Variable Constraints Research Articles

Topology optimization for rigid and compliant hybrid mechanisms

This paper proposes a topology optimization method integrating the variable trajectory constraints to design rigid and compliant hybrid mechanisms. The variable trajectory constraints are distributed in the design domain and are uniformly modeled by nonlinear spring model. Each of the variable trajectory constraints has an active or inactive state and different states map various mechanical properties of the nonlinear springs. The nonlinear relation between force and displacement of the spring is formulated. A new group of design variables denoting the states of the springs is introduced into the traditional density-based topology optimization model. The design variables representing the element density of the continuum structure and the states of the variable trajectory constraints are uniformly penalized to avoid intermediate values in order to obtain a physically realizable mechanism. Sensitivity analysis is performed by the adjoint equation method. To demonstrate the versatility and the generality of the proposed method, the typical numerical examples including linear, spline and circular trajectory constraints were implemented. The numerical comparisons between the nonlinear spring model and the actual trajectory constraints verify the accuracy. The proposed method expands the application of topology optimization and can be extensively employed to design the mechanisms integrating both the compliant members and rigid parts.

Construction and forward kinematics of a novel reconfigurable parallel mechanism

In this paper, a kind of hybrid kinematics chain with variable constraints is constructed by using a 6R spatial bifurcated chain and 4-DOF serial limbs, by using them, a 3-DOF Reconfigurable Parallel Mechanisms (RPM) was constructed, which has four motion modes: 3T, 2T1R, 2R1T, and 3R motion modes. Using screw theory, the change of wrench system in the hybrid kinematics chain caused by the reconfiguration of the 6R bifurcated chain is analyzed, which affects the wrench system of the moving platform (MP), and thus causes the reconfiguration of the parallel mechanism (PM). The triangular pyramid fixed to the MP was found as auxiliary geometry and used to intuitively describe the constraint relationship between joint axes in limbs. The latter was used to establish a unified forward kinematics model of the RPM in four motion modes. The general forward kinematics solution procedure for this type of RPM is given. The overall Jacobian matrix was obtained by screw theory for singularity analysis. The condition number of the Jacobian matrix measures the degree to which the mechanism is close to the singular configuration. Based on the Monte Carlo simulation, point cloud images representing the workspace of RPM were obtained.

Research on Energy Distribution Strategy of Tandem Hybrid Tractor Based on the Pontryagin Minimum Principle

In order to study the energy distribution of the tandem hybrid tractor and achieve optimal fuel economy under the whole operating condition, an energy distribution strategy based on PMP (Pontryagin minimum principle) is proposed. The performance parameters of the relevant power components are obtained by constructing the test bench of the tandem hybrid tractor. At the same time, the mathematical model of energy distribution is established from the aspects of energy distribution objective function, state variable, and variable constraint. Based on the external characteristic test of the hub motor and the resistance analysis, the transport operation condition of the tractor is selected as the simulation target condition. The ADVISOR2002 and Simulink software are used to jointly simulate the three energy distribution strategies: the thermostat type, the power-following type, and the PMP type. The simulation results show that, compared with the other two, the fuel economy and battery power loss of the PMP-based energy distribution strategy are significantly improved. The fuel consumption per 100 km is decreased by 32.91% and 26.10%, respectively, which verifies the feasibility of the strategy. This study is of great significance for improving the production efficiency and reducing fuel consumption of hybrid tractors.

Optimization design of brushless DC motor based on improved JAYA algorithm

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5–12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm2, 0.63 T, and 0.91 T, respectively. The research overcomes the problem of parameter optimization in the optimization design of brushless DC motor, improving their economic value of brushless DC motor in industrial production and application.

Noise-Resistant Adaptive Gain Recurrent Neural Network for Visual Tracking of Redundant Flexible Endoscope Robot With Time-Varying State Variable Constraints

Noise-Resistant Adaptive Gain Recurrent Neural Network for Visual Tracking of Redundant Flexible Endoscope Robot With Time-Varying State Variable Constraints

An efficient framework for controllable micromixer design through the fusion of data-driven modeling and machine learning insights: Numerical and experimental analysis

