Large chemical plants are usually designed by combining several individually well controlled process units into a whole. This practice produces satisfactory, controlled response if the dynamic interactions are "weak". However, high material and energy costs and increased market competition have driven chemical plants towards a higher degree of energy integration and material recycle. These "modern" plants become difficult to operate and control because strong interconnections generally degrade the performance of the individual units, and in some cases the total response for the plant may be unstable. Our study focuses on modifying the plant design and improving control configurations to regain stability and to obtain the original output trajectories of the units as closely as possible. Via a sequential procedure, we represent the plant by a hierarchical set of self-similar modules, in which the top level represents the entire plant. The analysis of the synthesis procedures depends on a proposed measure of interaction, $\phi$, whose minimization produces the required guidelines for stable design and control.

<div>Design is complex and an important way to deal with the difficulties of design is to meet designer needs within the product development process. Many design methodologies are inapplicable and inappropriate in design practice because they were developed to be context-free universal processes. This thesis argues that design involves social and technical context. The social context influences the design process through social factors like negotiation. Negotiation will determine the outcome of major design decisions. Therefor the design process should guide designers in addressing social factors. The technical context influences design through how the design requirements are linked with the non-static environment in which design takes place. Therefor the design process should guide the designer in addressing the technical context as each tool or design methodology will determine the very character of the solution space. This thesis will identify the social and technical designer needs. This Thesis then builds on the core concepts of Collective System Design and Decision Drive Design to create a guideline for conceptual product design. This thesis then applies the guideline to develop a product to show that meeting designer needs will improve conceptual product development. </div><div><br></div>

Erroneous decisions made by pilots during encounters with adverse weather is often cited as a cause of General Aviation accidents. Pilot experience, which can be measured in several ways, is believed to play a role in the outcome of such encounters. However, it is unclear whether any of the elements of experience alone or in combinations affect the likelihood of General Aviation accidents during actual encounters with adverse weather, or how they do so. One barrier to conclusively determining such effects is the danger in extrapolating simulation results to the real world; nearly all work done to date has used simulators to identify accident risk. Therefore, the extent to which such results can be applied to actual flying is not clear.<div><br></div><div>In this work, two conceptual models for analyzing experience and its role inencounters with adverse weather during the cruise phase of General Aviation Part 91 fixed wing operations are presented. A novel method for evaluating accident risk, specifically the likelihood that an incident turns into an accident is also presented and then used to evaluate the experience profile of 595 pilots, detailed in actual accident and incident reports from the NTSB and ASRS databases. The effect of various elements of experience, alone and in combinations, on that risk is evaluated using regression modeling. The level of significance for each experience variable is first established, and then a series of discrete models is developed to progressively evaluate accident risk along a hypothetical experience continuum. This approach obviates commonly encountered challenges with research in the area and provides results that are ecologically valid.<br></div><div><br></div><div>The focus of this research work was on the role of cognitive aspects of experience in the outcome of flights during the cruise phase of General Aviation Part 91 fixed wing flights between January 1, 2005 and December 31, 2015. Only flights which encountered adverse weather during the cruise phase and for which experience and/or errors in decision making were determined to be a cause or factor in the outcome were included in the study. All flights during the period that involved takeoff and landing, equipment failure or student pilots were not considered for the study. The emphasis of the research was on the effect of experience on cognitive aspects of pilot performance during adverse weather encounters, rather than “stick and rudder” skills.<br></div><div><br></div><div>It was found that variables related to the breadth or variety of pilots’ experience are more predictive of the likelihood of adverse weather encounters turning into accidents compared to those related to the duration or length of experience. While several commonly used measures of experience provide some level of insulation against accidents, the relationship between elements that define the length or duration of experience and outcomes is not linear. Furthermore, this relationship is mediated by variables that define the breadth of experience, especially at their lower levels. These findings may be leveraged to design specifically targeted regulatory or training policies and interventions to expedite the transition from novice to expert pilots in General Aviation weather-related decision making.<br></div>

This thesis provides a bridge between analytical modeling and neural network modeling. Two different approaches have been explored. Both approaches rely on embedding radial basis function (RBF) modules in the approximate model of the plant so that they can be trained to compensate for modeling uncertainty. One approach has led to the development of a model-based recurrent neural network (MBRNN) for modeling nonlinear dynamic systems. The RBF modules take the form of activation functions in the MBRNN network, that is formu1ulated according to a linearized state-space model of the plant. This network is trained to represent the process nonlinearities through modifying the activation functions of its nodes, while keeping the original topology of the neural network intact. The performance of the MBRNN is demonstrated via several examples. The results indicate that it requires much shorter training than needed by ordinary recurrent networks. The utility of the MBRNN is tested in fault diagnosis of the IFAC Benchmark Problem and its performance is compared with ‘black box’ neural solutions. The results indicate that the MBRNN provides better results than ‘black box’ neural networks, and that with training it improves the results from other model-based residual generators. The second approach incorporates RBF modules in the nonlinear estimation model to enhance the performance of the extended Kalman filter (EKF) in coping with the uncertainty of this model. In this method, single-input single-output radial basis function (RBF) modules are embedded within the nonlinear estimation model to provide additional degrees of freedom for model adaptation. The weights of the embedded RBF modules are. adapted by the EKF concurrent with state estimation. This modeling compensation method is tested in application to an induction motor benchmark problem. Simulation results indicate that the RBF modules provide the means to model the uncertain components of the estimation model within their range of variation. The utility of the embedded RBF-based nonlinear adaptive observer was tested in fault diagnosis of a throttle sensor fault in an internal combustion engine. The test results show that this observer enhances the residuals obtained for fault diagnosis.

