Geometric Design Parameters Research Articles

Lagrangian approach to origami vertex analysis: kinematics.

The use of origami in engineering has significantly expanded in recent years, spanning deployable structures across scales, folding robotics and mechanical metamaterials. However, finding foldable paths can be a formidable task as the kinematics are determined by a nonlinear system of equations, often with several degrees of freedom. In this article, we leverage a Lagrangian approach to derive reduced-order compatibility conditions for rigid-facet origami vertices with reflection and rotational symmetries. Then, using the reduced-order conditions, we derive exact, multi-degree of freedom solutions for degree 6 and degree 8 vertices with prescribed symmetries. The exact kinematic solutions allow us to efficiently investigate the topology of allowable kinematics, including the consideration of a self-contact constraint, and then visually interpret the role of geometric design parameters on these admissible fold paths by monitoring the change in the kinematic topology. We then introduce a procedure to construct lower-symmetry kinematic solutions by breaking symmetry of higher-order kinematic solutions in a systematic way that preserves compatibility. The multi-degree of freedom solutions discovered here should assist with building intuition of the kinematic feasibility of higher-degree origami vertices and also facilitate the development of new algorithmic procedures for origami-engineering design.This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Evaluation of compression behaviour of 316L SS Gyroid and Diamond structures using SLM process – Experimental programme under static and dynamic compression loadings

The relative comparison in terms of energy absorption efficiency for a set of 4 structures made of various Triply Periodic Minimal Surfaces (TPMS) topologies is experimentally investigated. These TPMS structures are printed by Selective Laser Melting AM process using 316L SS. The study is carried out in consideration of the effect of parameters such as relative density, compressive loading directions and loading rates, number of unit cells for Diamond and Gyroids TPMS both declined for Sheet and Skeletal topologies. The objective is to quantify their structural responses in terms of apparent stress and strain, dynamic enhancement and Specific Energy Absorbed (SEA) and to evaluate their structural integrity in terms of collapse stability. The results reveal that the Sheet pattern of TPMS structures with its constant wall thickness and uniform geometry exhibits better energy absorption capabilities than the Skeletal pattern. The Diamond family shows greater interest rather than the Gyroid family only in the case of the Sheet pattern. The increase in relative density from 20 to 30% is characterized by improved manufacturing quality, an increase in energy absorption capacity and more homogeneous progressive deformations during compression. On the whole, the set of TPMS geometries exhibits energy absorption capacities prior to those of other conventional cellular materials currently used for impact engineering applications. Finally, in a first approach, an original design methodology using charts can be developed to establish a link between the energy absorption capabilities and the design geometric parameters of TPMS structures.

Data-driven approach for design and optimization of rotor–stator mixers for miscible fluids with different viscosities

Rotor–stator mixers (RSMs) are essential equipment used in the food, pharmaceutical, and cosmetic industries. The performance of RSMs is conventionally analyzed using three approaches based on empirical correlations and experimental and numerical analyses. This paper introduces a new approach based on a multifidelity data-driven framework for designing and optimizing RSMs that systematically combines elements of experimental and high-resolution computational fluid dynamics (CFD) models to train a machine learning model to predict RSM performance. It was subsequently coupled to an optimization solver based on a genetic algorithm. The developed framework was applied to a small-scale RSM with variable rotor blade radius, rotor speed, stator mesh width, and mass flow rate that is operated to mix two miscible fluids with different viscosities. The RSM was optimized at two regimes of low and high viscosities to achieve the best performance in terms of shaft power and mixing indices. The results demonstrate the successful application of the developed frameworks. The optimization of the mixing of high-viscosity fluids yielded a higher improvement than that for low-viscosity fluids. A decrease in shaft power of approximately 50% was obtained for the high-viscosity fluids, whereas the mixing indices increased up to 20% by the optimization of the geometrical input design parameters. The proposed framework can be advantageous for designers and operators at the industrial scale.

