Computer-aided Engineering Tools Research Articles

Mold Flow Analysis of Motor Core Gluing with Viscous Flow Channels and Dipping Module.

A motor core is formed by stacking iron sheets on top of each other. Traditionally, there are two stacking methods, riveting and welding, but these two methods will increase iron loss and reduce usage efficiency. The use of resin is the current developmental trend in the technology used to join iron sheets, which has advantages including lowering iron loss, smoothing magnetic circuits, and generating higher rigidity. The flow behavior of resin in gluing technology is very important because it affects the dipping of iron sheets and the stacking of iron sheets with resin. In this study, a set of analytical processes is proposed to predict the flow behavior of resin through the use of computer-aided engineering (CAE) tools. The research results are compared with the experimental results to verify the accuracy of the CAE tools in predicting resin flow. CAE tools can be used to predict results, modify modules for possible defects, and reduce the time and costs associated with experiments. The obtained simulation results showed that the filling trend was the same as that for the experimental results, where the error between the simulation results for the final dipping process and the target value was 0.6%. In addition, the position of air traps is also simulated in the dipping process.

Fabric steering technology for variable stiffness panels: Manufacture and mechanical testing

A novel low-cost manufacturing process is introduced, referred to as ‘Fabric Steering’. By manipulating biaxial fabrics, curvilinear fibre paths can be created to manufacture variable stiffness panels, similar to those produced using Automated Fibre Placement [1]. With low equipment costs and the capability to steer multiple-layers of fabrics simultaneously, this technique potentially offers faster production rates and lower manufacturing costs compared to Automated Fibre Placement. A computer aided engineering tool, SteerFab [2,3], is used to guide the design and manufacture process by predicting: (a) the optimum 2D fibre paths, (b) the subsequent mechanical behaviour of the resulting variable stiffness panel (including improvements in buckling resistance and strength) and (c) step-by-step manufacturing instructions. Compared to conventional quasi-isotropic straight-fibre laminates, experimental buckling tests show slightly heavier (by 4.6%) steered-fibre laminates achieve improvements of ~9% and ~17% in buckling resistance load and failure load, findings that fit well with numerical predictions.

Implementation of Variable Blade Inertia in OpenFAST to Integrate a Flywheel System in the Rotor of a Wind Turbine

In this paper, the integration of the dynamic behavior of the flywheel system into the load simulation tool OpenFAST is presented. The flywheel system enables a wind turbine to vary the inertia of its rotor blades to control the power production and, most importantly, to affect the vibratory behavior of wind turbine components. Consequently, in order to simulate the behavior of a wind turbine with a flywheel system in its rotor, the variable blade characteristics need to be considered in the load simulation tool. Currently, computer-aided engineering tools for simulating the mechanical loads of wind turbines are not designed to simulate variable blade inertia. Hence, the goal of this paper is to explain how variable inertias of rotor blades are implanted in such load simulation tools as OpenFAST. OpenFAST is used because of it is free, publicly available, and well documentation. Moreover, OpenFAST is open source, which allows modifications in its source code. This add-on in the load simulation is applied to correct rotor mass imbalance. It can also be applied in many cases related to the change in the inertia of wind turbine rotor blades during its operation as, for example, atmospheric ice accretion on the blades, smart blades, etc.

Evaluating the benefits of a computer-aided software engineering tool to develop and document product configuration systems

Computer-Aided Software Engineering (CASE) tools are popular software programs to support the members of the development team (including analysts, designers, coders, database administrators, and project managers) in building new software systems. Up-to-date and consistent knowledge representation and documentation is crucial for companies developing Product Configuration Systems (PCSs). The literature reports various challenges in PCS development, such as maintenance, documentation, knowledge management, resource and time management, system quality, and communication with domain experts as particularly problematic. A CASE tool tailored to the specific needs of PCS development can prove to be useful in tackling at least some of these challenges. Such a CASE tool has to support product models, which means it has to not only allow the representation of the product core architecture and the optional selectable features, but also ensure consistency between representations (views) and deliver forward or reverse engineering. This enables support and automates, at least partially, the development in general and the implementation stage. The focus and main contribution of this paper is twofold. First, we describe the view-based approach required to fully conceptualise the knowledge to generate PCS software from the CASE tool. To this end, the tool indeed includes four different views to build or edit all the required knowledge. Second, we validate this CASE tool within two case companies, wherein we evaluate its application on a project each time it is used. The results show that the use of the CASE tool increases the quality of PCS documentation and saves time and resources while also improving the PCS’s overall quality.

