Engineering Analysis Models Research Articles

Development of an improved three-dimensional rough discrete fracture network model: Method and application

Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering. Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation. A new 3-dimensional rough discrete fracture network (RDFN3D) model and its modeling method based on the Weierstrass-Mandelbrot (W-M) function were presented in this paper. The RDFN3D model, which improves and unifies the modelling methods for the complex structural planes, has been realized. The influence of fractal dimension, amplitude, and surface precision on the modeling parameters of RDFN3D was discussed. The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed, and the relationship between the mathematical model and the joint characterization was established. The RDFN3D together with the smooth 3-dimensional discrete fracture network (DFN3D) models were successfully exported to the drawing exchange format, which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods. The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code, respectively. The reliability of the RDFN3D model was preliminarily discussed and analyzed. The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns. The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.

Analysis Model for Damage of PBO Composite Laminate Target by High-Velocity Projectile Penetration

Poly-p-phenylene benzobisoxazole (PBO) fiber is a new fiber with high ballistic performance, such as excellent heat and flame resistance and super mechanical properties. Consequently, increasing attention has been paid to its applications in the field of ballistic resistance. To identify the damage mechanism of PBO composite target caused by the high-velocity penetration of cylindrical projectiles and the energy conversion mode involved in the penetration process, this paper constructs a target damage engineering analysis model using the energy method in combination with stress wave propagation and projectile motion analysis. The residual velocity of the steel cylindrical projectile after perforating a PBO composite laminate is measured through ballistic tests, and it is compared with the value predicted by the analysis model. Comparison results show that the predicted value by the model is in good agreement with the measurement, with an error of less than 2.6%, thereby proving the validity of the constructed analysis model.

Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study

Abstract Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.

Design and Analysis of Foot Orthotic For Lower Limb Deformities Using Composites

This research details a procedure for the systematic design of foot orthotics based on simulation models and validation of the same with subsequent experiment. The present model may ultimately to replace the use of empirical tables for the designing of foot orthotics in accordance to the weight and activities of end-users. This present model is so general that it can simulate different kind of orthotics, loading conditions and material properties to give the desired output. Finally, the proposed model and the corresponding results may lead to the form of design basis for new generation foot orthotics. The methodical approach to the development of engineering analysis models using the FEA technique. Orthotic devices have been commonly made from plastics like polypropylene. However, insufficient mechanical properties, labour intensive preparation process, long client’s visiting times urge us to seek an alternative. Presently, two different advanced composite materials (Glassfiber, Basalt-fiber) have been chosen for the analysis purpose to achieve the low weight and High Strength.

A multiscale cohesive law for carbon fiber networks

Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. Here, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.

Data-driven smart control of injection molding process

The most widely used cyclic manufacturing process to produce plastic components, injection molding produces large quantities of scrap materials and consumes resources to mitigate the increasing demands for quality products and sophisticated shapes. Such inefficiencies require a smart quality-control system that can bring consistency in the product quality. Continuous monitoring and control of the injection molding are of excessive importance in providing a better understanding of the occurrence of the failures and their correction measures. In the presented paper, a cyber engineering analysis model is developed to illustrate the real injection molding behavior, and multiple experiments are performed to derive the process window for the product with very good quality. To detect the process disturbances, a control boundary with threshold limits of cavity temperature and pressure is established. If-then rule based control algorithm is derived to detect the failures and make the compensatory adjustments to the process parameters. Finally, the proposed method is illustrated with the manufacturing of an automotive product i.e. car door trim module, and its validity is verified. Study results after the introduction of the developed quality control methodology indicate the dramatic reduction in the quality failure rates and the wastages of resources.