Assessment of Seismic Responses on the Concrete Gravity Dam Subjected to the Geometrical Design Parameters Using Finite Element Approach

Assessment of Seismic Responses on the Concrete Gravity Dam Subjected to the Geometrical Design Parameters Using Finite Element Approach

Thermal and energy efficiency in 3D-printed buildings: Review of geometric design, materials and printing processes

Applying 3D printing in construction is a promising, sustainable alternative to conventional methods. However, the thermal and energy performance of 3D-printed buildings remains underexplored, particularly regarding the interactions between geometric design, material selection, and printing parameters. This review addresses this gap by critically examining how the main steps of the printing process impact the thermal efficiency of 3D-printed buildings. Key findings indicate that material development needs to focus on mixes that balance thermal properties,printability,and structural integrity. The review demonstrates how optimising wall cross-sections and cavity shapes can reduce thermal conductivity and enhance energy efficiency. Innovative geometric designs, such as cellular and honeycomb structures, have shown improvements in thermal insulation. Optimising printing parameters, such as extrusion rate and nozzle travel speed, is crucial to minimising thermal bridges and reducing material anisotropy. Advanced numerical models, validated by experimental data and incorporating factors like inhomogeneous porosity, anisotropy, and surface roughness, are essential for accurately predicting the thermal behaviour of 3D-printed structures. The review underscores the need for large-scale, long-term performance studies of 3D-printed buildings under diverse climatic conditions. Additionally, developing standardised fabrication protocols and testing methods is essential for ensuring consistent quality and broader adoption. Addressing these research gaps is crucial to fully realising the potential of 3D printing in sustainable construction and accelerating the adoption of additive manufacturing in the construction sector.

Shape-retaining beam-like morphing structures via localized snap through

In this study, we present a concept of morphing structure – featuring an arch mounted on a compliant base – that can be reconfigured via snap-through buckling and leverages bistability to retain its morphed shape. We show that one-dimensional arrays of such units yield beam-like structures that, upon localized snapping, can attain multiple, morphologically distinct stable shapes. Units are modeled using discrete elastic rods, a reduced-order formulation for beams and beam structures, and the results are validated via experiments. We leverage our model to understand the influence of the geometrical design parameters on the response and final shape of a unit. We then show that the morphed shapes of arrays of units can be predicted by concatenating results of simulations on single units, and leverage this idea to inverse-design structures that can be snapped into target stable shapes. Ultimately, our work suggests an up-scalable way to create shape-retaining morphing structures with target stable shapes.

Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks

High Mach number inlets face complex challenges such as shock wave/boundary layer interference, adversely impacting the aerodynamic characteristics and stable operating boundaries. Traditional computational fluid dynamics (CFD) simulations for performance and flow field analysis are slow, and data-driven deep learning technologies struggle with predicting flow fields in complex aerodynamic phenomena like shock wave/boundary layer interactions, boundary layer separation, and Mach disks. This study introduces a rapid, robust method for reconstructing hypersonic viscous inlet flow fields using inlet geometry design parameters. This method is called shock wave and boundary layer dominated two-dimensional multiphysical field reconstruction model based on multi-scale sensory field fusion residual physical information neural network (PIMSRF_ResNet), ensuring high-precision training data. Using the optimal Pareto solution set from multi-objective optimization with a small sample intelligence method, 150 sets of multi-physics fields under Ma10 conditions with varying geometric design parameters are calculated. Compared to traditional pure data-driven models, PIMSRF_ResNet's structural similarity in the test set reaches 0.9773, with a peak signal-to-noise ratio close to 30 dB and a correlation coefficient exceeding 98%. These experimental results demonstrated that the proposed method is a promising tool for real-time prediction of complex internal flow fields dominated by high Mach number shock waves and boundary layers.

Reverse engineering of geometric design parameters using shape information of non‐standard spur gear

AbstractIn this study, we conducted research on a method to reverse engineer geometric design parameters from the shape information of gears. The module and normal pressure angle were tracked using the bisection method, and the profile shift coefficient was extracted from the relationship between the working pitch circle diameter, center distance modification coefficient, and working pressure angle. It was confirmed that there are limitations to reverse engineering the shape using only a coordinate measuring machine or 3D scanner because various geometric information is additionally required to trace back the design requirements for gear strength. Finally, the gear tooth bending strength was calculated using finite element analysis and theoretical formula, and through this, the required level of gear strength design was inferred, and the verification of the gear reverse engineering method presented in this study was attempted.