Micromixers are inevitable components in microfluidics, micro-electro-mechanical devices, and numerous bio-chemical assays. By assays, we mean diverse analytical procedures encompassing gene engineering, the manipulation of genetic material; nanoparticle synthesis, focusing on the controlled creation of nanoparticles; and cell lysis, involving cell membranes disruption for the release of intracellular substances for diagnostic purposes. In these assays, the homogeneous mixture of two or more fluids is crucial. However, designing an efficient micromixer providing high homogeneity and low pressure drop, while maintaining controllability, is challenging. Controllability refers to the design of a micro-system tailored to meet the specific requirements of a given assay. This study proposes a controllable framework, combining machine learning and statistical modeling. The framework begins with the generation of a reference parametric micro-structure, herein a microchannel with L-shaped baffles and featuring seven variables. A response surface method, a data-driven modeling scheme, is used to establish functional relationships between design variables and objective functions. The study reveals that the baffle height significantly impacts the system functionality, increasing the mixing index by over 40% and the pressure drop by more than 220% when reaching its upper limit. Dean-like secondary vortexes are generated in the microchannel at Re = 10, demonstrating the efficiency of the implemented baffles. Subsequently, multi-objective optimization methods, non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE), are employed, with adaptable variable constraints. Comparative analysis of the methods shows that DE finds superior optimum solutions in fewer iterations. Finally, an optimum structure is fabricated using soft lithography, and experimental tests are conducted for validation.

Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

The aim of this work, which is an extension of previous research, is a comparative analysis of the results of the dynamic optimization of safe multi-object control, with different representations of the constraints of process state variables. These constraints are generated with an artificial neural network and take movable shapes in the form of a parabola, ellipse, hexagon, and circle. The developed algorithm allows one to determine a safe and optimal trajectory of an object when passing other multi-objects. The obtained results of the simulation tests of the algorithm allow for the selection of the best representation of the motion of passing objects in the form of neural constraints. Moreover, the obtained characteristics of the sensitivity of the object’s trajectory to the inaccuracy of the input data make it possible to select the best representation of the motion of other objects in the form of an excessive approximation area as neural constraints of the control process.

Multi-objective structure optimization and performance analysis of catalytic micro-reactor channel designed by an improved topology optimization model

Previous micro-reactor channel studies mainly focus on the limited design space or structure layout, rarely exploits design forms in which the procedural topology structure evolves freely. Therefore, a multi-objective topology optimization model are proposed for the inverse free evolution of micro-reactor channels, which can synergistically balance the effects among three objectives of reaction rate, flow loss and heat transfer to achieve optimal comprehensive performance. The topology optimization problem is formulated based on the material density-based design variable. The fluid dynamics response of design variables at each optimization iteration is updated by solving multi-physics governing equations, objective functions and variable constraints. Adjoint-based sensitivity analysis and GCMMA algorithm are used to obtain the gradient information of objective functions and update design variables, respectively. Additionally, the strict volume constraints and proposed pseudo-design-domain concept are introduced into the multi-objective function. In numerical examples, the influence of different Re conditions and different multi-objective weight-ratios on optimal designs is investigated, and their regular evolution mechanisms and the trade-off between performance optimization and structure evolution are analyzed. Then, the optimization principles for topology structure and performance parameters under the control of multi-objective functions are revealed. Finally, the comprehensive performance of micro-reactors under topology optimization method is compared with that of traditional micro-reactors to demonstrate the effectiveness of the improved method and reveal the physical mechanisms of multi-objective performance enhancement. The comparison results show that topology optimization designs can achieve 86.1% growth in reaction rate and 47.1% reduction in flow loss, and comprehensive performance coefficients have also been significantly optimized.

Responses of phytoplankton and periphyton community structure to an anthropic eutrophication gradient in tropical high-altitude Lake Titicaca