Design for Additive Manufacturing: Tool Review and a Case Study

This paper aims to collect in a structured manner different computer-aided engineering (CAE) tools especially developed for additive manufacturing (AM) that maximize the capabilities of this technology regarding product development. The flexibility of the AM process allows the manufacture of highly complex shapes that are not possible to produce by any other existing technology. This fact enables the use of some existing design tools like topology optimization that has already existed for decades and is used in limited cases, together with other novel developments like lattice design tools. These two technologies or design approaches demand a highly flexible manufacturing system to be applied and could not be used before, due to the conventional industrial process limitations. In this paper, these technologies will be described and combined together with other generic or specific design tools, introducing the study case of an additive manufactured mechanical design of a bicycle stem.

Environmental and Exergetic Analysis of Large-Scale Production of Citric Acid-Coated Magnetite Nanoparticles via Computer-Aided Process Engineering Tools.

Considering that functional magnetite (Fe3O4) nanoparticles with exceptional physicochemical properties can be highly applicable in different fields, scaling-up strategies are becoming important for their large-scale production. This study reports simulations of scaled-up production of citric acid-coated magnetite nanoparticles (Fe3O4-cit), aiming to evaluate the potential environmental impacts (PEIs) and the exergetic efficiency. The simulations were performed using the waste reduction algorithm and the Aspen Plus software. PEI and energy/exergy performance are calculated and quantified. The inlet and outlet streams are estimated by expanding the mass and energy flow, setting operating parameters of processing units, and defining a thermodynamic model for properties estimation. The high environmental performance of the production process is attributed to the low outlet rate of PEI compared to the inlet rate. The product streams generate low PEI contribution (−3.2 × 103 PEI/y) because of the generation of environmentally friendlier substances. The highest results in human toxicity potential (3.2 × 103 PEI/y), terrestrial toxicity potential (3.2 × 103 PEI/y), and photochemical oxidation potential (2.6 × 104 PEI/y) are attributed to the ethanol within the waste streams. The energy source contribution is considerably low with 27 PEI/y in the acidification potential ascribed to the elevated levels of hydrogen ions into the atmosphere. The global exergy of 1.38% is attributed to the high irreversibilities (1.7 × 105 MJ/h) in the separation stage, especially, to the centrifuge CF-2 (5.07%). The sensitivity analysis establishes that the global exergy efficiency increases when the performance of the centrifuge CF-2 is improved, suggesting to address enhancements toward low disposal of ethanol in the wastewater.

Improving the engineering design process by simulating iteration impact with ASM2.0

Engineering design processes that are dependent on Computer-Aided Engineering (CAE) tools commonly involve complex patterns of tasks, including concurrency and intertwined iterations. There are often inefficiencies and improvement opportunities in such processes, but it can be difficult to identify and evaluate them—partly because of the complex iteration patterns, and partly because process knowledge is usually tacit and often distributed among process participants so that it is difficult to appreciate the causes and effects of iteration in an integrated way. The Applied Signposting Model (ASM) was developed to address these issues during more than a decade of research and case studies in the aerospace sector. This article describes an evolved version of the ASM, called ASM2.0, alongside a detailed case to demonstrate its application to improve CAE-driven, iteration-intensive engineering design processes. A review of applications is also provided. A differentiating feature of ASM2.0 is that it integrates visually-familiar flowchart mapping with quantitative analysis of process performance, to enable the modelling of complex iteration patterns that are typical of engineering design practice.

A Comparative Study of Computer-Aided Engineering Techniques for Robot Arm Applications

As systems become increasingly complex, their simulation techniques have to be more accurate and enhanced. Despite the wide use of robotic arms in industries, they still encompass a wide number of complexities. The control of a flexible robot arm driven by a Brushless DC Motor (BDCM) for tracking problems is a great challenge, not only for its complex algorithms but also for its verification process. Robotic systems are designed heterogeneously by combining continuous and event discrete models. Therefore, computer-aided engineering tools have to be enhanced in order to support the verification of the control algorithms. Ensuring definite and rapid simulations is a challenging task for robotic application systems. Although control strategies can be tested using the Matlab/Simulink environment to assess their performance, their verification at a low-level still remains a very difficult task. This paper studies different simulation techniques based on Model In the Loop (MIL), Hardware In the Loop (HIL), and Hardware Software In the Loop (HSIL) on a flexible robot arm driven by BDCM in order to overcome each method's limits, focusing on performance and cost and simulation time reduction. The HSIL achieves the highest accuracy and speed than MIL and HIL and provides reusability and portability of the control unit compared to the other techniques.

Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models

The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO2, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns.