Marine shaft optimization using surrogate models and multi-objective optimization

This paper aims to obtain an application software using Ansys software in which a static, dynamic, and modal analysis for the power transmission of ships is presented. The objective functions of this research are maximum static stress, maximum dynamic stress, shaft mass, and modal shaft frequency analysis. A complete, comprehensive, and generalizable model for metal shaft types is presented. Then, using the kriging response surface and multi-objective genetic algorithm (NSGA II), these goals are optimized simultaneously. In this research, the optimization parameters are geometric parameters of the shaft design, including the position of the supports and the inner and outer diameters of the shaft. As a result of this optimization, the shaft's maximum static and dynamic stresses are reduced by 36% and 42%, respectively. The shaft's mass, which is one of the most critical factors during the shaft and its vibrations, is reduced by about 9%. In this regard, the first natural frequency of the optimized shaft increased by about 28.5% compared to the initial shaft.

Повышение эффективности процессов дробления с использованием новой конструкции валковой дробилки

In order to increase the efficiency of crushing and grinding processes, technological and technical-economic characteristics of roller crushers, numerous scientific research works are carried out to substantiate the modernization of the main workers organs of crushing machines, innovative materials and methods of hardening the surfaces of the main working organs are used. The article provides a rationale for the operational characteristics of a new roller crusher with cycloidal movements and determines its design geometric parameters. The complex cycloidal movement of the rolls ensures the lowest possible energy intensity of the crushing process. The working body of the crusher uses the properties of mutually bending cycloidal curves and bodies of constant width, which made it possible to significantly reduce the weight and dimensions of the machine. The planetary-rotor motion of cycloidal-shaped rolls and their design features make it possible to reduce the minimum gap between the rolls, and the width of the unloading gap remains constant, which ensures the maximum size of the finished crushing product and the yield of cube-shaped crushed stone

Performance prediction and Bayesian optimization of screw compressors using Gaussian Process Regression

Optimizing the performance of screw compressors is critical for achieving high efficiency and reducing costs in various industrial and engineering applications. Often, the design and optimization processes are time-consuming owing to the underlying iterative complex analyses. In this context, the present research investigates the potential of Gaussian Process Regression (GPR) and Bayesian optimization for the prediction and optimization of the performance of an oil-flooded screw compressor. Specifically, the GPR-based surrogate model is developed to predict the compressor performance characteristics based on its four main geometrical design parameters such as wrap angle, relative length, tip speed of the male rotor and built-in volume ratio. The model is trained using a dataset comprising 19,200 data points relating the input design parameters with the compressor performance, obtained using physics-based multi-chamber thermodynamic models. While four different learning algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Polynomial regression and GPR are explored, the GPR performed the best resulting in an R2 value of 0.99 for the test dataset after hyperparameter tuning. Further, the model is also experimentally validated on a completely unseen dataset, showing very good predictions with a maximum error of 5%. The resulting surrogate model is then used to optimize the compressor design parameters using Bayesian optimization. The results are compared with optimization using Genetic Algorithm (GA) and physics-based multi-chamber thermodynamic model. It was shown the proposed approach results in similar optimal design parameters but with a significantly less optimization time by a factor of 7. The study highlight the potential of machine learning-based prediction and optimization of screw compressors in engineering applications.

Hydrodynamic performance of a land-based multi-chamber OWC wave energy capture system: An experimental study

Improving ocean wave energy capture in a cost-effective manner is a challenging task. Multi-chamber oscillating water column (OWC) devices are gaining favor due to their potentially efficient characteristics. This research experimentally investigated the hydrodynamic performance of a land-based OWC wave energy capture system with multiple chambers (ranging from 1 to 5). The free surface elevation, air pressure fluctuations, hydrodynamic efficiency, and reflection coefficient are considered. The hydrodynamic efficiencies of the overall, and sub-chambers in the OWC system with varying numbers of chambers are graphically presented. The influence of geometrical design parameters and wave conditions are also considered in evaluating the performance of the multi-chamber OWC system. It is found that the multi-chamber arrangement improves the hydrodynamic energy extraction characteristics compared to the traditional single-chamber setup. The sloshing mode of the chamber water column is utilized to effectively capture wave energy. As the number of chambers increases, the wave attenuation capability of the system improves for long waves. Yet, the overall efficiency declines when the number of OWC chambers exceeds 3. The chamber draft has the most substantial impact on the wave energy capturing capability, while the effects of opening ratio and wave height are attenuated due to multiple water-column interactions inside the chambers. The isometric sub-chamber structure demonstrates overall efficiency and wave attenuation advantages for short waves. The paper aims to guide the design and optimization of a multi-chamber OWC system.

