Simulation-based Design Approach Research Articles

Enhancing hull form design for robust efficiency: A data-enhanced simulation-based design approach

This paper presents a design approach that integrates machine learning techniques with traditional physics based simulations/models to enhance the ship design process with robust efficiency. While generative machine learning methods, which can directly produce design outputs such as the 3D hull form, have the potential to transform the design strategy, ship design inherently involves a decision-making process that requires consensus among stakeholders based on a foundation in physics-based simulations/models. This paper proposes a practical design strategy that positions physics-based simulations/models at the core of the design process, augmented by data-driven models. The paper first classifies hybrid types of the two models and integrates them into a practical design process. Finally, it demonstrates the effectiveness of the proposed design approach by showcasing the impact of data circulation, which accumulates and reinforces data in day-to-day design operations, on improving design outcomes.

Monolithic AM façade: multi-objective parametric design optimization of additively manufactured insulating wall elements

Additive Manufacturing (AM) offers transformative opportunities to create functionally hybridized, insulating, monolithic AM wall elements. The novel fabrication methods of AM allow for the production of highly differentiated building components with intricate internal and external geometries, aiming for reduced material use while integrating and enhancing building performance features including thermal insulation performance. This study focuses on integrating such thermal insulation performance by leveraging the individual features of three distinct AM processes: Selective Paste Intrusion (SPI), Selective Cement Activation (SCA), and Extrusion 3D Concrete Printing (E3DCP). Using a simulation-based parametric design approach, this research investigates 4,500 variations of monolithic AM façade elements derived from a generative hexagonal cell layout with differing wall widths, the three respective AM processes, different material compositions with and without lightweight aggregates, and three different insulation strategies, namely, air-filled cells, encapsulated lightweight aggregates, and additional insulation material within the cavities. Thermal performance feedback is realized via 2D heat flux simulations embedded into a parametric design workflow, and structural performance is considered in a simplified way via geometric and material-specific evaluation. The overall research goal is a multi-objective design optimization, particularly identifying façade configurations that achieve a U-value of less than 0.28 W/m2K and a theoretical compressive strength exceeding 2.70 MN per meter wall length. The results of this study detect 7% of all generated variations in line with these thermal and structural requirements, validating the feasibility of monolithic, thermally insulating AM wall elements. The presented workflow contributes to exploiting the potential of a new design of functionally hybridized AM components.

Parametric Design and Prototyping of a Low-Power Planar Biped Robot.

This study proposes a design approach and the development of a low-power planar biped robot named YU-Bibot. The kinematic structure of the robot consists of six independently driven axes, and it weighs approximately 20 kg. Based on biomimetics, the robot dimensions were selected as the average anthropomorphic dimensions of the human lower extremities. The optimization of the mechanical design and actuator selection of the robot was based on the results of parametric simulations. The natural human walking gait was mimicked as a walking pattern in these simulations. As a result of the optimization, a low power-to-weight ratio of 30 W/kg was obtained. The drive system of the robot joints consists of servo-controlled brushless DC motors with reduction gears and additional bevel gears at the knee and ankle joints. The robot features spring-supported knee and ankle joints that counteract the robot's weight and compensate for the backlash present in these joints. The robot is constrained to move only in the sagittal plane by using a lateral support structure. The robot's feet are equipped with low-cost, force-sensitive resistor (FSR)-type sensors for monitoring ground contact and zero-moment point (ZMP) criterion. The experimental results indicate that the proposed robot mechanism can follow the posture commands accurately and demonstrate locomotion at moderate stability. The proposed parametric natural gait simulation-based design approach and the resulting biped robot design with a low power/weight ratio are the main contributions of this study.

Monolithic PneuNets Soft Actuators for Robotic Rehabilitation: Methodologies for Design, Production and Characterization

Soft-robotics for biomedical applications, such as rehabilitation robots, is a field of intense research activity. Different actuation solutions have been proposed in the last decades, involving study and development of soft actuators of different types and materials. The purpose of the paper is to present procedures for an optimized design, and for easy and low cost production and characterization of monolithic PneuNets soft-actuators. An innovative design approach has been developed. The parameterization of the geometry, combined with FEM simulations is the basis for an optimized design of the actuator, as a function of the obtained bending and of the generated forces. Simple and cheap characterization setup and procedures have been identified for the actuator characterization and for simulation results validation. An easy and low-cost fabrication method based on lost wax core obtained through a silicone based mold has been developed for a monolithic PneuNets soft-actuator. The proposed solution performs well in bending, without the need for a strain limiting layer. Experimental results validated simulations, confirming the feasibility of adopting an optimized simulation-based design approach.

Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach.

During the second phase of SARS-CoV-2, an unknown fungal infection, identified as black fungus, was transmitted to numerous people among the hospitalized COVID-19 patients and increased the death rate. The black fungus is associated with the Mycolicibacterium smegmatis, Mucor lusitanicus, and Rhizomucor miehei microorganisms. At the same time, other pathogenic diseases, such as the Monkeypox virus and Marburg virus, impacted global health. Policymakers are concerned about these pathogens due to their severe pathogenic capabilities and rapid spread. However, no standard therapies are available to manage and treat those conditions. Since the coptisine has significant antimicrobial, antiviral, and antifungal properties; therefore, the current investigation has been designed by modifying coptisine to identify an effective drug molecule against Black fungus, Monkeypox, and Marburg virus. After designing the derivatives of coptisine, they have been optimized to get a stable molecular structure. These ligands were then subjected to molecular docking study against two vital proteins obtained from black fungal pathogens: Rhizomucor miehei (PDB ID: 4WTP) and Mycolicibacterium smegmatis (PDB ID 7D6X), and proteins found in Monkeypox virus (PDB ID: 4QWO) and Marburg virus (PDB ID 4OR8). Following molecular docking, other computational investigations, such as ADMET, QSAR, drug-likeness, quantum calculation and molecular dynamics, were also performed to determine their potentiality as antifungal and antiviral inhibitors. The docking score reported that they have strong affinities against Black fungus, Monkeypox virus, and Marburg virus. Then, the molecular dynamic simulation was conducted to determine their stability and durability in the physiological system with water at 100 ns, which documented that the mentioned drugs were stable over the simulated time. Thus, our in silico investigation provides a preliminary report that coptisine derivatives are safe and potentially effective against Black fungus, Monkeypox virus, and Marburg virus. Hence, coptisine derivatives may be a prospective candidate for developing drugs against Black fungus, Monkeypox and Marburg viruses.

Simulation-based data-driven design of digital short fibre reinforced composites

Additive manufacturing is a state-of-the-art method used to produce digital composites with complex structure and composition. One promising type of digital composite is Digital Short Fibre Reinforced Composite (DSFRC), whose properties can be digitally controlled by adjusting the material's micro or mesoscale structures. In this paper, we propose a simulation-based data-driven design approach to efficiently explore the property space of DSFRC. Our method can obtain optimal material descriptors to achieve tailored material properties.

Quantifying potential dynamic façade energy savings in early design using constrained optimization

Parametric design and optimization studies have demonstrated high energy savings for dynamic building envelope materials compared to static high-performance envelopes. However, most parametric studies about dynamic buildings were conducted on prototypical buildings with a focus on either optimal geometric settings or idealized material property characteristics, neglecting the potential collective effects of geometric and material design decisions on energy performance. This study investigates the implications of an automated sequential optimization process while designing with dynamic envelope materials. Two case studies were used to quantify energy savings across different optimization-based design procedures and identify the relative importance of various decision categories. When considering realistic design constraints and intrinsic material limitations, geometric optimization alone yielded only 2% energy savings, while dynamic material optimization savings reached up to 19%. Significantly, a sequential design process in which the geometry is configured first before the façade is optimized and vice versa can lead to around 5% missed energy savings. These findings encourage changes to traditional design guidelines and simulation-based building design approaches when working with dynamic façades.

WaSim model for subsurface drainage design using soil hydraulic parameters estimated by pedotransfer functions