A two-dimensional Jensen model with a Gaussian-shaped velocity deficit

The one-dimensional (1D) Jensen model is probably the most often used model for engineering analysis of wind turbine wakes. Identifying a more realistic shape function for the near and far wakes behind a wind turbine and incorporating the identified shape function into a wake model can significantly improve the accuracy of wake modelling. The conventional approach is to first solve the 1D Jensen model and subsequently redistribute the wake using a specified shape function. The above procedure conserves mass globally and is useful in wake modelling. However, it needs to solve a top-hat wake using Jensen model first, which inevitably violates the local mass conservation. In this work, we propose a two-dimensional (2D) wake model that conserves mass locally and globally. The model is a direct extension of Jensen model, and the wake decay rate is the only model parameter. In addition, by accounting for the pressure recovery region, which is often neglected in wake models, the present model can provide accurate prediction of the velocity deficit behind a wind turbine. The present model is compared with the high-fidelity simulations, wind-tunnel measurements, and field observations. A reasonably good agreement is found between the model and the validation data.

Investigating relationships between cost and CO2 emissions in reinforced concrete structures using a BIM-based design optimisation approach

An integrated design approach for the cost and embodied carbon optimisation of reinforced concrete structures is presented in this paper to inform early design decisions. A BIM-based optimisation approach that utilises Finite Element Modelling (FEM) and a multi-objective genetic algorithm with constructability constraints is established for that purpose. A multilevel engineering analysis model is developed to perform structural layout optimisation, slab and columns sizing optimisation, and slab and columns reinforcement optimisation. The overall approach is validated using real buildings and the relationships between cost and carbon optimum solutions are explored. The study exhibits how cost effective and carbon efficient solutions could be obtained without compromising the feasibility of the optimised designs. Results demonstrate that the structural layout and the slab thickness are amongst the most important design optimisation parameters. Finally, the overall analysis suggests that the building form can influence the relationships between cost and carbon for the different structural components.

Project-Based Curriculum for Teaching Analytical Design to Freshman Engineering Students via Reconfigurable Trebuchets

This paper presents an effort to revitalize a large introductory engineering course for incoming freshman students that teaches them analytical design through a project-based curriculum. This course was completely transformed from a seminar-based to a project-based course that integrates hands-on experimentation with analytical work. The project is centered on a reconfigurable trebuchet kit that student groups assemble and work to identify design decisions that will maximize projectile launch distance. Challenges include streamlining the project experience for the large enrollment (up to 148 students) with limited contact hours, and helping students fuse hands-on experiences with quantitative engineering analysis. A mixed-methods approach supported the claim that the curriculum improved the students’ engineering judgment and demonstrated to students the value of engineering analysis and mathematical models in practical engineering design. A rigorous statistical analysis of student trebuchet launch performance at different course stages is included. A qualitative assessment of student learning is derived through students’ reflection of their course experience. Comprehensive results comparing students’ design iterations versus algorithmic design optimization iterations provide important insights into student design intuition, paving the way for hybrid design education models that teach students how to combine human design intuition with quantitative design tools to design superior systems.

Exact-to-precision generalized perturbation theory for neutron transport calculation

This manuscript extends the exact-to-precision generalized perturbation theory (EPGPT), introduced previously, to neutron transport calculation whereby previous developments focused on neutron diffusion calculation only. The EPGPT collectively denotes new developments in generalized perturbation theory (GPT) that place premium on computational efficiency and defendable accuracy in order to render GPT a standard analysis tool in routine design and safety reactor calculations. EPGPT constructs a surrogate model with quantifiable accuracy that can replace the original neutron transport model for subsequent engineering analysis. This is achieved by reducing the effective dimensionality of the state variable (i.e. neutron angular flux) via projection onto an active subspace determined using reduced order modeling techniques. Confining the state variations to the active subspace allows one to recast the problem in terms of a small number of ‘pseudo’ responses which are solely dependent on the physics model rather than on the nominal number of responses, the input parameters, or the number of points in the state phase space. This renders a computationally efficient construction of the surrogate whose form is determined using a recursive relationship from the solution of the transport equation. Unlike conventional surrogate techniques, the EPGPT can upper-bound the errors resulting from its predictions with high probability, which can be preset by the user.

