the creation of an efficient crystal plasticity framework for the parent β grain polycrystalline ensembles with parametric representation of the α lath statistics of size, shape, orientation, and crystallography. This statistical representation is expected to significantly enhance its efficiency over models that represent each α lath explicitly in the microstructure. Defects in the form of voids are represented at two scales. The smaller voids in the microstructure are manifested as porosity or void volume fraction distribution in the crystal plasticity model. Larger voids are represented explicitly in the statistically equivalent microstructural volume element (SEMVE) model. The models are built from experimentally acquired electron back scatter diffraction (EBSD) and micro-focus X-ray computed tomography (XCT) images and calibrated and validated with mechanical testing data. This paper extends the developments in Pinz et al. (2022) through the development of a special self-consistent boundary condition in the context of a concurrent model to overcome limitations of periodicity boundary conditions. The concurrent model embeds the SEMVE in a homogenized exterior domain represented by a rate-dependent isotropic plasticity model. Parametric studies are conducted to comprehend the effect of void size, shape and orientation on the overall material response.

P‐10.4: Lattice Boltzmann free energy model for accurate and efficient simulation of electrowetting‐based reflective display

P‐10.4: Lattice Boltzmann free energy model for accurate and efficient simulation of electrowetting‐based reflective display

Optimization of French Hospital Department Activities Using an Efficient Discrete Event Simulation Model

A mismatch between patient demand and emergency departments (EDs)’ capacity to deliver care often leads to poor patient flow and overcrowding, and therefore an increase in waiting. This paper presents a mid-term decision support system dealing with scheduling and allocation of resources in the ED of the Hospital Center of Troyes, France. An efficient performance evaluation tool based on discrete event simulation (DES) is developed. Through a what-if scenario strategy, 145 scenarios were simulated that allowed us to create a new benchmark that does not exist in the literature. To rank and select the best alternatives, the VIKOR multicriteria analysis method and the entropy method were used. With the objective of minimizing the waiting time and inpatient stay under an additional resource cost constraint, this work has helped to quantify and analyze the impacts related to the structural and organizational reorganization of the ED.

Studying CdS:In green phosphor's impacts on white-light emitting diode with higher luminous flux

Abstract A lumen efficiency (LE) simulation model with the Monte Carlo method is introduced to the white-light-emitting diode devices that utilize the red LED (light-emitting diode) dyes instead of red-emitting phosphors (R-WLEDs). By simulating this model, the desirable spectrum-related indices and photometric efficiencies, which are adequate for superior chromatic consistency (or R f > 97), can be accomplished for R-WLEDs under the correlated color temperature (CCT) range of 5000–8000 K. The structure of the R-LED has LEDs in red and blue colors (650 nm and 448 nm) combined with phosphors possessing yellow and green emissions (586 nm and 507 nm). In comparison with pc-WLEDs (WLEDs operating with conversion phosphors) and QD-WLEDs (WLED devices with quantum dots), pc/R-WLED devices could present its outperformance to the others and become a promising way to achieve remarkable chromatic generation, particularly under the condition of small color temperature limit, and act as a substitute for the pc-WLED devices.

An efficient model for simulation of temperature field of steel-concrete composite beam bridges

Thermal performance between steel and concrete differs significantly and leads to the complexity of the temperature field and effect of steel–concrete composite bridges. Refined numerical models and accurately calibrated thermal parameters can solve the transient analysis of complex temperature fields. However, the method is inefficient and difficult to apply to analyzing massive historical data in calculating design temperature loads. In this paper, a vertical discrete and dimensionality-reduced model of the steel–concrete composite bridge is deduced and established for rapid temperature load calculation. A MATLAB-based state equation solving method is proposed to implement the one-dimensional efficient numerical model. Besides, an outdoor sunshine experiment is conducted to study the temperature field. The outdoor sunshine experiment measurements and refined model simulations verify the efficient numerical model. The computational efficiency of the efficient model is nearly 1000 times higher than that of the refined model, achieving rapid calculation of the composite bridge temperature field. The efficient thermal model proposed in this paper provides a powerful tool for analyzing the temperature field and effect and calculating the design temperature load of the composite bridge.