The agricultural drainage engineering community is steadily shifting the design of subsurface drainage systems from the experience-based design approach to the simulation-based design approach. As with any design problem, two challenges are faced; firstly, how to determine all the input data required by the simulation model, and secondly to, a priori, anticipate what the performance of the designed system will be. This study sought to evaluate the performance of the WaSim model to simulate fluctuating water table depths (WTD), and drainage discharges (DD) in KwaZulu-Natal Province, South Africa. Saturated hydraulic conductivity (Ksat), which is an input to the WaSim model, was estimated by the Rosetta computer program, based on soil particle size distribution data, bulk density, and soil water retention characteristics at pressure heads of – 33 and – 1500 kPa. performance of the WaSim model was statistically assessed using the coefficient of determination (R2), coefficient of residual mass (CRM), mean absolute error (MAE), mean percent error (MPE), and the nash–sutcliffe efficiency (NSE). during the validation period, the WaSim model predicted WTDs with R2, CRM, MAE, MPE, and NSE of 0.86, 0.003, 4.9 cm, 6.0%, and 0.98, respectively. In the same validation period, the model predicted DDs with R2, CRM, MAE, MPE, and NSE of 0.57, 0.002, 0.30 mm day−1,11%, and 0.76, respectively. These results suggest that the use of Rosetta-estimated Ksat data as inputs to the WaSim model compromised its accuracy and applicability as a subsurface drainage design tool. Owing to the relatively low R2 value of 0.57, and that the WaSim model was empirically developed, we recommend further improvement on the calibration of the model for it to be suitable for application under the prevailing conditions. Also, in the absence of other means of determining Ksat, we caution the use of Rosetta-estimated Ksat data as inputs to the WaSim model for the design and analysis of subsurface drainage systems in KwaZulu-Natal Province, South Africa.

Sensitivity analysis of geometrical design parameters on the performance of conical plain bearings for use as main bearings in wind turbines

The failure of a wind turbine’s main bearing causes very high repair costs, as the entire drivetrain has to be disassembled with the help of an external crane. Just the costs for a crane for an offshore turbine can sum up to 200.000€ per day [1]. In contrast, segmented hydrodynamic plain bearings offer the advantage that individual bearing segments can be replaced on the tower without the use of an external crane, which offers great potential for savings in repair costs. However, this application requires new, innovative plain bearing concepts for which the current design guidelines are not applicable. This paper presents a simulation-based design approach for a plain bearing concept with conical sliding surfaces. Such a simulation-based approach with a multi-objective optimization is necessary, because due to the geometric complexity the manual, iterative design process is not efficient. In this paper, a bearing geometry optimized in terms of load distribution and friction losses was developed by applying this approach.

Approximating the Steady-State Temperature of 3D Electronic Systems with Convolutional Neural Networks

Thermal simulations are an important part of the design process in many engineering disciplines. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. An alternative, fast simulation tool would be a welcome addition to any automatized and simulation-based optimisation workflow. In this work, we present a proof-of-concept study of the application of convolutional neural networks to accelerate thermal simulations. We focus on the thermal aspect of electronic systems. The goal of such a tool is to provide accurate approximations of a full solution, in order to quickly select promising designs for more detailed investigations. Based on a training set of randomly generated circuits with corresponding finite element solutions, the full 3D steady-state temperature field is estimated using a fully convolutional neural network. A custom network architecture is proposed which captures the long-range correlations present in heat conduction problems. We test the network on a separate dataset and find that the mean relative error is around 2% and the typical evaluation time is 35 ms per sample (2 ms for evaluation, 33 ms for data transfer). The benefit of this neural-network-based approach is that, once training is completed, the network can be applied to any system within the design space spanned by the randomized training dataset (which includes different components, material properties, different positioning of components on a PCB, etc.).

Analysis of a Lightweight Aluminum Vehicle Chassis in a Simulation-Based Design Approach

This study investigates different chassis designs through a simulation-based design approach. The inherent aluminum ductility and softness could make chassis a daunting modification if not analyzed properly. Structural finite element analysis is comprehensively performed on a vehicle chassis for static loading cases up to 1G in equivalent acceleration. The analysis of the vehicle chassis of both A36 steel and 6061 aluminum for the scenarios of bump, front impact, side impact and a rollover. The von Mises stresses and displacement results showed that the steel chassis possessed higher safety factor in all load cases. The safety factors for an aluminum clone of the steel chassis in some load cases are below 1.0, hence indicating that the failure criterion has been triggered and failure would occur under the 1G load. The original aluminum chassis deformation is far more severe than steel reaching as high as 9.88 mm for the bump loading. A modified aluminum chassis is proposed, by optimizing the wall thickness of the rectangular bars. The slight increase in weight resulted in overcoming the deficiency of aluminum in load carrying capacity. An evaluation matrix procedure is implemented to analyze the trade offs between cost, weight and safety factor for the three chassis materials.