Multidisciplinary design modeling and optimizationfor satellite with maneuver capability

Abstract According to the mission of a satellite with maneuver capability, the collaborative optimization (CO) method was introduced for the satellite system design, and the related multidisciplinary design optimization (MDO) model was established. The possessing and needed velocity increments Δv and Δv n e e d were taken as the measurement of maneuvering capability of the studied satellite, which were then combined with total mass of the satellite to form the optimization objective in the systematic level of the MDO problem. The design variables and constraints of the MDO problem dealt with disciplines or subsystems as guidance, navigation and control (GNC), power, and structure, and corresponding engineering analysis models were also built. A program system to solve the MDO problem wasdeveloped by integrating a non-nested CO method, the commercial and user-supplied codes on framework software iSIGHT. The result showed that the satellite performance could be obviously improved, which also indicates MDO technique is feasible and effective for the spacecraft design problem. The modeling and optimization procedure of the work can be referred for further research and engineering design.

Sensitivity measures for optimal mitigation of risk and reduction of model uncertainty

This paper presents a new set of reliability sensitivity measures. The purpose is to identify the optimal manner in which to mitigate risk to civil infrastructure, and reduce model uncertainty in order to improve risk estimates. Three measures are presented. One identifies the infrastructure components that should be prioritized for retrofit. Another measure identifies the infrastructure that should be prioritized for more refined modeling. The third measure identifies the models that should be prioritized in research to improve models, for example by gathering new data. The developments are presented in the context of a region with 622 buildings that are subjected to seismicity from several sources. A comprehensive seismic risk analysis of this region is conducted, with over 300 random variables, 30 model types, and 4000 model instances. All models are probabilistic and emphasis is placed on the explicit characterization of epistemic uncertainty. For the considered region, the buildings that should first be retrofitted are found to be pre-code unreinforced masonry buildings. Conversely, concrete shear wall buildings rank highest on the list of buildings that should be subjected to more detailed modeling. The ground shaking intensity model for shallow crustal earthquakes and the concrete shear wall structural response model rank highest on the list of models that should be prioritized by research to improve engineering analysis models.

J102011 複合領域システムの最適設計における設計者の思考過程の分析とその記述的管理法(〔J102-01〕1DCAEを活用した設計と教育)

Design of a complicated system requires multi-disciplinary engineering analysis for the verification of its physical behavior. Although a CAE (Computer-Aided Engineering) system can support its calculation and visualization of the results, it is important to make appropriate models considering critical phenomena, modeling conditions, the level of details and so on. These processes of engineering analysis modeling remain with the realm of tacit knowledge of engineers. This paper analyses an engineer's thinking process in design of a heavy manufacturing system, and proposes descriptive management method in order to explicitly describe knowledge of engineering analysis modeling and its operation process in multi-disciplinary system design. This method employs EAMM (Engineering Analysis Modeling Matrix) using IDEFO (Integrated Definition for Function Modeling) as a knowledge representation framework of modeling of each system element. IBIS model is employed to represent bases of input etc of modeling of system. The ability of the method is verified with a design example of desiccant air conditioning system.

The Influence Study of League Shear Wall Seismic Capacity by the Coupling Wall-Beam Rigidity and Reinforcement Ratio

According to the important role of the league shear wall seismic capacity in the practical engineering, a brief and effective finite element static push-over analysis model was established through using the MIDAS GEN7.95. With the changes in the coupling beam rigidity and reinforcement ratio, the structural base shear, structural failure displacement change during the simulation. On the basis, the curve of the coupling wall-beam rigidity, reinforcement ratio vs. base shear and failure displacement were built respectively. The results show that the coupling wall beam stiffness, reinforcement ratio and league shear wall seismic capacity have a significant effect relationship, which provides an important reference for shear wall seismic design of structures.

Quantitative control of idealized analysis models of thin designs

When preparing a design model for engineering analysis, model idealization is often used, where defeaturing, and/or local dimension reduction of thin regions, are carried out. This simplifies the analysis, but quantitative estimates of the idealization error, the analysis error caused by this idealization, are necessary if the results are to be of practical use. The paper focuses on a posteriori estimation of such idealization error, via both a theoretical analysis and practical algorithms. Our approach can compute bounds for the errors induced by dimension reduction, defeaturing or both in combination. Performance of our error estimate is demonstrated using examples.

