Simulation-driven Design Research Articles

LATTICE STRUCTURES GRADING FOR STIFFNESS OPTIMIZATION: AN APPROACH BASED ON TOPOLOGY OPTIMIZATION FOR PARTS MANUFACTURED VIA MULTI JET FUSION

The rapid advancement of additive manufacturing (AM) technology and computational design techniques has unveiled a promising synergy between these two fields. The high print resolution achievable nowadays enables the pro-duction of intricate shapes, such as lattice structures, which can be further opti-mized through simulation-driven design techniques such as topology optimization (TO). This paper presents an optimization workflow that leverages field-driven design to employ the results of a TO to locally vary the diameter of lattice struc-tures, aiming for weight reduction and stiffness maximization. The process in-volves the automation of nTop design software via Python scripts to systemati-cally evaluate design variants and identify optimal solutions. The workflow steps are demonstrated through the application to a connecting rod and conclude with the evaluation of the achieved mechanical performance of the optimized compo-nent via Finite Element Method (FEM) analysis. Keywords: Additive Manufacturing, Topology Optimization, Lattice Structures, Field-Driven Design, Finite Element Method.

Predicting the biomechanical behavior of lumbar intervertebral Discs: A comparative finite element analysis of a novel artificial disc design.

Osseointegration along with better mimicry of natural bone behaviour addresses the long-term performance of artificial intervertebral disc prosthesis. Here the effect of a novel artificial intervertebral disc geometry on stress, deformation and strain on lumbar segments to restore movement of the spine was investigated. The process involved, using CT image data, and solid modelling, simulation-driven design and finite element (FE) analysis, hexahedral mesh sensitivity analysis, implant placements. The range of motion (ROM) was calculated using an ANSYS deformation probe. The intact lumbar spine model established was compared with two implants, replacement at segment L4-L5 level, and biomechanical results were compared using axial loads of 500N, 800N, 1000N and 10Nm moment. The two lumbosacral FE models, a novel implant Titanium Conix (TIC) and another FDA approved SB Charite™ (SBC) implant were considered. Novel TIC implant geometry exhibited comparable ROM values in four physiological motions, which were comparable to as required for restoring natural motion. The result shows that the proposed TIC observed the deformation during flexion, extension, bending and twist as 3.43mm, 3.19mm, 3.33mm and 3.48mm respectively. Similarly strain of 0.01 during flexion, 0.02 during extension, 0.01 during bending and 0.02 during twist. The implants designed in this study demonstrate the suitability of titanium alloy in endplates and annulus. The FE models in the study with their biomechanical parameters can be considered before clinical implementation of any implants, pre-surgery evaluations, implant placement simulations, postsurgical response, follow-up revisions, implant customization and manufacturing.

Estimation of temperature-dependent piezoelectric material parameters using ring-shaped specimens

Abstract Simulation-driven design processes of piezoelectric actuators and sensors necessitate precise material parameter knowledge. Historically, the IEEE standard on piezoelectricity is used for this purpose, which proposes an analytical method for material parameter identification based on resonance frequency evaluations. This method requires different impedance measurements on differently shaped and processed samples, which yields inconsistent material parameter sets due to processing effects on material properties. Previous studies show that an inverse procedure using a single disc-shaped specimen with some adaptations can be used to identify a full set of parameters. As high-power actuators often use ring-shaped components, this method needs adaptation for this kind of specimen. Furthermore, the dependence of the material parameters on temperature needs to be considered to characterise the temperature effects and the associated non-linear effects of the specimens. For this purpose, samples with different geometrical dimensions at a range of different temperatures are examined in this study. Material parameters are then estimated using an inverse approach utilising the measurement data in conjunction with a numerical FEM simulation model. The observed temperature dependence of the estimated material parameters is examined and presented in this contribution.

Simulation-driven design of stabilized SARS-CoV-2 spike S2 immunogens

The full-length prefusion-stabilized SARS-CoV-2 spike (S) is the principal antigen of COVID-19 vaccines. Vaccine efficacy has been impacted by emerging variants of concern that accumulate most of the sequence modifications in the immunodominant S1 subunit. S2, in contrast, is the most evolutionarily conserved region of the spike and can elicit broadly neutralizing and protective antibodies. Yet, S2’s usage as an alternative vaccine strategy is hampered by its general instability. Here, we use a simulation-driven approach to design S2-only immunogens stabilized in a closed prefusion conformation. Molecular simulations provide a mechanistic characterization of the S2 trimer’s opening, informing the design of tryptophan substitutions that impart kinetic and thermodynamic stabilization. Structural characterization via cryo-EM shows the molecular basis of S2 stabilization in the closed prefusion conformation. Informed by molecular simulations and corroborated by experiments, we report an engineered S2 immunogen that exhibits increased protein expression, superior thermostability, and preserved immunogenicity against sarbecoviruses.

