Efficient Modeling Method Research Articles

Analysis and optimization of the efficient press shop operation

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.

Promoting Upcycled Food: An analysis of social media communication strategies of Upcycled Food Association

Global food loss and waste are significant challenges in the food system, critical to addressing food insecurity and national security in the U.S. Upcycled food, which reduces waste at the source, is a promising solution. Social media offers both opportunities and challenges for communicating food benefits. This study examines publicly available data from Instagram, Facebook, and Twitter of a non-profit organization Upcycled Food Association (UFA) using content analysis and machine learning techniques. The analysis revealed that social media posts focused on promoting food waste awareness and benefits related to sustainability, health, and the economy, with environmental information being prioritized. UFA employs diverse impact information frames and uses positive emotion framing to increase food waste awareness and empower consumers by highlighting the impact of their purchasing power. Communication strategies are tailored to the unique characteristics of each social media platform and its users. We recommend social media initiatives continue emphasizing food waste awareness and upcycling, alongside increasing health information to encourage upcycled food consumption. Diverse strategies with diverse benefits and tailored to each platform's characteristics and sociodemographic groups are recommended. Future research can explore efficient topic modeling methods in larger datasets in social media analysis.

Optimized Dropping and Rolling and Virtual Surface Method for High Packing Fraction of Dispersed Fuel Particles in Annular Container

Because of their favorable thermal conductivity and commendable fuel performance, dispersed fuel elements have been widely adopted in research reactors. However, the geometric complexity resulting from the high packing density of dispersed fuel poses challenges to current geometric modeling methods. Various implicit and explicit modeling techniques have been implemented in Monte Carlo codes. Implicit methods struggle to achieve the correct packing fraction and, therefore, lack accuracy. In this paper, we introduce the Optimized Dropping and Rolling and Virtual Surface method for precise and efficient geometric modeling of such structures. Simultaneously, we apply this novel method to the packing process of an annular container. Test results across various packing fraction cases demonstrate that our modified Optimized Dropping and Rolling method significantly enhances packing efficiency, surpassing the Distinct Element Method hundreds of times.

A novel extended social force model for studying the impact of the heterogeneity of pedestrian physical fitness on emergency evacuation efficiency

Emergencies in public places, particularly confined crowded areas, will disrupt the stability of dense crowds and consequently lead to accidents. To promote public emergency safety, there is a pressing need for efficient modeling methods to investigate the evacuation mechanism in these places and improve the social public safety. This study proposes a Physical Fitness Heterogeneity based Social Force Model (PFH-SFM) that takes into account the heterogeneous desired evacuation velocity caused by the heterogeneity of pedestrian physical fitness, by means of developing the normalized desired velocity ratio. Then, we use PFH-SFM to investigate the relationships between the escape rate and the desired velocity, and between the evacuation duration and the desired velocity in terms of various group sizes with heterogeneous physical fitness, the relationship between the percentage of reduction in evacuation duration and desired velocity when including weak pedestrians, the pedestrian distribution in the evacuation process, the relationship between the total evacuation duration and the desired velocity in terms of various proportions of weak pedestrians and the relationship between the evacuation duration and the desired velocity in terms of various normalized starting and ending velocity ratios by considering various group sizes, respectively. The findings of this study show that the existence of a certain small proportion of pedestrians with weak physical fitness can promote global evacuation dynamics, especially in the case of high crowded density, and can reduce evacuation duration by up to 20% in our experiments. Additionally, when the percentage of pedestrians with weak physical fitness is relatively high, they tend to have a detrimental effect on the evacuation efficiency. Furthermore, there exists a moderate normalized desired starting velocity ratio that maximizes the overall evacuation efficiency; on the other hand, the lower the normalized desired ending velocity ratio is, the more efficient the evacuation is. To the best of the authors’ knowledge, this study is the first time to introduce the concepts of normalized desired starting and ending velocity ratios and innovatively analyzes the impact of the continuously changing desired velocity of pedestrians on the evacuation efficiency in multi-exit scenarios. The results offer valuable insights for relevant stakeholders to formulate effective evacuation plans, so as to enhance urban emergency capacity and minimize social and economic losses.

