PDSIDES: A Knowledge-Based Platform for Decision Support in the Design of Engineering Systems

The design of complex engineering systems often relies on simulation-based models, which inherently introduce uncertainty. Consequently, effective system design necessitates the incorporation of robust design principles and metrics to manage the uncertainties. From a decision-based design perspective, metrics such as the Design Capability Index (DCI) and Error Margin Index (EMI) are employed in problem formulations to address this challenge. These metrics typically utilize Taylor series-based approximations to estimate variance. However, due to the structural limitations of the Taylor series expansion, these formulations may mislead designers toward local minima rather than guide them toward actual robust solutions. In this paper, we propose an alternative formulation of the Design Capability Index (DCI) and Error Margin Index (EMI) that leverages the multiple-point variance estimation method. This approach employs a weighted average of multiple points to assess variance estimations around the nominal design, ensuring that the solution is not confined to a local optimum by estimating the correct variance at the local optimum. The proposed alternative metrics are incorporated into the robust Concept Exploration Framework (rCEF) to find robust solutions for complex engineering systems. A machine learning-based visualization tool, the interpretable Self-Organizing Maps (iSOM), is used to visualize and explore the high-dimensional robust solution space generated using the updated rCEF. Two examples are used to evaluate the proposed formulation: (i) a polynomial function to examine the effect of the alternative variance estimation method on EMI and DCI metrics, and (ii) a comprehensive hot rod rolling problem, considering model parameters and model structure uncertainty.

Strategic Environmental Assessment (SEA) Process towards the Sustainable Design and Construction of Computer, Communication, Network Engineering, Machine Learning and Artificial Intelligence Systems

Integrating Core Systems Engineering Design Concepts into Traditional Engineering DisciplinesIn this study we describe the integration of core systems engineering design concepts intotraditional mechanical and electrical engineering curriculum at the University of Arkansas atLittle Rock (UALR) undergraduate Systems Engineering program. The program was launched in1999 in response to the industry demands for engineering graduates capable of managingcomplex engineering projects. The program has since graduated over 150 students who areworking in industry (aerospace, defense, manufacturing, design, and consulting), federal andstate government, or attending graduate school. The program curriculum has four broadcomponents: a 35 hour university core that covers basic math and science (11 hours) and generaleducation (24 hours); a 30 hour college core that enhances the math and science preparation witha course in engineering ethics; a 32 systems engineering component that introduces core systemsengineering concepts; and, a 32 hour option component that teaches the mechanical or electricalengineering disciplines. The program is accredited by ABET under general criteria, and plans areunderway to satisfy mechanical and electrical engineering program criteria. Students entering theprogram are introduced to systems engineering core concepts through courses in EngineeringEconomy, DES Modeling and Simulation, Optimization Methods, Decision and Risk Analysis,and Systems Engineering Design and Analysis. Among them, Engineering Economy coversengineering project evaluation and economic decision making. Optimization Methods in SystemsEngineering covers linear and nonlinear methods of engineering design optimization. DESModeling and Simulation covers theory and practice of discrete event simulation modeling.Decision and Risk Analysis covers uncertainty modeling and analytical techniques for rationaldecision making. Systems Engineering Design and Analysis covers the systems engineering life-cycle process, including identification of system requirements, engineering design andarchitecture, system modeling, testing and integration, and system operation and support. The128-hr program culminates in a two-semester Systems Engineering Capstone Design thatemphasizes the multi-disciplinary nature of the program. The students perform functional designin the first semester followed by detailed physical design and testing in the second semester. TheUALR program graduates skilled systems engineers who combine holistic thinking with deepproduct knowledge, strong technical background, effective communication skills, and goodinterpersonal skills.

The management of software development for single and dual processor system designs is making progress towards becoming a mature discipline. A good part of the progress can be attributed to the development and use of standard system and software engineering methods and design principles.However, new computer system designs (networking, distributed systems, embedded systems, multi- and coprocessors, fault tolerant systems, etc.) will create new challenges for managers of software development. The reason for this effect on management is that some of the system and software engineering methods and design principles developed for single and dual processor system designs are not valid for these newer designs.Some of the issues that software development project managers will need to cope with are:1. Life cycle model adjustments2. Rapid prototyping activities3. Different hardware and software phasing4. Increased tool development5. New trade-offs and hybrid developments of off-the-shelf software and newly developed software6. Development of concurrent design principles7. New software design principles to support fault tolerance and the use of new memory technologiesThe above items are just now being recognized as problems, and solutions for them either do not exist, or are not widely known.These problems create a series of new challenges that managers must deal with for software development based on the new architectures and requirements. The purposes of this paper are to discuss these issues and to identify some solutions that can serve in the interim as the technology changes to meet these new challenges.

Risk is constantly present in the engineering design of systems. It influences all lifecycle phases of every system and manifests itself through a system's cost, schedule, and performance attributes. These attributes are expressed through a system's design requirements and therefore provide a measure of how successful a system is. Design requirements are stated by means of a design threshold and a design goal. The distance between the threshold and the goal, known as the design space, provides not only a measure of system performance, but also an understanding of system risk and risk management. Risk management is the sum of activities undertaken to understand, identify, classify, measure, and mitigate risk in the engineering design of systems. This paper presents an output‐oriented view of risk and risk management. It provides a taxonomy of risks that exploits the relationship between risk and performance and then discusses several hedging strategies that serve to manage risk in the engineering design of systems.