Eutrophication is a global environmental problem in aquatic ecosystems, mainly caused by increased nutrient loads (nitrogen and phosphorus). Phytoplankton and periphyton responses to the nutrients increase and temporal variation may be related to mutual seeding and/or variable environmental constraints. Each of these communities may be useful for characterizing and monitoring eutrophication processes. However, little information exists about the simultaneous responses or interactions between such communities during eutrophication, even less in high-altitude tropical lakes such as Lake Titicaca. Here, we first established a eutrophication gradient with stable isotopes (δ15N and δ13C) and physicochemical variables in a shallow bay of Lake Titicaca. Later, we analyzed the shifts in taxonomic and morphological forms of phytoplankton and periphyton colonizing the underwater stems of totora (Schoenoplectus californicus ssp. tatora), an emergent aquatic macrophyte, along the eutrophication gradient. There is a clear turnover (groups, genera, and morphological forms) in phytoplankton and periphyton and decreased biodiversity along the eutrophication gradient. Gomphonema genus relative abundance increase with eutrophication in both communities, while Achnanthidium abundance decreases. However, other genera behave differently in each community, allowing for the identification of specific bioindicators of eutrophication for phytoplankton (Oscillatoria, Spirogyra, and Euglena) and periphyton (Oedogonium, Stigeoclonium, and Characium). They share genus composition with some taxa showing similar behaviors, thus interactions between phytoplankton and periphyton may exist. We also believe that each compartment can act as a seeding reservoir for the other, though remaining independent to some extent.

A constraint region of attraction-based large-signal stability analysis method for direct current microgrids

Large-signal stability of direct current (DC) microgrids (MGs) are required to be seriously considered when more power converters are penetrated. For DC MGs suffering from large disturbances, the power electronics at both source and load sides are operated within the voltage and current limits to ensure the stability and safety. However, no existing methods have been developed to address the non-conservative stability analysis of a DC MGs with physical variable constraints. To this end, a comprehensive is developed in this paper to analyze the large-signal stability of typical DC MGs with voltage/current and pulse-width modulation duty cycle constraints, with a low level of conservatism. First, the system-level model of a DC MG is established as the basis of stability analysis. Then, a workflow based on the Lyapunov theory and numerical integration is developed to evaluate the large signal stability with physical variable constraints. The developed stability analysis method has less conservatism than the existing methods with only one moderate computational cost. The effectiveness of the proposed strategy is validated by case studies based on a single-bus direct current microgrid.

Designing a Model Predictive Controller for Displacement Control of Axial Piston Pump

Variable displacement hydraulic pumps are widely used for energy efficiency, and they often have a mechanical feedback mechanism to ensure target tracking control performance and stability of tilt angle control. Furthermore, there are many examples which add electronic control to realize higher tracking control performance. However, in such cases, the control performance is significantly affected by the dynamic characteristics of the mechanical feedback mechanism, and this problem prevent its widespread use. Additionally, tilt angle control is susceptible to changes in dynamic characteristics and load pressure depending on the operating point, and there are constraints on the tilt angle. Hence, high control performance cannot be obtained without considering these nonlinearities. In this study, the variable displacement pump without mechanical feedback mechanism is focused on, and the objective of this study is to design a displacement control system for a hydraulic pump based on a model predictive control (MPC) that can consider various constraints on the design step. An adaptive system, which handles changes in dynamic characteristics and the effects of load pressure, is introduced. Additionally, the control performance of adaptive MPC is compared to adaptive model matching-based MPC with inverse optimization that can optimally design the weight matrices of the evaluation function without trial and error. Furthermore, in order to improve the transient response, a variable control input constraints are added in these two control systems. Experimental results of control performance have shown that the proposed method achieved a high tracking performance and short settling time, which confirmed the effectiveness of the variable control input constraints.

A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem

A novel hybrid algorithm of genetic algorithm, variable neighborhood search and constraint programming for distributed flexible job shop scheduling problem

Design methodology based on multi-objective optimization with evolutionary algorithms for humidification-dehumidification desalination systems using updated approaches

This paper develops a methodology for multi-objective optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The methodology aims to address the problem of the close relation between variables based on a mathematical model that simulates design variants of humidification-dehumidification (HDH) desalination systems, where the variation of one parameter can affect another. The approach consists of an exhaustive study of the state of the art to incorporate as many results as possible, including variable and parameter constraints established by previous research. Energy efficiency and potable water production are optimized with respect to the total area of the system's active components and the ratio of seawater converted to potable water. The calculations generate 20 Pareto optimal solutions, with an average pure water production of 14.01 l/h, and an average of the total and solar heating areas of 22.43 m2 and 8.52 m2, respectively, including many other parameters with similar values compared to other publications. The methodology is flexible, updatable, and generates high efficiency values, confirmed by the comparison of equivalent parameters and the pinch analysis, also with respect to previous results. The sensitivity analysis performed, presents different trends in the relation between variables in this scenario contributing to feedback on the behavior of hyperparameters of the algorithm, indicating a very wide potential of solutions. For the water temperature entering the dehumidifier, the range between 30 and 37 °C is determined as the optimum range, however, this is not the only result from the trend analysis of these 20 solutions, compared with other references. This methodology is a contribution to the overall objective of improving the HDH technology design process; optimal solutions are obtained for systems of the configuration chosen to apply it, but it can be adapted to others.