Behavior study of prestressed concrete wind-turbine tower in circular cross-section

In this work, we sought to study the behavior of prestressed concrete wind-turbine tower in circular cross-section. The idea was to use Genetic Algorithm to obtain a structural optimization of the projected tower. Thus, a tower with the shape of a conical section was obtained, which minimizes the appearance of stress concentrations in the structure. The tower has a tapered profile to reduce the area subjected to wind thus lower the total weight and applied moment. It will also enhance the dynamic response of the tower and improve its overall stability. Steel tubular wind turbine towers are most widely utilized for wind turbines, however, the use of concrete material is becoming more attractive for wind towers because concrete towers are more stable for buckling failure. As the turbine size is growing and the towers are rising in height, steel towers are required to be sufficiently strong and stiff, consequently leading to high construction costs. Therefore, the prestressed concrete wind-turbine tower has a reduced construction cost compared to the steel tubular wind turbine towers or the self-supporting steel truss towers with a maximum height of 150 m. For this, Computer-Aided Engineering (CAE) tools were used and a 100 m prestressed concrete tower system for a wind turbine was optimized. It is concluded that the model met all the restrictions described and represents an economy in raw material and greater simplicity of design in relation to the octagonal model used for comparison. A new structural methodology was described for the study and application of prestressed concrete to replace the steel towers currently used.

Numerical study of plasma-fluid characteristics and thrust performance of a low-power argon inductively coupled plasma electrothermal thruster

Low-power (from the sub-kilowatt range up to a few kilowatts) inductively coupled plasma (ICP) electrothermal thrusters for space propulsion are potential alternatives to low-power arcjet thrusters, which are often implemented on geostationary satellites for north–south station keeping. We develop an axisymmetric two-dimensional magnetohydrodynamic (MHD) numerical simulation technique using a two-temperature plasma model as a computer-aided engineering tool for low-power argon ICP electrothermal thrusters. Furthermore, to the best of our knowledge, this is the first study to provide a basic understanding of the plasma-fluid characteristics of low-power ICP electrothermal thrusters. To this end, we perform the MHD numerical simulation for a low-power argon ICP electrothermal thruster model that was developed for thrust measurement experiments to validate the proposed numerical simulation technique. The numerical results indicate that the plasma flow produced in the low-power argon ICP electrothermal thruster model is basically in a strongly thermal and ionizing nonequililbrium state. In addition, the numerical results show that the experimentally measured thrust forces can be reproduced with an underestimation of 10% or less over the RF input power range of 0–0.6 kW considered in the experiment. Moreover, the numerical results suggest that the main reason why the performance of the low-power argon ICP electrothermal thruster model is significantly lower than the target performance is the considerable heat loss of more than 90% of the input power through the flow-channel wall.

Predicting Transdermal Fentanyl Delivery Using Mechanistic Simulations for Tailored Therapy.

Transdermal drug delivery is a key technology for administering drugs. However, most devices are “one-size-fits-all”, even though drug diffusion through the skin varies significantly from person-to-person. For next-generation devices, personalization for optimal drug release would benefit from an augmented insight into the drug release and percutaneous uptake kinetics. Our objective was to quantify the changes in transdermal fentanyl uptake with regards to the patient’s age and the anatomical location where the patch was placed. We also explored to which extent the drug flux from the patch could be altered by miniaturizing the contact surface area of the patch reservoir with the skin. To this end, we used validated mechanistic modeling of fentanyl diffusion, storage, and partitioning in the epidermis to quantify drug release from the patch and the uptake within the skin. A superior spatiotemporal resolution compared to experimental methods enabled in-silico identification of peak concentrations and fluxes, and the amount of stored drug and bioavailability. The patients’ drug uptake showed a 36% difference between different anatomical locations after 72 h, but there was a strong interpatient variability. With aging, the drug uptake from the transdermal patch became slower and less potent. A 70-year-old patient received 26% less drug over the 72-h application period, compared to an 18-year-old patient. Additionally, a novel concept of using micron-sized drug reservoirs was explored in silico. These reservoirs induced a much higher local flux (µg cm-2 h-1) than conventional patches. Up to a 200-fold increase in the drug flux was obtained from these small reservoirs. This effect was mainly caused by transverse diffusion in the stratum corneum, which is not relevant for much larger conventional patches. These micron-sized drug reservoirs open new ways to individualize reservoir design and thus transdermal therapy. Such computer-aided engineering tools also have great potential for in-silico design and precise control of drug delivery systems. Here, the validated mechanistic models can serve as a key building block for developing digital twins for transdermal drug delivery systems.

Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing

Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development.

A support approach for the modular design of Li-ion batteries: A test case with PCM

ABSTRACT Nowadays, there is an increasing interest in Phase-Change Materials due to their capacity to store latent heat during the phase transition. In the literature, the solid-liquid change is widely applied in cooling applications such as the thermal management of Li-ion batteries. The optimization of the cooling system is one of the solutions to reduce battery aging and increase the battery lifetime. Even if Phase-Change Materials are studied to be used in this context, there is still a lack of suitable solutions to be used in engineering design for a further product commercialization. In this context, the paper proposes a modular approach to support the design of a battery pack considering Phase-Change Materials in the cooling system. The approach shows how a single module of cells can be stacked and how the cooling system of each module can be easily connected to each other. The modularity concept is extended from the electrical configuration to the cooling system. As a test case, a battery module has been analyzed. The battery module has been simulated using a Computer-Aided Engineering tool to analyze the behavior of the temperature profile under loading conditions.