A Practical Model for Selecting Nonwoven Cover Date Bunch via Finite Element Analysis Using COMSOL Multiphysics

ABSTRACT Water injury and fungus infection are the main causes of large economic deterioration and losses in the quality of date fruits during harvesting period. The nonwoven bagging product was used for the first time on date palm and has been shown to efficiently protect date fruit from heavy rain and sunburn that reduces the rate of transpiration and increases fungus spoilage. The aim of this study is to evaluate the potential applicability of COMSOL code in simulating the heat transfer behavior through nonwoven date cover. The study also intended to use finite element method and adaptive mesh refinement technique to increase the accuracy of the solution and create model for efficient simulation of heat exchange in nonwoven bag. The proposed model was validated by the uniformity between simulating and experimental results. A detailed numerical study was conducted to find the influence of air permeability on heat transfer through nonwoven textiles. Results demonstrated that the reduction of nonwoven permeability is beneficial to minimize the heat taken away by forced convection by decreasing the inner air transportation.

Systematic bottom-up molecular coarse-graining via force and torque matching using anisotropic particles.

We derive a systematic and general method for parameterizing coarse-grained molecular models consisting of anisotropic particles from fine-grained (e.g., all-atom) models for condensed-phase molecular dynamics simulations. The method, which we call anisotropic force-matching coarse-graining (AFM-CG), is based on rigorous statistical mechanical principles, enforcing consistency between the coarse-grained and fine-grained phase-space distributions to derive equations for the coarse-grained forces, torques, masses, and moments of inertia in terms of properties of a condensed-phase fine-grained system. We verify the accuracy and efficiency of the method by coarse-graining liquid-state systems of two different anisotropic organic molecules, benzene and perylene, and show that the parameterized coarse-grained models more accurately describe properties of these systems than previous anisotropic coarse-grained models parameterized using other methods that do not account for finite-temperature and many-body effects on the condensed-phase coarse-grained interactions. The AFM-CG method will be useful for developing accurate and efficient dynamical simulation models of condensed-phase systems of molecules consisting of large, rigid, anisotropic fragments, such as liquid crystals, organic semiconductors, and nucleic acids.

An efficient simulation model for layout and mode performance evaluation of robotic mobile fulfillment systems

The robotic mobile fulfillment system (RMFS) is a “goods to man” picking system that employs numerous automatic guided vehicles (AGVs) instead of workers to transport goods from the storage station to the picking station. Using smarter AGVs enhances the performance but also leads to layout problems and the challenge of efficiently estimating the system performance with various layouts. With the same capability of storage, the different row and column of storage position area can lead to different system performance. The number of combinations of rows and columns is huge which makes it time-consuming to compare all the combinations to find the best one, and different path modes (unidirectional path mode and bidirectional path mode) and different number of AGVs further complicate the problem. Quickly modeling and performance estimating of RMFS become the biggest challenge for logistics enterprise. In this paper, according to the modular simulation theory, an efficient model based on discrete-event system simulation is proposed for quickly constructing different RMFS layouts and estimating their performance. The model contains two independent components: a physical component and a control component. The nuclear module for physical component is the track module that forms all tracks and can be temporarily or permanently blocked to satisfy the requirements of both unidirectional and bidirectional path mode models. The main functions of the control component are routing, collision prediction, and collision avoidance. The physical and control components are independent but work together to construct and run a certain model according to the input parameters; thus, an RMFS model can be efficiently built by simply entering parameters. A series of experiments were conducted to evaluate the performance of the RMFS in different layouts with various numbers of AGVs in both the unidirectional and bidirectional path modes. The results indicated that in the same layout, the bidirectional path mode can reach the summit output with fewer AGVs. The experimental results also indicated that in a large-scale RMFS, vertically arranging the storage station on the block island and then horizontally positioning the block island can result in a higher output than other layouts. In addition, the experimental results indicated that the proposed model can quickly construct different RMFS layouts and efficiently determine the best scheme.