Design simulations for a combined shaker-acoustic vibration test technique

A novel approach of combining multiple shaker and acoustic inputs can achieve a high-fidelity ground vibration test. Additionally, supplementing acoustic inputs with shakers enables higher response levels which is possible with shakers alone. As this is a new testing concept, exploring test design effects using simulations is useful. Here, models and simulation are utilized to explore how combining shaker and acoustic inputs can improve response levels and reduce input requirements while maintaining test accuracy. The derivation of acoustic and shaker inputs is explored using different control or input estimation techniques. In particular, the correlation requirements between shaker and acoustic inputs is examined using a Kronecker product formulation of the control problem, which enables specific inputs to be uncorrelated. This simulation-based test design approach provides useful guidance in the design of combined shaker-acoustic vibration tests. [Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under Contract No. DE-NA0003525.]A novel approach of combining multiple shaker and acoustic inputs can achieve a high-fidelity ground vibration test. Additionally, supplementing acoustic inputs with shakers enables higher response levels which is possible with shakers alone. As this is a new testing concept, exploring test design effects using simulations is useful. Here, models and simulation are utilized to explore how combining shaker and acoustic inputs can improve response levels and reduce input requirements while maintaining test accuracy. The derivation of acoustic and shaker inputs is explored using different control or input estimation techniques. In particular, the correlation requirements between shaker and acoustic inputs is examined using a Kronecker product formulation of the control problem, which enables specific inputs to be uncorrelated. This simulation-based test design approach provides useful guidance in the design of combined shaker-acoustic vibration tests. [Sandia National Laboratories is a multimission laborator...

Designing Metagratings via Local Periodic Approximation: From Microwaves to Infrared

Recently, metamaterials-inspired diffraction gratings (or metagratings) have demonstrated unprecedented efficiency in wavefront manipulation by means of relatively simple structures. Conventional one-dimensional (1D) gratings have a profile modulation in one direction and a translation symmetry in the other. In 1D metagratings, the translation invariant direction is engineered at a subwavelength scale, which allows one to accurately control polarization line currents and, consequently, the scattering pattern. In bright contrast to metasurfaces, metagratings cannot be described by means of surface impedances (or local reflection and transmission coefficients). In this paper, we present a simulation-based design approach to construct metagratings in the "unit cell by unit cell" manner. It represents an analog of the local periodic approximation that has been used to design space-modulated metasurfaces and allows one to overcome the limitations of straightforward numerical optimization and semianalytical procedures that have been used to date to design metagratings. Electric and magnetic metagrating structures responding to, respectively, transverse electric and transverse magnetic incident plane waves are presented to validate the proposed design approach.

Simulation-Supported Early Stage Design Optimisation for a Case Study of Life Cycle Oriented Buildings

To reduce the energy and resource consumption in the building sector this study is focusing on a design optimisation of life cycle oriented buildings. In order to optimise the performance of the buildings and in consequence also to achieve improved results for the mandatory Austrian energy certificate a simulation-based rapid design approach is used for the early stage design phase of the buildings, in particular for the architectural design of the buildings.Methods like the Window to Wall Ratio, at the very beginning of the design process, a parametric simulation with EnergyPlus or a more detailed optimisation approach with GenOpt are integrated in this study applied to example buildings. The results are showing that the method can be used in a circular approach for improving the heating demand of the Austrian energy certificate for this case study by more than 25 % compared to the preliminary design

Integrated computational materials engineering for advanced materials: A brief review

After developing the simulation-based design approach of materials for decades, computational materials science/engineering present the power in accelerating the discoveries and the applications of novel advanced materials through a digital-twin design paradigm of integrated computational materials engineering (ICME). While the short goals of ICME are almost accomplished, those long goals are on the right way, highlighting the concept/strategy of materials by design. In this brief review, the recent frameworks of data-driven ICME in the last two years are discussed, presenting key aspects of principles, benchmarks, standards, databases, platforms and toolkits via various case studies. The author and his collaborators display a routine of data-driven ICME utilized in the investigations of Mg alloys through integrating the high-throughput first-principles calculations and the CALPHAD approach. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of pure elements, alloys, metal melts, oxides and semiconductors, surface and interfaces, but also reveal the fundamental solid-solution strengthening/embrittlement mechanism and the grain refinement mechanism, paving a path in accelerating the development of advanced materials. It is believed that the combinations of high-throughput multi-scale computations and fast experiments/manufacturing will build the advanced algorithms in the development of a promising digital fabricating approach to overcome the present and future challenges, illuminating the way toward the digital-twin intelligent manufacturing era.