Simulation-driven design of smart gloves for gesture recognition

Smart gloves are in high demand for entertainment, manufacturing, and rehabilitation. However, designing smart gloves has been complex and costly due to trial and error. We propose an open simulation platform for designing smart gloves, including optimal sensor placement and deep learning models for gesture recognition, with reduced costs and manual effort. Our pipeline starts with 3D hand pose extraction from videos and extends to the refinement and conversion of the poses into hand joint angles based on inverse kinematics, the sensor placement optimization based on hand joint analysis, and the training of deep learning models using simulated sensor data. In comparison to the existing platforms that always require precise motion data as input, our platform takes monocular videos, which can be captured with widely available smartphones or web cameras, as input and integrates novel approaches to minimize the impact of the errors induced by imprecise motion extraction from videos. Moreover, our platform enables more efficient sensor placement selection. We demonstrate how the pipeline works and how it delivers a sensible design for smart gloves in a real-life case study. We also evaluate the performance of each building block and its impact on the reliability of the generated design.

Ship Design in the Era of Digital Transition

The evolution of ship design from a manual toward a computer-aided, digital approach has been drastic after the 1970s, with the explosive development of computer hardware and software systems. In today’s era of smart digitalization in the frame of Industry 4.0, recently introduced digital/software tools and systems increase the efficiency and quality of the life-cycle ship design process, but also the operational complexity and the demand for proper training of users of software platforms. Parametric optimisation and simulation-driven design, product lifecycle management, digital twins and artificial intelligence are nowadays frequently used by the maritime industry during the commissioning/quality control activities and in the various phases of ship design, ship operation and ship production. This paper presents an overview of notable developments in the above areas and the way ahead to respond to present and future challenges of the maritime industry.

Simulation-driven design of a fast monohull

ABSTRACT A fast monohull was designed and optimized, using CAESES® for parametric modeling and optimization coupled to STAR-CCM + for free-surface RANS simulations. The boat is a 9.5ton pilot boat, featuring tunneled propellers, shafts and rudders. The optimizations were undertaken for thrust at design speed of 27.5kn. The boat was free to trim and rise, utilizing an overset grid for high resolution and acceptable turn-around time. Propulsion was taken into account via an actuator disc, balancing resistance and thrust. The geometry of the bare hull and tunnel were varied during the optimizations, using fully-parametric models. A systematic comparison of numerical and experimental data was undertaken. The experiments were conducted with the high-speed carriage at TU Berlin, using a 3.5m fully-appended self-propelled model. The optimizations comprised Design-of-Experiments, deterministic searches, surrogates and global strategies. Substantial improvements were found with variants being more energy-efficient than a representative baseline. The paper discusses modeling, simulations and optimizations along with selected results.

Enhancement of fatigue life modeling using a metamodel-based global sensitivity analysis framework

Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the dependency between the simulation's output and the model's input is quantifed. However, classical GSA approaches, such as Sobol’ indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been applied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three different metamodels for performing GSA. The micromechanical model predicts the stiffness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters affect the material properties. The obtained sensitivity analysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach.

Integrating Wind Flow Analysis in Early Urban Design: Guidelines for Practitioners

The research focused on simulating wind patterns in urban planning design offers substantial contributions to both the social and economic aspects of the urban planning and design field. To begin with, it addresses a critical factor in urban development, especially in Mediterranean climates, where natural ventilation significantly influences summer comfort. By incorporating predictive numerical simulations of urban wind patterns, this study provides valuable insights into improving outdoor thermal comfort within urban areas. This holds particular importance in the context of adapting to climate change, as it equips urban planners and architects with informed decision-making tools to create more sustainable and comfortable urban environments. Additionally, this research makes an economic contribution by presenting guidelines for iterative wind simulations in the early stages of designing medium-scale urban projects. Through the validation of a simulation workflow, it streamlines the design process, potentially reducing the time and resources required for urban planning and architectural design. This enhanced efficiency can result in cost savings during project development. Moreover, the study's recommendations concerning simulation parameters, such as wind tunnel cell size and refinement levels, offer practical insights for optimizing simulation processes, potentially lowering computational expenses and improving the overall economic viability of urban design projects. To summarize, this research effectively addresses climate-related challenges, benefiting both social well-being and economic efficiency in the field of urban planning and design, while also providing guidance for more efficient simulation-driven design procedures.