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling.

In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.

ВИКОРИСТАННЯ ІНСТРУМЕНТІВ MESHING TOOLBOX ДЛЯ ПІДГОТОВКИ І СТВОРЕННЯ СКІНЧЕННО-ЕЛЕМЕНТНИХ МОДЕЛЕЙ

Geometry preparation and construction of high-quality finite element mesh is one of the key steps of analysis using computer aided engineering tools. Therefore, rational application of tools used for creation of finite element models is useful skill that determines the efficiency and productivity of numerical analysis and also effects the accuracy and time required for getting the result.The capabilities of the Meshing Toolbox of FEMAP v. 10.2 finite element software, which are used for preparation and creation of finite element models of the objects with complex geometry, are described in the paper. The goal of the work is to build high-quality, mapped meshes for objects with complex geometry and reduce the dimension of the finite element model. Methods for improving the quality of the mesh in the local areas of a model with complex geometry and reducing the dimension of a model are considered using several practical examples. A useful and practical feature of the tools is that automatic remeshing is provided in case of changing the basic geometry. The effective application of tools for preparation and creation of finite element models allows you to build mapped hexahedral meshes and thereby significantly reduce the dimension of finite element models, increasing their computational efficiency. Depending on the degree of complexity of the model, the number of elements in the model decreased by several times, and the number of nodes — on average, by 20%. The issue of applying the described approach and tools is especially concern for large models and in case of limited computational resources.

Numerical analysis of a new multi-body floating wave energy converter with a linear power take-off system

Abstract A floating wave energy converter (FWEC) with central buoy (CB), circumferentially connected by a set of four, smoothly-finished tubular floats is proposed. The central buoy and floats are connected rigidly at one end while the other end is hinged, enabling a relative motion. The rotation of floats about the CB represents the energy extraction mode of the device. Float shapes of five different configurations, including a newly-proposed ‘bean float,’ are investigated to estimate their power outputs with linear PTO. FWEC remains afloat and re-centered using taut-moored tethers. The frequency-domain model is utilized to determine the hydrodynamic coefficients of the device, assuming a Potential Wave Theory (PWT). The research evaluates the impact of the obtained hydrodynamic coefficients on the average power absorbed by the FWEC. The influence of a linear power take-off (PTO) system on the average power output of FWEC is studied by optimization of the damping coefficients for the encountered regular waves. Further, time-domain analysis is carried out on the ‘bean float’ - FWEC configuration using an open-source computer-aided engineering tool called WEC-Sim (Wave Energy Converter Simulator), and MATLAB® - Simulink® implementing nonlinear buoyancy and Froude-Krylov forces.

Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.

Construction of specialized Computer Aided Software Engineering (CASE) tools for the development of expert systems

This report presents an approach to building specialized computer-aided software engineering (CASE) tools for the development of expert systems. These tools form an integrated development environment allowing the computer aided development of different applications in the appropriate field. The integrated environment which we consider in our report represents the combination of SWI-PROLOG and Data Base Management System (DBMS) PostgreSQL tools. SWI-PROLOG provides the most appropriate tools for the solution of logical tasks in expert systems. However, SWI-PROLOG cannot manage large amounts of data. Therefore, we need to apply an appropriate data base management system to extend the capability of the knowledge base. For this purpose we used the most advanced open source PostgreSQL tools. As a result of our research we have created tools enabling the compatibility of SWI-PROLOG and DBMS PostgreSQL within the integrated development environment.

Assessment of Automobile Door Slam using Finite Element Method

Automotive cars have various types of doors. The swinging door is the most common & complicated parts because they are doing both functions, general guidelines of car style, & passenger's safety by protecting humans from side crashes. With the advent of Computer Aided Engineering (CAE), Finite Element Analysis (FEA) has become a necessity for the automotive industry to improve and validate all manner of automotive structures. The use of FEA in the design procedure has increased significantly making validation of the FE Models used is essential. A comparison with experimental results of Door slam testing is a very effective method to evaluate the accuracy of the FE Models used. The objective of the door slam testing is to determine acceleration, stress, strains & buckling energy induced while slamming action. In this door slam analysis carried out using CAE tools, Hypermesh-v12, Ls-Dyna. Door is slammed on rigid surface at velocity 1m/sec from opening angle 20 degree and results are evaluated for 0.35 seconds in the time step of 0.01second.Then predictability of the CAE method is examined through detailed comparison of experimental acceleration and strain results. While these results shows excellent agreement in CAE and test for accelerations on the outer panel. Also CAE predicts higher strains on the inner panel than the test. In addition, elastically buckling of outer panel is examined. These results of Acceleration, strain and buckling are also discussed in detail.