A magnesium screw with optimized geometry exhibits improved corrosion resistance and favors bone fracture healing

Stress-induced corrosion impairs the mechanical integrity of magnesium (Mg) and its alloys as potential orthopedic implants. Although there has been extensive work reporting the effects of stress on Mg corrosion in vitro, the geometric design principles of the Mg-based orthopedic devices still remain largely unknown. In this work, a numerical simulation model mimicking fractured bone fixation and surgical animal models were applied to investigate the effects of the geometric design of Mg screws on the stress distribution and the stress-induced degradation behavior. Finite element (FE) analysis was used for calculation of stress concentrations around the Mg screws, with different thread type, thread pitch, and thread width. Afterward, the Mg screws of the pre-optimization and post-optimization groups exhibiting the highest and lowest stress concentrations, respectively, were implanted in the fractured distal femora and back subcutaneous tissue of rabbits. Encouragingly, there was a significant difference between the pre-optimization and the post-optimization groups in the degradation rate of the stressed screw parts located around the fracture line. Interestingly, there was no significant difference between the two groups in the degradation rate of the non-stressed screw parts. Consistently, the Mg screw post-optimization exhibited a significantly lower degradation rate than that pre-optimization in the back subcutaneous implantation model, which generated stress in the whole screw body. The alteration in geometric design did not affect the corrosion rate of the Mg screws in an immersion test without load applied. Importantly, an accelerated new bone formation with less fibrous encapsulation around the screws was observed in the Mg group post-optimization relative to the Mg group pre-optimization and the poly (lactic acid) group. Geometry optimization may be a promising strategy to reduce stress-induced corrosion in Mg-based orthopedic devices. Statement of significanceStress concentrations influence corrosion characteristics of magnesium (Mg)-based implants. The geometric design parameters, including thread type, thread pitch, and thread width of the Mg screws, were optimized through finite element analysis to reduce stress concentrations in a fractured model. The Mg screws with triangular thread type, 2.25 mm pitch, and 0.3 mm thread width, exhibiting the lowest maximum von Mises stress, showed a significant decrease in the volume loss relative to the Mg screws pre-optimization. Compared with the Mg screw pre-optimization and the poly(lactic acid) screw, the Mg screw post-optimization favored new bone formation while inhibiting fibrous encapsulation. Collectively, optimization in the geometric design is a promising approach to reduce stress-induced corrosion in Mg-based implants.

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

Design and fabrication of an opto-mechanical antenna in the NIR range

In this study we present a novel device for the direct transduction of optical radiation in the near-infrared region into mechanical actuation, which is based on a plasmonic optical nanoantenna integrated in a microcantilever. We propose and demonstrate the feasibility of a simple fabrication process consisting in the nano-tailoring of a commercially available Atomic Force Microscope (AFM) cantilever by means of the Focused Ion Beam (FIB) milling technique. Furthermore, the comprehensive analysis of the device performance characteristics included in this work reveals the different sensitivity values of these characteristics to the fabrication process tolerances of the most relevant geometric design parameters.

Design and simulation of a nano biosensor based on amorphous indium gallium zinc oxide (a-IGZO) thin film transistor

In this study, a biosensor utilizing a dielectric-modulated amorphous indium gallium zinc oxide (a-IGZO) thin film transistor (TFT) is introduced. TFT biosensors have garnered significant attention due to their heightened sensitivity, scalable nature, low power consumption, rapid electrical detection capabilities, and cost-effective means of mass production. By embedding a nano-cavity within the gate insulator of the TFT, biomolecules can accumulate within. As each biomolecule possesses its own dielectric constant, it modulates the effective gate capacitance and, subsequently, changes the channel conductance. To assess the sensitivity of the biosensor, variation in saturation current after the absorption of biomolecules with respect to the drain current in the case of an air-filled cavity has been considered as a precise measure. The efficient operation of a biosensor is contingent upon the sensitivity being highly dependent on the dielectric constant of the biomolecules that are accumulated within the nano-cavity. Consequently, a comprehensive evaluation has been conducted to ascertain the impact of critical design parameters which have the potential to affect the sensitivity of the biosensor. Additionally, a statistical analysis based on coefficient of variation measure has been performed to evaluate the susceptibility of the biosensor’s sensitivity to variations in geometrical and physical design parameters. The utilization of label-free detection methodology in this device presents a notable advantage due to its compatibility with the fundamental CMOS processing technology and its cost-effective potential for macro production.

Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology

Wind energy, renowned for cost-effectiveness and eco-friendliness, addresses global energy needs amid fossil fuel scarcity and environmental concerns. In low-wind speed regions, optimising wind turbine performance becomes vital and achievable by augmenting wind velocity at the turbine rotor using augmentation systems such as concentrators and diffusers. This study focuses on developing a velocity augmentation model that correctly predicts the throat velocity in an empty concentrator-diffuser-augmented wind turbine (CDaugWT) design and determines optimal geometrical parameters. Utilising response surface methodology (RSM) in Design Expert 13 and computational fluid dynamics (CFD) in ANSYS Fluent, 86 runs were analysed, optimising parameters such as diffuser and concentrator angles and lengths, throat length, and flange height. The ANOVA analysis confirmed the model’s significance (p < 0.05). Notably, the interaction between the concentrator’s length and the diffuser’s length had the highest impact on the throat velocity. The model showed a strong correlation (R2 = 0.9581) and adequate precision (ratio value of 49.655). A low coefficient of variation (C.V.% = 0.1149) highlighted the model’s reliability. The findings revealed a 1.953-fold increase in inlet wind speed at the throat position. Optimal geometrical parameters for the CDaugWT included a diffuser angle of 10°, concentrator angle of 20°, concentrator length of 375 mm (0.62Rth), diffuser length of 975 mm (1.61Rth), throat length of 70 mm (0.12Rth), and flange height of 100 mm (0.17Rth) where Rth is the throat radius. A desirability value of 0.9, close to 1, showed a successful optimisation. CFD simulations and RSM reduced calculation cost and time when determining optimal geometrical parameters for the CDaugWT design.

Modeling of 3D Brick‐and‐Mortar Structures Using Cohesive Zone Finite Elements

Nacre‐inspired brick‐and‐Mortar composite structures exhibit exceptional combinations of properties as well as a highly tuneable mechanical response, due to their large range of design parameters. Understanding the effect of these parameters on the response is essential to optimally design these structures and can be guided by modeling. Traditional models only consider 2D geometries and limited attempts at modeling 3D geometric designs exist. Herein, 3D brick‐and‐mortar structures using a finite element in conjunction with an experimentally calibrated cohesive zone model to represent the layers are proposed. The model is successfully validated against experimental results for a nonplanar brick assembly using so‐called osteomorphic bricks. The capabilities of the model are further demonstrated through a parametric study, where the effect of brick shape, number of bricks, and soft layer material properties on the structure mechanical properties (elastic modulus, yield strength and toughness) are investigated. Numerical results show that toughness is significantly increased by transitioning from a “two‐peak” failure mechanism to a “peak‐plateau‐peak,” which is controlled by the brick shape. It is also shown that 3D structures may exhibit significant out‐of‐plane deformation involving the cooperative motion of many bricks, which may contribute to their improved toughness compared to 2D structures.

Effects of Geometry Design Parameters on the Fatigue Failure of a Drive Axle Housing using Finite Element Analysis

The current paper investigates the effects of geometric design parameters on the fatigue failure of the drive axle housing using the Finite Element Method (FEM). The study examines the effects of various factors on the fatigue life of the drive axle housing, such as axle housing wall thickness, housing cross-sectional rounding radius, and rounding radius of the central part of the housing. Based on the known material properties and dynamic loads, a CAD/FEM model of the drive axle housing was developed, and a structural analysis was carried out. Based on the results of the structural analysis, critical places on the housing were determined, and fatigue analysis and lifetime prediction were performed. Through a series of simulations, the study reveals that increasing housing wall thickness can significantly improve fatigue performance. Similarly, increasing the rounding radius at the housing cross-section, as well as the rounding radius at the central part of the housing can also lead to improved fatigue performance. However, the effect of increasing the value of these two radii is not as significant as the effect of the wall thickness. These findings give useful information regarding the design and manufacture of drive axle housings for vehicles, intending to reduce the likelihood of fatigue failure.

Geometric parameter design and contact characteristics of beveloid gear and involute cylindrical gear transmission with crossed axes

Geometric parameter design and contact characteristics of beveloid gear and involute cylindrical gear transmission with crossed axes