Efficient multibody modeling of offshore wind turbines with flexible substructures

Offshore wind turbines, especially floating wind turbines, are often simulated assuming rigid substructures to obtain computationally efficient simulation models for preliminary parameter variation studies. This causes large errors in the determination of coupled natural frequencies and internal loads, particularly with increasing turbine sizes. Finite Element models for flexible substructures were developed by several researchers, often resulting in a high simulation effort. In this paper, a modally reduced Finite Element model, precomputed by the SubDyn module of OpenFAST, is directly included in the generalized Equation of Motion of the Simplified Low Order Wind turbine model SLOW. The approach was tested with the DTU10MW reference wind turbine mounted on a flexible monopile. It shows a high agreement with the former beam-based Multibody System in the calculated coupled natural frequencies and steady state results both for the linear and nonlinear model. A basis has been established to integrate flexible bodies of any shape even into computational efficient Multibody Systems of reduced order, such as SLOW, without coupling of two modules as in OpenFAST. This might improve numerical stability due to unified equations of motion.

An XFEM-based approach for 3D hydraulic fracturing simulation considering crack front segmentation

Hydraulic fracturing is a commonly adopted and effective well stimulation technique in the oil and gas extraction area. Under a mixed I/III loading condition, the crack front segmentation (i.e., the parent crack segments into echelon-shaped daughter cracks) usually occurs, which highly complicates the paths of fluid-driven cracks. This paper presents an efficient numerical model for 3D hydraulic fracturing simulation considering crack front segmentation on basis of the extended finite element method (XFEM). Solutions of the momentum balance equation and the fluid flow equation are simultaneously determined by the Newton-Raphson method along with a reduction technique. In the XFEM framework, a robust local mesh-refinement scheme of the tip-enriched elements is designed to enhance the resolution of the near-front stress field which is crucial for the determination of crack segmentation and propagation behaviors. The locally refined tip-enriched elements are then divided into a series of tetrahedra to perform high-accuracy numerical integration. After verification of the proposed approach, the effects of several critical parameters in hydraulic fracturing treatments are investigated. Results show that crack front segmentation has significant effects on the resulting crack paths and crack aperture distribution. The propagation of hydraulic fractures will be depressed on account of the stress shadow induced by overlapped segments, leading to higher pumping pressure compared to the case without considering front segmentation. The sensitivity analyses indicate that larger elastic modulus of rock formation, larger fluid viscosity, higher fluid pumping rate, and smaller fluid leak-off coefficient can alleviate the influence of crack front segmentation on the pumping pressure. Larger elastic modulus, larger fluid viscosity, higher fluid pumping rate, and greater fluid leak-off coefficient lead to smaller twisting angles of the segments and smaller overlapping ratios.

Simulation Research on Flow Boiling and Heat Transfer of Micro-channel for Electronic Cooling

Micro-channel heat exchanger is a popular device for the electronic cooling. Heat transfer of flow boiling in micro-channel is a research hotspot. For the convenience of the theoretical research on micro-channel, a high efficiency simulation model on flow boiling and heat transfer of micro-channel was established. In the simulation research, the influence rules of operating conditions and thermal properties of refrigerants (working fluids) on the flow boiling were studied; the distribution rules along flow direction of characteristic parameters such as vapor quality, Heat Transfer Coefficient (HTC), pressure drop and bottom wall temperature of micro-channel T w were analyzed; the key factors on the distribution of characteristic parameters were proposed. The characteristic parameters based on the refrigerants R134a and R1234ze(E) in micro-channel were compared and researched. According to the simulation results, HTC and pressure drop of R134a are larger and smaller than those of R1234ze(E) respectively, the uniformities of HTC and T w of R134a are all better than those of R1234ze(E) respectively.