<p>Space habitats will involve a complex and tightly coupled combination of hardware, software, and humans, while operating in challenging environments that pose many risks, both known and unknown. It will not be possible to design habitats that are immune to failure, nor will it be possible to foresee all possible failures. Rather than aiming for designs where “failure is not an option”, habitats must be resilient to disruptions. We propose a control-theoretic approach to resilient design for space habitats based on the concept of safety controls from system safety engineering. We model disruptions using a state and trigger model, where the space habitat is in one of three distinct states at each time instance: nominal, hazardous, or accident. The habitat transitions from a nominal state to hazardous states via disruptions, and further to hazardous and accident states via triggers. We develop an approach for identifying safety controls that considers these disruptions, hazardous states, and identifies control principles and their possible control flaws. We use safety controls as ways of preventing a system from entering or remaining in a hazardous or accident state. We develop a safety control option space for the habitat, from which designers can select the set of safety controls that best meet resilience, performance, and other system goals. We show how our approach for identifying safety controls drives our control-theoretic approach for resilient design, and how that fits into the larger system safety engineering process. To identify and assess hazards, we use a database and create a network format that stores the relationships between different disruptions and hazardous states for an example space habitat. We use this database in combination with traditional hazard assessment techniques to prioritize control of possible disruptions and hazardous states. To mitigate hazards, we develop a safety control option space that contains safety controls that either prevent transition to hazardous states or return the habitat to a nominal state. We use generic safety controls, or the principle of control, to generate new safety controls as our set of disruptions and hazardous states grows, and store these in the database. Lastly, we evaluate our mitigation techniques using our control effectiveness metric, a metric intended to assess how well a safety control addresses the hazardous state or disruption that it is designed for. Our control-theoretic approach is one way in which we can complete the system safety engineering process for a space habitat system and can provide design guidance for the development of resilient space habitats.</p>

<div><div><div><p>As technology advances, so does the complexity of engineering projects. Systems en- gineering has evolved with these technological advances as a means of coping with this complexity. Traditionally, systems engineers use separate documents to track all project in- formation. This trend continues today, as projects are becoming more complex than before and the traditional document based approach is now seen as time consuming and error prone. Model based Systems engineering (MBSE) is a systems engineering approach that seeks to incorporate all project information into a single source of truth that can be thought of as a model. However, there are several challenges that face widespread MBSE adoption, includ- ing data transfer incompatibilities between MBSE and engineering discipline models. These incompatibilities are a major focus of this thesis. One goal of this thesis is to demonstrate the addition of a translation framework between MBSE and the Modeling Architectures and Parameterization for Spacecraft (MAPS) environment. The translation framework takes information read from System Modeling Language (SysML) diagrams and converts it into a representation suitable for the MAPS environment. Adding a translation framework en- ables more rapid analysis of different architectures and allows more users to interact with the MAPS environment. This thesis also seeks a method by which to evaluate MBSE and systems engineering tools. A qualitative framework is created based on rigor in systems en- gineering. The rigor evaluation framework specifically targets weaknesses commonly found in MBSE to enable users to find better directions for these tools. The effectiveness of the translation framework is shown via a demonstration case. Additionally, the rigor evalua- tion framework is applied to the translation framework. This application results in several recommendations for improvements and demonstrates the evaluation framework’s ability to find and address problem areas in MBSE tools.</p></div></div></div>