Reduced-cost microwave modeling using constrained domains and dimensionality reduction

Development of modern microwave devices largely exploits full-wave electromagnetic (EM) simulations. Yet, simulation-driven design may be problematic due to the incurred CPU expenses. Addressing the high-cost issues stimulated the development of surrogate modeling methods. Among them, data-driven techniques seem to be the most widespread owing to their flexibility and accessibility. Nonetheless, applicability of approximation-based modeling for real-world microwave components is hindered by a high nonlinearity of the system characteristics, dimensionality issues, and broad ranges of operating parameters the model should cover to make it practically useful. Performance-driven modeling frameworks deliver a partial mitigation of these problems through appropriate spatial orientation of the metamodel domain, which only encapsulates high-quality designs and not the entire space. Unfortunately, the initial model setup cost is high, as defining the domain requires database designs that need to be a priori acquired. This paper introduces a novel approach, where the database designs are replaced by random observables, and dimensionality of the domain is reduced using spectral analysis thereof. The major contributions of the work include implementation of the explicit dimensionality reduction of the confined surrogate model domain and introducing this concept into a complete cost-efficient framework for modeling of microwave components. Comprehensive benchmarking demonstrates excellent performance of the introduced framework, both in terms of predictive power of the rendered surrogates, their scalability properties, as well as low computational overhead associated with the model setup.

High-efficacy global optimization of antenna structures by means of simplex-based predictors

Design of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the accumulated cost of multiple antenna evaluations. This problem is especially pronounced in the case of global search, frequently performed using nature-inspired algorithms, known for poor computational efficiency. At the same time, global optimization is often required, either due to multimodality of the design task or the lack of sufficiently good starting point. A workaround is to combine metaheuristics with surrogate modeling methods, yet a construction of reliable metamodels over broad ranges of antenna parameters is challenging. This work introduces a novel procedure for global optimization of antenna structures. Our methodology involves a simplex-based automated search performed at the level of approximated operating and performance figures of the structure at hand. The presented approach capitalizes on weakly-nonlinear dependence between the operating figures and antenna geometry parameters, as well as computationally cheap design updates, only requiring a single EM analysis per iteration. Formal convergence of the algorithm is guaranteed by implementing the automated decision-making procedure for reducing the simplex size upon detecting the lack of objective function improvement. The global optimization stage is succeeded by gradient-based parameter refinement. The proposed procedure has been validated using four microstrip antenna structures. Multiple independent runs and statistical analysis of the results have been carried out in order to corroborate global search capability. Satisfactory outcome obtained for all instances, and low average computational cost of only 120 EM antenna simulations, demonstrate superior efficacy of our algorithm, also in comparison with both local optimizers and nature-inspired procedures.

Combustion customization strategy of mixed propellant charges for multi-material additive manufacturing: Simulation and experiment

This paper addressed three major challenges in the customization of mixed propellant charges: optimal design, performance simulation, and manufacturing. Based on parametric modeling software Grasshopper, parametric models of propellants with highly complex geometries were endowed with combustion properties of multiple formulations, coupled with rapidly changing burning rate-pressure field during iterative combustion to achieve visual and integrated simulation of burning surface regression, form function, and combustion performance of mixed charges, providing convenient theoretical guidance for structural optimization design of customized strategies. Customized zones designed with complex geometries and multiple formulations co-constructed in one propellant were prepared by multi-material additive manufacturing techniques. Different customized zones of one or more propellants were superimposed in the design sequence to customize the combustion performance. Mixed charges for three cases of burning surface, burning rate, and joint control were discussed. The simulation results matched well with the experimental results, including the progressively random perforated combustion, variation of burning rate pressure exponent, and superimposed combustion of single and multiple charges with different customized zones. As an advanced engineering strategy urgently needed in chemical engineering, this work not only opens the door to matching performance simulation-driven design with MM-AM technologies but also serves as an efficient and common customization strategy from design to manufacture.