Efficient BFGS quasi-Newton method for large deformation phase-field modeling of fracture in hyperelastic materials

The prediction of crack propagation in materials is a crucial problem in solid mechanics, with many practical applications ranging from structural integrity assessment to the design of advanced materials. The phase-field method has emerged as a powerful tool for modeling crack propagation in materials, due to its ability to accurately capture the propagation of cracks. However, current phase-field algorithms suffer from the elevated computational cost associated with the so-called staggered solution scheme, which requires extremely small time increments to advance the crack due to its inherent conditional stability. In this paper, we present, for the first time, a quantitative analysis detailing the numerical implementation and comparison of two common solution strategies for the coupled large-deformation solid-mechanics-phase-field problem, namely the quasi-Newton based Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) and the full Newton based alternating minimization (or staggered) (AM/staggered) algorithm. We demonstrate that the BFGS algorithm is a more efficient and advantageous alternative to the traditional AM/staggered approach for solving coupled large-deformation solid-mechanics-phase-field problems. Our results highlight the potential of the quasi-Newton BFGS algorithm to significantly reduce the computational cost of predicting crack propagation in hyperelastic materials while maintaining the accuracy and robustness of the phase-field method.

An overset-grid finite-difference algorithm for seismic wavefield propagations modelling in the polar coordinate system with a complex free-surface topography

Summary Nowadays, finite-difference method has been widely applied to simulate seismic wavefield propagation in a local seismic model with a complex topography by utilizing curvilinear grids and traction image method in Cartesian coordinates system. For a global seismic model in polar coordinate system, it is still a challenge for the conventional finite-difference method to deal with the grid singularity at the center and the topographic free surface at the top. In this study, we develop a finite-difference method in polar coordinates for seismic wavefield propagation in the 2D global model. In the proposed finite-difference method, the overset-grid algorithm is used to handle the grid singularity at the center, and the curvilinear grid technique as well as the traction image method are applied to implement free-surface boundary condition on the complex topography. The proposed finite-difference method is validated in flat and topographic free-surface models by comparing synthetic waveforms with reference solutions. The seismic wavefield propagation in a realistic Mars profile is computed by the proposed finite-difference method. The proposed finite-difference method is an efficient and accurate method for seismic wave modeling in the polar coordinate system with a complex free-surface topography.

A machine learning based EMA-DCPM algorithm for production scheduling

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, “5+X” was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.

Modeling magnetic soft continuum robot in nonuniform magnetic fields via energy minimization

The emerging magnetic soft continuum robots (MSCRs) – a type of slender and soft rods with embedded hard-magnetic particles – hold great promise in endovascular intervention via remote magnetic actuation. Although numerous advantages of using permanent magnets have been demonstrated for manipulating MSCRs (e.g., simple systems with high actuation force, large operating workspace), the magneto-mechanical behavior of MSCRs in nonuniform magnetic fields, particularly those generated by permanent magnets, remains largely unexplored. In this work, a systematic study of MSCRs in the nonuniform field is presented, which includes theoretical modeling using hard-magnetic elastica theory, numerical analyses by energy minimization method, finite element simulations using ABAQUS user element (UEL), and experimental validation. Without solving governing differential equations, the large deflection of MSCRs is efficiently obtained via the minimization of the total potential energy using sequential quadratic programming (SQP). This efficient modeling method offers insights into the control of MSCRs using nonuniform magnetic fields. Two practical strategies are provided for precisely controlling MSCRs by manipulating a cubic magnet through the adjustment of the actuation distance, rotation angle, and spin angle, laying the foundation for applications of magnetically-assisted endovascular intervention.

Efficient Modeling of Ship Structure Based on Image Recognition and Data Retrieval

In order to significantly improve the efficiency of ship structure modeling, an efficient ship structure modeling method based on image recognition and data retrieval is proposed. First of all, the typical Panel has been designed, according to specific data standards, the data and drawings are saved to the database. Then when a new Panel needs to be created, compare the 2D drawings of the structure around the Panel through image recognition to get a similar structure and confirm which Panel's script file in the database is used. Finally, using the standard numbering method, the mapping relationship between the original Panel and the target Panel topology objects and the transformation logic of coordinate data are calculated, and then the target Panel script file is generated. This is the method of efficient modeling in this paper.

Performance prediction of co-rotating disk cavity with finned vortex reducer based on machine learning

Machine learning method was applied for rapid and precise prediction of flow and heat transfer characteristics within co-rotating disk cavity with a finned vortex reducer. A new data preprocessing scheme was developed to reduce modeling cost. By using this scheme, classical radial basis function neural network (RBFNN) shows better prediction performance compared with deconvolutional neural network. Furthermore, RBFNN has a simpler topological structure and fewer hyperparameters. By testing, the relative root mean square error for total pressure, total temperature, and swirl ratio is 0.51 %, 0.32 %, and 1.99 %, and corresponding coefficient of determination reaches 99.68 %, 96.55 %, and 99.16 %. The effects of input parameters on the outputs of RBFNN were analyzed, and a sensitivity analysis was conducted. Within the investigated parameter range, the total pressure loss increases as dimensionless mass flow rate, rotational Reynolds number, and radial position of the fin increases. The total temperature drop increases with the increase of dimensionless mass flow rate and rotational Reynolds number, but the decrease of radial position of the fin. Additionally, an increase in dimensionless mass flow rate or a decrease in rotational Reynolds number causes the increase of swirl ratio. This research provides an efficient modeling method for components in secondary air system in gas turbines.