Default risk-based probabilistic decision model for risk management and control

This study describes how risk-based risk control allocation models work. We begin by discussing the economic rationale for allocating risk control in a diversified organization such as an enterprise. For a probability model for risk control decision making under uncertainty and risk, we propose a model involving stochastic total loss amount constraints with respect to various tolerable default levels. Our main objective is to develop a method that will allow shaping of the risk associated with risk control outcomes. The direct and indirect losses caused by simulated disasters can be estimated using an engineering and financial analysis model. Based on this model, we can generate an exceeding probability curve and then calculate how much of the loss can be eliminated or transferred to other entities should funds be allocated to risk control. The optimal natural disaster risk control arrangement with a probabilistic formulation is explained in this paper. Results from the proposed formulations are compared in case studies. The model attempts to apply risk-based budget guidelines to risk reduction measurement within a portfolio-based risk framework.

J112013 複合領域システム設計における解析モデリングとその操作プロセスの記述的管理法

J112013 複合領域システム設計における解析モデリングとその操作プロセスの記述的管理法

A Framework of Describing and Managing Engineering Analysis Modeling Knowledge for Design Validation

Engineering design is validated by engineering analysis. In the process for predicting the physical behavior of the design, the design components, physical mechanisms, etc. are idealized as models, the contents of which depend on a various factors such as the type of product, the validation objectives, and the design lead time as well as the design’s physical features. It is important to examine such modeling knowledge to enhance the rationality and reusability of engineering analysis. This article proposes a concise and comprehensive format for describing the knowledge of engineering analysis, called Engineering Analysis Modeling Matrix (EAMM). EAMM is a matrix of two axes, that is, a modeling aspect axis and a modeling level axis. The former axis consists of target objects, governing principles and behavior, and the latter axis consists of conceptual, mathematical and computational approaches. EAMM takes the form of a nine-cell matrix by combining the two modeling axes, each having the three sub concepts. Seven scaphoid boxes called operation spaces are arranged among the cell spaces. When a designer describes various elements of modeling knowledge in such a format, the association of the cells and boxes enables the designer to organize the modeling process and obtain its overview. The design of a water heater is used as an example to demonstrate the capability of EAMM for description and management of engineering analysis modeling knowledge.

2204 設計における工学解析知識の管理フレームワークに向けたオントロジー構築(OS10-1 設計における知識マネジメント・情報共有I,OS10 設計における知識マネジメント・情報共有)

This research proposes a framework for representing and managing the knowledge involved in engineering analysis modeling for design verification, and discusses formalization and building of knowledge management system. The computation-based design consists of the two phases, engineering analysis for understanding physical phenomena and design verification based on its results. The management format of the engineering analysis modeling process, which is named EAMM (Engineering Analysis Modeling Matrix), is introduced for the first phase. The process structure of design verification and classification of designer's decision making are discussed for the second phase by referring the framework of EAMM. gIBIS (graphical Issue-Based Information System) model, which is a hypertext graph model, is adopted for representing the process structure and the designer's decision makings with multimedia information. This paper also defines an ontology of the engineering analysis modeling knowledge, which is the fundamental of the implementation of the knowledge management framework. A design process of a micro mixing mechanism demonstrates the potential and the promise of a proposed framework.

1105 設計検証のための工学解析におけるモデリング知識の管理フレームワークの構想(OS10-2 設計における知識マネジメント・情報共有II,OS10 設計における知識マネジメント・情報共有)

Any engineering analysis model is indispensable to predict the behavior of a product for its rational design validation. In the modeling process of engineering analysis, an engineer idealizes physical phenomena and introduces assumptions, which depend on a various factors of design, such as the kinds or types of products, the objectives of the engineering analysis or design time constraints as well as physical features of the product. Because the knowledge of engineering analysis exists in modeling process, it is important to capture and manage the process for enhancement of rationality and reusability of engineering analysis models. This research aims to propose a management framework of engineering analysis modeling knowledge for design validation process. This paper introduces an outline of a framework, in which EAMM (Engineering Analysis Modeling Matrix) is used to represent a snapshot of model description, and IBIS (Issue-based Information System) is used to represent transition of model description and arguments behind it. Management of a design process of a micro mixing mechanism demonstrates the potential and promise of a proposed framework.