A simulation-based concept design approach for combustion engine and battery electric vehicles

The early concept design of a vehicle is becoming increasingly crucial to determine the success of a car. Broadening market competition, more stringent regulations and fast technological changes require a prompt response from carmakers, and computer-aided engineering has emerged in recent years as the promising way to provide more efficient and cost-effective design and to cut development time and costs. The work presented in this paper shows an approach based on computer-aided engineering to determine vehicle’s energy consumption and performance. The different vehicle’s subsystem are first analyzed separately by using dedicated simulation tools and then integrated to obtain the entire vehicle. The work covers a wide range of vehicle layouts. Internal combustion engine vehicles and battery electric vehicles are considered and various transmission configurations are contemplated with respect to some of the most adopted solutions for these vehicles. The simulation results allow to identify the most effective design variables regarding the combustion engine and the electric motor and to compare the different layouts over various car segments. The results clearly point out that for internal combustion engine vehicles, the combustion engine is the crucial component that defines the vehicle’s characteristics and particularly the energy consumption. Conversely, battery electric vehicles show a more balanced distribution of the losses, and therefore to improve the vehicle’s behavior, different components should be considered in detail. Nevertheless, the choice of the number of electric motors and the transmission choice play a significant role in defining the vehicle performances.

A system approach to selective catalyst reduction DeNOx monolithic reactor modelling using bond graphs

Stricter NOx emission limits for marine diesel engines have resulted in a market demand for engine external NOx reduction solutions. This demand has led to the development of ammonia-based selective catalytic reduction DeNOx systems for marine applications. For selective catalytic reduction systems in general, mathematical modelling and numerical simulation have been essential for increasing knowledge, improving the design and developing control algorithms. This has resulted in higher NOx reduction performance, reducing NH3 slip and improving transient and start-up performance. Due to the increasing complexity of diesel-engine-based power systems, it is often argued that system development requires a simulation-based design approach to reduce development cost and increase development speed. For this to be cost-effective, reusable and interchangeable models of appropriate complexity need to be available. In this article, a system approach is applied to modelling of selective catalytic reduction DeNOx monolithic reactors. Three models with different levels of fidelity are developed using the bond graph method. The three models are compared by simulating dynamic conditions to uncover differences between the models. In addition, accuracy is investigated by comparing the simulation results with measurement data. The contribution in this article can be summarized to be an exploration of monolith selective catalytic reduction DeNOx modelling in a system simulation framework, and investigation of the effect of selective catalytic reduction model fidelity on a coupled system performance prediction.

Simulation–Optimization-Based Design of Crude Oil Distillation Systems with Preflash Units

In energy-intensive crude oil distillation systems, with their distillation units and heat recovery systems, preflash units can create opportunities to reduce demand for fired heating and increase heat recovery. As holistic, systematic design methodologies are not available for design of crude oil distillation systems with preflash units, this work proposes a simulation-based design approach, where the objective is to minimize fired heat demand of the system. The approach exploits interactions between the separation and heat recovery systems, considering product quality and yield. Rigorous distillation models in Aspen HYSYS, pinch analysis, and randomized optimization (a genetic algorithm) are employed, via a MatLab interface, to minimize hot utility demand by optimizing operating variables and one structural variable, namely, the feed location of the preflash vapor. A case study demonstrates the approach, showing that a preflash unit can reduce hot utility demand but that the system is vulnerable to yiel...

Finite Element Analysis for Deep Drawing Process and Part Shape Optimization

The paper presents some aspects regarding the sheet metal deep drawing process and shape optimzation for such parts using Finite Element Analysis. Simulation based design approaches have been used for forming processes. Using similar approach, a full process simulation and optimisation was undertaken in the present work. The work done here involved computer simulations of the forming processes to evaluate the amount of strain and stress each forming process contributed to the final product. Based on the simulation results, recommendation to change the product shape were presented. Not all the virtual products should be manufactured and the Finite Element Analysis eliminate all the possible errors and let the designers to optimize the geometry before designing the Molds and Dies for Deep Drawing process.

A "plug and play" computationally efficient approach for control design of large-scale nonlinear systems using cosimulation: a combination of two "ingredients"

This article describes a computationally efficient simulation-based control design approach that has the capability of handling optimization problems arising from large-scale nonlinear systems, with fast convergence properties and low computational requirements. The purpose of this article is to describe the main features of the PCAO algorithm, analyze its convergence and stability properties, and demonstrate its efficiency using simulations of two large-scale, real-life systems (a traffic network and an energy-efficient building) for which conventional optimization techniques fail to provide an efficient simulation-based control design.