Design and implementation of a collaborative modeling architecture based on MDE (Multi-DDS Edge)

Abstract Virtual space is an integral part of the Metaverse. Typical virtual spaces are modeled step by step using artificial means. With the development needs of Metaverse modeling and simulation technology and the need for simulation fidelity and content richness in virtual space construction, efficient and correct modeling methods are required. It has become a bottleneck restricting the efficiency of virtual space modeling. This paper designs a multi-person collaborative virtual space modeling method, which improves the modeling efficiency, communication security, and interaction stability of the virtual space collaborative modeling method. Compared with existing collaborative modeling methods, this paper proposes a virtual space collaborative modeling method based on multi-edge DDS technology. By constructing MDE (multi-DDS edges) and MDE on user migration, collaborative construction in multiple virtual spaces is completed. Model data communication extends collaborative modeling technology from the virtual space creation stage to the virtual space simulation and deduction stage, and designs and verifies collaborative content, collaborative architecture, and collaborative communication. Experiments show that the architecture synergy efficiency is 145.6%, the architecture synergy efficiency is 212.5%, and the architecture synergy efficiency is 105.4%. As the number of people in collaborative modeling increases, the operational complexity and demonstration take a logarithmic form, meeting the needs of the future development of the metaverse.

Semi-analytical simulation of ultrasound wave propagation in large plate-like structures

The ultrasonic guided wave (UGW) has gained considerable popularity among NDE methods for damage detection and structural health monitoring (SHM) due to its high frequency and low attenuation. Lamb waves are reflected or diffracted by structure boundaries, discontinuities, and damages. It is possible to determine a structure's health, defects, and locations based on its UGW propagation characteristics. In this study, we propose an efficient modeling method for UGW propagation through damaged and scattering plates by combining the Modal Pencil method with Wave Finite Element (WFE) and Hybrid FE/WFE methods and verifying the results with a high-precision elastic finite element model (FEM). With this approach, the transducer-excited ultrasonic field can be modeled, as well as its steady-state and transient propagation, scattering, and reflections at defects and boundaries. Long-range propagation configurations can be efficiently addressed by this method because its computation efficiency is independent of propagation path lengths. As part of the case study, we study the propagation of Lamb waves through a joint metal thin-walled structure with embedded damage. In the case study, different sensor locations are used to validate the received UGW signals quantitatively. Our findings will contribute to the development of a digital twin for monitoring structural health by providing an efficient and robust approach to modeling ultrasound propagation in damaged and large plate-like structures.

An efficient parallel multi-fidelity multi-objective Bayesian optimization method and application to 3-stage axial compressor with 144 variables

Parallel infill sampling is a promising approach to improve the efficiency of multi-fidelity multi-objective Bayesian optimization (MOBO). In existing literature, the number of infill samples per iteration is typically limited to 10. Additionally, the application of the multi-fidelity MOBO method in engineering optimization designs with over 100 variables is rare. To that end, a novel generalized expected improvement matrix (GEIM) criterion is proposed by using generalized reference values for the element in expected improvement matrix. Parallel infill sampling strategy based on GEIM is developed and incorporated into the multi-fidelity MOBO framework. The distinct feature of the proposed method is that the number of infill samples at each iteration can be significantly larger than existing methods (i.e. as much as 100), so as to further enhance the optimization efficiency. Empirical experiment results on analytic problems show that the proposed parallel multi-fidelity MOBO method is highly competitive in comparison with existing methods. To address the curse of dimensionality in optimizing multi-stage axial compressor, an efficient modeling method for the Hierarchical Kriging (HK) is incorporated. The HK predictor for the efficiency of the 3-stage compressor with 144 variables is built in 16.71 s with acceptable accuracy. Remarkable improvements over the two objectives of the 3-stage compressor with 144 variables are achieved simultaneously within 870 high-fidelity CFD simulations.