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

A simplified method for reliability- and integrity-based design of engineering systems and its application to offshore mooring systems

This paper develops an alternative optimization approach for systematic design of a parametrized engine system and illustrates the procedure through application to a novel conversion device, the patented Stiller-Smith mechanism (1, 2). Using simultaneous interplay between a simulation scheme, presented in detail elsewhere (3), and an optimization scheme, the proposed structural design process integrates the multiple objectives of structural design. Developed here in detail, optimization involves intermediate continuous optimization via a penalty function method, and integer or discrete programming through the branch-and-bound algorithm. The ensuing application illustrates the approach by optimizing a 16-cylinder Stiller-Smith engine for minimum weight-power and dimensions-power ratios under several types of constraints. In the context of a multi-objective constrained non-linear programming problem, the design example proceeds through three stages: (a) preliminary, in which the designer applies the simulation scheme to obtain the system response variables from the design requirements to reflect trade-off relationships among multiple design objectives; (b) secondary, the intermediate continuous optimal design stage, in which a penalty function method is used to specify constraints within a general range to allow variation in the choice of parameters; and (c) final, in which the branch-and-bound algorithm constrains integer variables to take integer values and discrete variables to take discrete values, thereby arriving at an optimal design.

The finite element method (FEM) is established as one of the most popular and powerful tools in analysis and design of engineering systems. However, the method can only be used at an advanced (or detail) design stage when a designer has developed sufficient confidence in the design scheme. In the early (or conceptual/ preliminary) design phase, the method can not be used directly due to the existence of uncertainties of various types. In this work, a procedure is proposed for the FEM to handle engineering systems in the presence of hybrid uncertainties that are characterized by randomness (or stochastic uncertainty) and fuzziness (or design imprecision). The proposed scheme utilizes the techniques of stochastic and fuzzy FEMs in a unified manner to investigate the effects of both input random and fuzzy uncertainties on the output system response. Interpretations of the generated solution space and guidelines in selection of an appropriate solution, according to specific design requirements, are discussed. Two numerical examples are presented to illustrate the computational aspects of the proposed scheme. The current procedure is expected to enhance the understanding of designers regarding the effects of uncertainties in an evolutionary design process, especially in the conceptual or preliminary design phase.

Chapter 1 - Introduction

Integration of System Modeling and Design Processes, Data and Technology for Streamlining Enterprise Integration

The draft ASME Plant Systems Design standard integrates systems engineering, risk evaluation, and probabilistic design practices into traditional plant engineering processes. This standard provides a framework through which plant owners, designers, and other stakeholders may execute projects in a manner that minimizes errors and omissions in the design process and produces optimal cost and schedule outcomes for its users. Integration of classic systems engineering processes into plant design provides a uniform framework for communicating design requirements, design information, and acceptance criteria throughout the lifecycle of a plant design project. Traditional plant design processes emphasize the use of separate engineering disciplines, which often create information silos that cause errors and omissions in design. The use of a systems engineering process ensures that the plant, its systems, and its infrastructure are designed holistically and consider all requirements for productivity, safety, and the plant lifecycle. This paper provides an introduction and overview of the Systems Engineering and Design Integration (SEDI) portion of the new standard that is targeted for publication in 2025.

Traditional system engineering has been the source of most of the requirements' decomposition and management techniques. Requirements' decom‐ position and flow down concentrate on a top‐down mission oriented view. The emphasis is on functional flow (input/output), black box performance allocation, and system interaction. However, it is weak in areas like operating logic, behavior, and timeline requirements.We contrast and compare the requirements' process and management techniques for the traditional system engineering and modern software approaches. We can indicate at least one gap in the area of operating logic and timeline analysis as well as numerous important under laps.Consequently, the design of many computer‐ based systems suffers from a substantial risk at the interface between system and software design engineering. The gap, created by a failure to address operational logic, sequencing, performance and timeline requirements in both disciplines, can have a major impact on program cost, schedule, and performance. Potentially, major safety related risks may result from the gap in logic and timeline requirements' tracking and allocation. In addition, significant under laps occur in operational analysis, interface control, performance, human factors, risk analysis, trade‐off studies, reviews, and planning. Therefore, these can create a risk if the software engineering effort is weak in traditional system engineering discipline. Suggestions for reducing the gap and under lap are offered.

This study deals with the engineering system idea.The relevance of this work derives how it is carried through all the stages of the system life cycle.This is achieved through the timely identification of weak chains in the organization.In the course of assessing the amount of losses it the necessary give a detail description of the life cycle phases and solve the tasks involved in each phase.The work used methodology of engineering systems, such as the high-level organization of the overall building and release of complex systems.

ABSTRACTThis paper focuses on the engineering design of modular systems in the manufacturing industry, in which companies can benefit from modular and configurable products that fulfil a variety of customer needs. The purpose of this paper is to study the types of challenges faced in the engineering design of modular systems and the types of support needed to address these challenges. The topic is studied via a literature review and seven industrial case studies. The analysis method for the case studies combines engineering and business perspectives. The engineering perspective highlights engineering concepts related to designing modular systems, while the business perspective focuses on related managerial issues. We find that the studied companies emphasised different aspects of modular system development, despite having similar high-level goals related to the benefits of modularity and design reuse. We also identify several challenges, along with the need for support related to the engineering design of modular systems in the manufacturing industry. Current engineering design practices related to modular systems are not sufficient to generate the typical benefits related to modularity and design reuse; organisations must also focus on other aspects of the design process, such as knowledge retrieval.