A Hybrid Model for Fast and Efficient Simulation of Fluid Power Circuits With Small Volumes Utilizing a Recurrent Neural Network

The modeling and simulation of fluid power systems are essential parts of the real-time simulation of virtual prototypes of mobile working machines. In several cases in the dynamic simulation of such fluid power systems, a longer simulation time is required. This makes the traditional mathematical models inefficient for real-time simulations, particularly when simulating fluid power systems because of the small volumes in stiff differential equations of pressure. The main reason for such an issue in the simulation of fluid power systems is the existence of small oil volumes in stiff differential equations of pressure. To overcome this issue, a novel hybrid model is proposed for stiff fluid power systems simulation. The main feature of the model is the utilization of a recurrent neural network instead of stiff differential equations of pressure with small volume. At the same time, the dynamics of the rest system are traditionally presented with algebraic and differential equations. The testing results of the introduced hybrid model showed that the novel method can reduce the simulation time, which makes the model suitable for real-time applications. Moreover, the accuracy of the model remains at a fairly high level compared to traditional mathematical models.

Discrete model for 3D motion of helical springs

Large displacements of coiled springs are affected by both contact between coils and geometric non-linearities. The two phenomena also interact with each other and the only technology that efficiently accounts for both is Finite Elements Method (FEM). Such analysis can become very expensive, especially when a great number of elements and contacts are involved, as it is often the case. A discrete 3D model for fast and efficient simulation of non-linear springs is presented. The discrete model is based on equivalent beam theory and each node is associated with a contact region placed on the helix. Thus contact is accounted on its real occurrence and fast non-linear solution procedure is implemented. A study case is presented and comparison with FEM is discussed.

Improvement of 3D-simulation models based on experiments

The accuracy of manufacturing machines and specially the products of automatic panel benders is continuously improving. Within the last ten years the permissible deviations of the bending angles have been reduced in the specification by 50%, from 1.0° to 0.5°. This accuracy is to be achieved from the first performed bend. In order to further improve and shorten the design process, a parameter identification is carried out based on measurements on the real machine. A good pre-adjustment and tuning of the developed digital twin with respect to the bending process of various sheet metals with a large variety of operating parameters has been derived. In this contribution it is shown how the computation results of the highly efficient 3D-FEM simulation models are matched with the real bending machine in order to derive further improvements.

Physics-informed deep learning model in wind turbine response prediction

Subjected to strong cyclic wind and wave loads, wind turbines could experience severe fatigue damages and possibly fail to function normally due to accumulated damages at certain critical locations. Therefore, fatigue damage evaluation and prediction are essential and important to be conducted, which could involve massive numerical simulations and computational costs due to dynamic analyses of the wind turbines under various environmental conditions. To reduce the calculation cost related to the time-consuming dynamic analysis, sequence models such as the recurrent neural network (RNN) and the long-short term memory model (LSTM) originated from the deep learning topic are good and promising candidates to predict structural dynamic responses at multiple critical locations under different environmental scenarios. However, the training cost and prediction accuracy of these deep learning models might not be satisfiable since these models are purely data-driven and require significant amount of training data and a large number of training parameters. To reduce the computational cost and improve the prediction accuracy, a hybrid method that integrates the physical information of the underlying wind turbine system into the data-driven model is implemented in the present study as a computationally efficient simulation model. Structural properties and linearized representations of the wind turbine system are served as the physical constraints and applied in a recently proposed deep residual recurrent neural network (DR-RNN) to form as a physics-informed deep learning model. This physics-informed model is first applied to a frame structure with four degrees of freedom as a benchmark study to show the accuracy and efficiency of this model. The applicability of this physics-informed model to a complex wind turbine system is then investigated, and the performance of the developed physics-informed model on the structural response prediction is also compared with a regular data-driven model.

Platform and mooring system optimization of a 10 MW semisubmersible offshore wind turbine

In this paper, an optimization procedure is proposed to find platform and mooring system configurations which most effectively reduce the dynamic response of a semisubmersible 10 MW Floating Offshore Wind Turbine (FOWT). This is done by developing an efficient frequency domain simulation model able to account for the viscous drag forces and the contributions to the equation of motion stemming from turbine and mooring lines. The objective function is the value of the Response Amplitude Operator (RAO) at the eigenfrequency of the selected degree of freedom (DoF) of the system. Both parked and power production states are investigated. Feasibility constraints related to mean displacements and moorings layout are considered. Results show that optimized configurations can be found with better performances and smaller platform dimensions with respect to the configuration obtained by scaling up the 5 MW geometry.