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

An Efficient Forward Modeling Method of Electromagnetic Response of Multiscale Hydraulic Fracture Based on Deep Learning

An Efficient Forward Modeling Method of Electromagnetic Response of Multiscale Hydraulic Fracture Based on Deep Learning

Modelling of flexural fatigue behaviours of hybrid engineered cementitious composite link slabs using cycle-driven analysis

Link slabs are designed and expected to be serviced under high-cycle traffic loadings. Although their high-cycle fatigue performance is critical in their practical applications, very few experimental and numerical studies have been conducted to study high-cycle fatigue failure of link slabs due to the huge amount of time and resources needed for fatigue tests and the lack of efficient modelling method for high-cycle fatigue failure. Recently, as a newly developed material with superior mechanical properties, the hybrid fibre reinforced engineered cementitious composite (hybrid ECC) has been recommended for link slab application. In this study, a novel and efficient cycle-driven analysis (CDA) procedure was developed to model the structural behaviour of ECC link slabs made of the hybrid ECC under high-cycle fatigue loadings, which is the first attempt for CDA to be applied in high-cycle fatigue structural modelling of a hybrid ECC link slab with reinforcement bars. In the proposed CDA, fatigue damages of both hybrid ECC and reinforcement bars were taken into account. The accuracy and reliability of the new CDA procedure were validated by comparing the modelling predictions with the experimental results on two quarter-scaled hybrid ECC link slabs. It was found that the proposed CDA procedure could predict the overall fatigue behaviours including the deflection histories, crack patterns, fatigue life, fatigue failure mode and the residual static strength of the hybrid ECC link slabs after fatigue damage with good accuracy.

Adaptive Ensemble of Multi-Kernel Gaussian Process Regressions Based on Heuristic Model Screening for Nonparametric Modeling of Ship Maneuvering Motion

Abstract Gaussian process regression (GPR) is a commonly used approach for establishing the nonparametric models of ship maneuvering motion, and its performance depends on the selection of the kernel function. However, no single kernel function can be universally applied to all nonparametric models of ship maneuvering motion, which may compromise the robustness of GPR. To address this issue, an adaptive ensemble of multi-kernel GPRs based on heuristic model screening (AEGPR-HMS) is proposed in this paper. In the proposed method, four kernel functions are involved in constructing the ensemble model. The HMS method is introduced to determine the weights of individual-based GPR models, which can be adaptively assigned according to the baseline GPR model. To determine the hyper-parameters of these kernel functions, the genetic algorithm is also employed to compute the optimal values. The KVLCC2 tanker provided by the SIMMAN 2008 workshop is used to validate the performance of the proposed method. The results demonstrate that the AEGPR-HMS is an efficient and robust method for nonparametric modeling of ship maneuvering motion.

Research on the efficient EM modeling method from multilayered anisotropic medium-metal composite targets

The paper presents a high-frequency modeling approach tailored for the electromagnetic (EM) scattering characteristics from electrically large radar targets coated with multi-layered anisotropic mediums (MLAMs). The approach begins by deriving the plane-wave spectrum expression for the incident EM field within MLAMs. It then employs spectral domain full-wave analysis method (SDFWAM) to obtain an analytical representation of the scattering field, further leveraging saddle point evaluation (SPE) to derive asymptotic solutions in the spatial domain. By integrating principles of physical optics (PO) and the tangent plane approximation, the far-field scattering characteristics of target enveloped in the specified medium are efficiently delineated. Validations against standard structure and the Misty satellite model reveal the method's pronounced alignment with the method of moments – finite element method (MoM-FEM) hybrid numerical algorithm, underscoring its notable computational efficiency. Furthermore, in conjunction with scattering sources decomposition technique, the approach is applied to optimize the radar cross-section (RCS) of the Su-57 aircraft, achieving precise and intelligent RCS control at minimal material cost. In conclusion, this research offers pivotal technological and theoretical foundations for EM scattering prediction, stealth design, and performance assessment in radar target domains.

Efficient modelling of surface roughness effects in additively manufactured materials

3D printing defects are often used to explain the mismatch between numerical predictions and experimental results of additively manufactured acoustic materials. Although they are often perceived as desirable, as in most cases, they improve the absorptive behaviour of the system, occurring gains are unplanned and uncontrollable. Among different 3D printing inaccuracies, surface roughness provides additional acoustic losses, mostly due to the enlargement of the boundary layer thickness. On the other hand, roughness is responsible for the shift of the operating frequency compared to the numerical predictions. The goal of this research is to provide efficient modelling methods to capture the effects of surface roughness arising from the additive manufacturing process. First, a direct modelling method will be discussed, which can be computationally expensive as it relies on an accurate geometrical representation of surface roughness. Second, an alternative indirect modelling approach will be presented, which mimics the presence of roughness by adjusting specific air parameters in the locations of interest of the smooth geometry to reduce the computational cost. The two strategies will be compared with each other and to the results of an experiment where additional roughness is added to target locations within the design.

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters.

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.