Design of GPIO Transmitter

Abstract: This paper presents a comprehensive design approach for a versatile 1.8V GPIO transmitter tailored for a 5pf load capacitance. The work introduces a novel design that accommodates 1.8V IO supply, enhancing the flexibility and compatibility of the GPIO transmitter. The GPIO consists of a level shifter and a driver. A level up shifter is used along with a driver capable of driving the signals without any data loss. The work embarks on the objective of designing a robust GPIO transmitter by utilizing a combination of level shifting and driver circuitry. The methodology encompasses meticulous simulation-driven design, encompassing a 45nm technology node and the Cadence Virtuoso platform. The design incorporates a level shifter that elevates input data from a core domain operating at 0.8V to IO levels of 1.8V , ensuring seamless communication across various voltage domains. The driver circuit, implemented with progressive sizing, effectively drives the substantial load capacitance of 5pF, maintaining the integrity of input data. Through detailed analysis, the architecture ensures compliance with the demanding specifications of Intel Max 10 FPGA. Simulation results exhibit the efficacy of the proposed design. The level shifter successfully transforms an incoming 0.8V data signal to 1.79V , aligning with the 1.8V IO requirements. The driver also drives the same signal out with a load of 5pf. The level shifter and driver is combined to check for the working of transmitter, with input at 0.8V and the signal is obtained at the expected voltage level at the output of driver

Experimental and simulated study of 3D-printed couplings’ suitability for industrial application

This paper explores the possibility of applying reverse engineering to flexible coupling spare parts through additive manufacturing. Although couplings’ simplicity makes them go unnoticed, they connect elements that transmit power between two shafts, thus being an essential component for most of the machinery currently used in the industry. In this study, flexible couplings with different infill density (60% and 80%) were 3D printed by the fused filament fabrication technique. The original and the additive manufactured couplings were modelled to compare their compressive response and energy-absorbing characteristics, and experimental tests were performed to validate finite element analysis. To derive an optimal material distribution within coupling structure, a generative design approach was conducted through nTopology software. With this novel simulation-driven design, it is possible to reduce the weight of the redesigned part up to 15.8% by defining the wall thickness of the internal structure based on the results of finite element simulation, while maintaining its functionality. Moreover, an economic-environmental study was carried out. Results ensure that the 3D printed prototypes are suitable for replacing the original one under its current operating conditions. Additionally, the economic study shows that the redesigned couplings allow companies to save more than €2700 per coupling in relation to CO2 emission payments.

Optimising General Configuration of Wing-Sailed Autonomous Sailing Monohulls Using Bayesian Optimisation and Knowledge Transfer

Wing-sailed autonomous sailing monohulls are promising platforms used in various scenarios to provide data for marine science research. These platforms need to operate long-term in changing seas; their general configurations (size matching between sail, hull, and keel) necessitate careful trade-offs to balance safety and efficiency. Since autonomous sailboats are often designed for different observation missions, scientific pay-loads and target areas, their design space is considerably large. It is also challenging to obtain prior performance estimation from historical designs. Therefore, traditional offline surrogate-based simulation-driven design frameworks suffer from a large amount of sampling required, the computational cost of which remains too expensive for such ad hoc design tasks. This paper proposes an innovative, generalised simulation-driven framework combining Bayesian optimisation and knowledge transfer. It allows for high-quality, low-cost optimisation of autonomous sailing monohulls’ general configuration without initial design and prior performance estimation. The proposed optimisation framework has been used to optimise the ‘Seagull’ prototype within the design constraints. The optimised design exhibits significant performance improvements. At the same time, the results show that the present method is significantly superior to traditional offline methods. The authors believe that the proposed framework promises to provide the autonomous sailing community with a solution for a general design methodology.

Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag

Today digital qualification tools are part of many design processes that make them dependent on long and expensive simulations, leading to limited ability in exploring design alternatives. Conventional surrogate modelling techniques depend on the parametric models and come short in addressing radical design changes. Existing data-driven models lack the ability in dealing with the geometrical complexities. Thus, to address the resulting long development lead time problem in the product development processes and to enable parameter-independent surrogate modelling, this paper proposes a method to use images as input for design evaluation. Using a case study on the curtain airbag design process, a database consisting of 60,000 configurations has been created and labelled using a method based on dynamic relaxation instead of finite element methods. The database is made available online for research benchmark purposes. A convolutional neural network with multiple layers is employed to map the input images to the simulation output. It was concluded that the showcased data-driven method could reduce digital testing and qualification time significantly and contribute to real-time analysis in product development. Designers can utilise images of geometrical information to build real-time prediction models with acceptable accuracy in the early conceptual phases for design space exploration purposes.