Unveiling the connection among customer concentration, trade credit financing, and firm performance: A moderated mediation model

The augmentation of firm performance via customer concentration is particularly indispensable for organizational evolution. Both trade credit financing and financing constraints play pivotal roles in the nexus between customer concentration and performance. This research constructs a moderated mediation model to rigorously investigate the impact of customer concentration on firm performance, positing trade credit financing as the mediating variable and financing constraints as the moderating variable. The relevant hypotheses are evaluated empirically using panel data compiled from listed manufacturing firms in China over the period 2013–2020, yielding 8 firm-year observations. The empirical outcomes denote that customer concentration exerts a positive influence on firm performance, albeit having a negative impact on trade credit financing. Trade credit financing serves as a partial mediator in the relationship between customer concentration and manufacturing firm performance. Financing constraints are found to positively moderate the mediating role of trade credit financing in the relationship between customer concentration and firm performance. This research broadens the understanding of the implications of customer relationships on trade credit financing and performance, thereby enriching the knowledge base for managing a firm’s financing channels more effectively.

Theoretical model of energy absorption for square tubes under transverse loading with variable axial constraint condition

Thin-walled tubes in the actual engineering structure (e.g., autobody) are usually connected to other structure, and the boundary constraint condition at both ends of tubes are usually very complex when bending deformation occurs under transverse loading. This paper aims at the effect and mechanism of axial constraint on the bending energy absorption effect of thin-walled tube under limited and variable axial constraint condition at both ends. A typical of thin-walled tube, square tube, is taken as an example. Firstly, we compare the experiment results of square tube under transverse loading with three-point-bending condition and fully clamped condition, revealing that the axial constraint force has a significant effect on the energy absorption effect. After that, a FE model of the square tube under variable axial constraint condition at both ends is established and validated based on experiment. Based on the validated FE model, the effect of different sizes of axial constraint stiffness on the bending energy absorption of square tube are investigated, and it is found that it has a significant effect on the deformation mode and bending energy absorption. With the increase of axial constraint stiffness, it will mainly affect the latter segment of the force–displacement curve in the bending process, which increases the bending energy absorption. Finally, theoretical models are developed to predict the force–displacement curves and bending energy absorption of the square tube under three different types of variable axial constraint properties: linear axial constraint, unequal linear axial constraint and nonlinear axial constraint.

Multi-stage 3D Gravity Inversion Scheme for Maximum Optimization of the Subsurface Basement Model at Gebel El-Zeit Basin, Southwestern Gulf-of-Suez, Egypt

Relevance and purpose of the work. Due to its basement fault block pattern in the sedimentary basin, the Southwestern Gulf of Suez’s Gebel El-Zeit basin is one of Egypt’s most desirable hydrocarbon concessions. However, salt diapers in sedimentary layers have hindered seismic interpretations in this area, making it challenging to build a 3D central primary basinal structure. This study uses Bouguer gravity anomalies to input basement complex lateral density model assumptions to determine the optimal three-dimensional basement depth for the study area. Research methodology. Based on the concept of sequential 3D spectral layered-earth inversion approaches, through trials with the Oldenburg and other forward models, many forward optimization strategies and parameterization sequences with variable constraint parameter assumptions were used to regulate the inversion operations within a proposed three-stage gravity inversion scheme to identify the optimal depth-density solution with a minimal computational data misfit. This study statistically analyzes the basement’s relief and complicated lateral density distribution to determine the best parameters for a 3D depth-density model solution. Zero regional gravity offset and DC-shift, which forced the mean error to be zero, helped simulate the lateral density model’s best-possible constraining assumptions. Results and conclusions. Correlating depth data from many stratigraphical-control wells drilled in the inverted 3D basement model confirmed the basement relief optimality of the study area. Correlation analysis showed a good match between the predicted and measured depths, proving the resulting optimality of the basement complex’s lateral density distribution, minimizing the computational depth error to a minimal percentage.