Topology-Optimized and Simulation-Driven Design for Compact Autonomous Electric Vehicle Drive Systems: A Novel Approach

Topology-Optimized and Simulation-Driven Design for Compact Autonomous Electric Vehicle Drive Systems: A Novel Approach

Structural optimization design of a bolster based on a simulation-driven design method

Abstract A simulation-driven design method which uses multiple optimization methods can effectively promote innovative structural design and reduce the product development cycle. Meanwhile, the sub-model technology which has more detailed simulation and optimization analysis can enormously improve the efficiency of modelling and solving. This study establishes a general workflow of structural optimization for a stainless-steel metro bolster by combining the simulation-driven design method and sub-model technology. In the sub-model definition phase, the end underframe sub-model which contains the bolster is obtained based on the whole car body finite element (FE) model, and the effectiveness of the end underframe sub-model is also proved. In the conceptual design phase, the topology path inside the bolster is obtained by the topology method and the optimized structure of the inner ribs inside the bolster is determined according to manufacturing processes and design experiences. In the detailed design phase, the thickness of each part of the bolster is determined by size optimization. The simulation analyses indicate that the requirements of static strength and fatigue strength are fulfilled by the optimized bolster structure. Besides, the weight can be reduced by 11.18% and the weld length can be decreased by 17.79% compared with the original bolster structure, which means that not only the lightweight design goal is achieved, but also the welding quantity and manufacturing difficulty are greatly reduced. The results show the effectiveness of the simulation-driven design method based on the sub-model technology in the structural optimization for key parts of rail transit vehicles.

Parametric model embedding

Methodologies for reducing the design-space dimensionality in shape optimization have been recently developed based on unsupervised machine learning methods. These methods provide reduced dimensionality representations of the design space, capable of maintaining a certain degree of the original design variability. Nevertheless, they usually do not allow to use directly the original parameterization method, representing a limitation to their widespread application in the industrial field, where the design parameters often pertain to well-established parametric models, e.g. CAD (computer aided design) models. This work presents how to embed the parametric-model original parameters in a reduced-dimensionality representation of the design space. The method, which takes advantage from the definition of a newly-introduced generalized feature space, is demonstrated, as a proof of concept, for the reparameterization of 2D Bezier curves and 3D free-form deformation design spaces and the consequent solution of simulation-driven design optimization problems of a subsonic airfoil and a naval destroyer in calm water, respectively.

Low-Cost Design Optimization of Microwave Passives Using Multifidelity EM Simulations and Selective Broyden Updates

Geometry parameters of contemporary microwave passives have to be carefully tuned in the final stages of their design process to ensure the best possible performance. For reliability reasons, the tuning has to be carried out at the level of full-wave electromagnetic (EM) simulations. This is because traditional modeling methods are incapable of quantifying certain phenomena that may affect the operation and performance of these devices, such as cross-coupling effects. As a consequence, the designs yielded with the use of equivalent network models may only serve as starting points that need further refinement. Unfortunately, simulation-driven numerical optimization is computationally demanding even in the case of local search procedures. Thus, significant research efforts have been aimed toward identifying effective ways of expediting EM-driven optimization procedures, critical from the point of view of cost of design cycles. Among these, one may list the recently proposed multifidelity optimization frameworks. Another option for accelerating simulation-driven design procedures is sparse sensitivity updating schemes, where costly gradient estimation through finite differentiation (FD) is suppressed for selected variables. This work proposes a novel algorithm that capitalizes on both aforementioned mechanisms to reduce the optimization cost of local gradient-based parameter tuning of compact microwave components. In our approach, multifidelity optimization is further expedited by replacing expensive FD sensitivity updates with the Broyden formula for selected design variables. Verification using two microwave structures, a branch-line coupler and a power divider, demonstrates average savings of around 80% over the basic trust-region (TR) routine, with only minor degradation of the design quality.