Formation‐Aware Planning and Navigation with Corridor Shortest Path Maps

AbstractThe need to plan motions for agents with variable shape constraints such as under different formations appears in several virtual and real‐world applications of autonomous agents. In this work, we focus on planning and execution of formation‐aware paths for a group of agents traversing a cluttered environment. The proposed planning framework addresses the trade‐off between being able to enforce a preferable formation when traversing the corridors of the environment, versus accepting to switch to alternative formations requiring less clearance in order to utilize narrower corridors that can lead to a shorter overall path to the final destination. At the planning stage, this trade‐off is addressed with a multi‐layer graph annotated with per‐layer navigation costs and formation transition costs, where each layer represents one formation together with its specific clearance requirement. At the navigation stage, we introduce Corridor Shortest Path Maps (CSPMs), which produce a vector field for guiding agents along the solution corridor, ensuring unobstructed in‐formation navigation in cluttered environments, as well as group motion along lengthwise‐optimal paths in the solution corridor. We also present examples of how our multi‐layer planning framework can be applied to other types of multi‐modal planning problems.

Modelling spatio-temporal energy consumption from nighttime radiance satellite dataset

National electricity consumption increases in line with continuous population growth and other socio-economic factors. The national electric power capacity goal develops largely for industrial manufacture and new settlement. The electrification- ratio on the target; is based on the accessibility of electricity services. The spatial distribution of electricity services coverage over the Indonesian territory is insufficient, particularly over the remote area that is out of electric services. Modeling by spatial (location) and temporal (year) to estimate electricity or energy consumption is necessary to develop using a low-light nighttime satellite dataset, therefore spatial boundaries can be accomplished. The modeling procedure starts by preparing the data frame of the independent variable input (amount of radiance) and the dependent variable output (the consumption of electricity or energy). The modelling method uses the curve-fitting approach where the indicator results by evaluating the R-square and RMSE values. The output model function is used to convert radiances into electrical power consumption units with a certain degree of accuracy. The selection of the input-output variable was achieved after variable analysis with the highest R-square outcome. Results indicate that the model functions in a polynomial form and correlations between variables are not simple. The selection of various model functions did not change the degree of correlation. The accumulative of energy radiances as independent variable input provides the optimum correlation result. The energy consumption from street lighting, in general, offers appropriate information that can be seen from satellites. The model function can be applied to a narrower spatial scale by input variable constraints.

Underactuated Humanoid Peeling Approach for Pickled Mustard Tuber Based on Metamorphic Constraints.

Pickled mustard tuber (PMT), also known as Brassica juncea var. tumida, is a conical tuberous vegetable with a scaly upper part and a coarse fiber skin covering the lower part. Due to its highly distorted and complex heterogeneous fiber network structure, traditional manual labor is still used for peeling and removing fibers from pickled mustard tuber, as there is currently no effective, fully automated method or equipment available. In this study, we designed an underactuated humanoid pickled mustard tuber peeling robot based on variable configuration constraints that emulate the human "insert-clamp-tear" process via probabilistic statistical design. Based on actual pickled mustard tuber morphological cluster analysis and statistical features, we constructed three different types of pickled mustard tuber peeling tool spectral profiles and analyzed the modular mechanical properties of three different tool configurations to optimize the variable configuration constraint effect and improve the robot's end effector trajectory. Finally, an ADAMS virtual prototype model of the pickled mustard tuber peeling robot was established, and simulation analysis of the "insert-clamp-tear" process was performed based on the three pickled mustard tuber statistical classification selection. The results showed that the pickled mustard tuber peeling robot had a meat loss rate of no more than 15% for each corresponding category of pickled mustard tuber, a theoretical peeling rate of up to 15 pieces per minute, and an average residual rate of only about 2% for old fibers. Based on reasonable meat loss, the efficiency of peeling was greatly improved, which laid the theoretical foundation for fully automated pickled mustard tuber peeling.

Artificial CT images can enhance variation of case images in diagnostic radiology skills training

ObjectivesWe sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.MethodsArtificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated.ResultsPrimary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones.ConclusionArtificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases.Critical relevance statementArtificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images.Key points• Artificial images, similar to original ones, can be created using generative networks.• Pathological features of artificial images can be adjusted through guiding the network.• Artificial images proved viable to augment the depth and broadening of diagnostic training.Graphical