Systems Engineering is catching pace in many segments of product manufacturing industries. Model-Based Systems Engineering (MBSE) is the formalized application of modeling to perform systems engineering activities. In order to effectively utilize the complete potential of MBSE, a methodology consisting of appropriate processes, methods and tools is a key necessity. In the last decade, several MBSE projects have been implemented in industries varying from aerospace and defense to automotive, healthcare and transportation. The Systems Modeling Language (SysML) standard has been a key enabler of these projects at many companies. Although SysML is capable of providing a rich representation of any system through various viewpoints, the journey towards adopting SysML to realize the true potential of MBSE has been a challenge. Among all, one of the common roadblocks faced by systems engineers across industries has been the software engineering-based nature of SysML which leads to difficulties in grasping the modeling concepts for people that do not possess a software engineering background. As a consequence, developing a system (or a system of systems) architecture model using SysML has been a challenging task for many engineers even after a decade of its inception and multiple successive iterations of the language specification. Being a modeling language, SysML is method-agnostic, but its associated limitations outweigh the advantages. ARCADIA (Architecture Analysis and Design Integrated Approach) is a systems and software architecture engineering method based on architecture-centric and model-based engineering activities. If applied properly, ARCADIA allows for a very effective way to model the architecture of multi-domain systems, and overcome many of the limitations faced in traditional SysML implementation. This thesis evaluates the architecture development capabilities of ARCADIA/Capella versus SysML following the Object-Oriented Systems Engineering Method (OOSEM). The study focuses on the key equivalences and differences between the two MBSE solutions from a model development perspective and provides several criteria to evaluate their effectiveness for architecture development using a conceptual case of Adaptive Cruise Control (ACC). The evaluation is based on three perspectives namely, architecture quality, ability to support key process deliverables, and the overall methodology. Towards this end, an industry-wide survey of MBSE practitioners and thought leaders was conducted to identify several concerns in using models but also to validate the results of the study. The case study demonstrates how the ARCADIA/Capella approach addresses several challenges that are currently faced in SysML implementation. From a process point of view, ARCADIA/Capella and SysML equally support the provision of the key deliverable artifacts required in the systems engineering process. However, the candidate architectures developed using the two approaches show a considerable difference in various aspects such as the mapping of the form to function, creating functional architectures, etc. The ARCADIA/Capella approach allows to develop a ‘good’ system architecture representation efficiently and intuitively. The study also provides answers to several useful criteria pertaining to the overall candidate methodologies while serving as a practitioner’s reference in selecting the most suitable approach.

This thesis explores the possibility of using the Collective System Design Methodology to design systems that will improve the health of people. The focus of the thesis is on the reversal of type-2 diabetes.

yes / This paper considers the importance of applying sound instructional systems design to the development of a learning intervention aimed at developing skills for the effective deployment of an enhanced methodology for engineering systems design analysis within a Product Development context. The leading features of the learning intervention are summarised including the content and design of a training course for senior engineering management which is central to the intervention. The importance of promoting behavioural change by fostering meaningful learning as a collaborative process is discussed. Comparison is made between the instructional design of the corporate learning intervention being developed and the systems engineering based product design process which is the subject of the intervention.

Engineering designers commonly make sequential information acquisition decisions such as selecting designs for performance evaluation, selecting information sources, deciding whom to communicate with in design teams, and deciding when to stop design exploration. There is significant literature on normative decision making for engineering design, however, there is a lack of descriptive modeling of how designers actually make information acquisition decisions. Such descriptive modeling is important for accurately modeling design decisions, identifying sources of inefficiencies, and improving the design process. To that end, the research objective of the dissertation is to understand how designers make sequential information acquisition decisions and identify models that provide the best description of a designer’s decisions strategies. For gaining this understanding, the research approach consists of a synthesis of descriptive theories from psychological and cognitive sciences, along with empirical evidence from behavioral experiments under different design situations. Statistical Bayesian inference is used to determine how well alternate descriptive decision models describe the experimental data. This approach quantifies a designer's decision strategies through posterior parameter estimation and Bayesian model comparison. <br><br>Two research studies, presented in this dissertation, focus on assessing the effects of monetary incentives, fixed budget, type of design space exploration, and the availability of system-wide information on information acquisition decisions. The first study presented in this dissertation investigates information acquisition by an individual designer when multiple information sources are available and the total budget is limited. The results suggest that the student subjects' decisions are better represented by the heuristic-based models than the expected utility(EU)-based models. <br>While the EU-based models result in better net payoff, the heuristic models used by the subjects generate better design performance. The results also indicate the potential for nudging designers' decisions towards maximizing the net payoff by setting the fixed budget at low values and providing monetary incentives proportional to the saved budget.<br><br>The second study investigates information acquisition through communication. The focus is on designers’ decisions about whom to communicate with, and how much to communicate when there is interdependence between subsystems being designed. This study analyzes team communication of NASA engineers at a mission design laboratory (MDL) as well as of engineering students designing a simplified automotive engine in an undergraduate classroom environment. The results indicate that the rate of interactions increases in response to the reduce in system-level design performance in both settings. Additionally, the following factors seem to positively influence communication decisions: the pairwise design interdependence, node-wise popularity (significant with NASA MDL engineers due to large team size), and pairwise reciprocity.<br><br>The dissertation work increases the knowledge about engineering design decision making in following aspects. First, individuals make information acquisition decisions using simple heuristics based on in-situ information such as available budget amount and present system performance.<br>The proposed multi-discipline approach proves helpful for describing heuristics analytically and inferring context-specific decision strategies using statistical Bayesian inference. This work has potential application in developing decision support tools for engineering design. Second, the comparison of communication patterns between student design teams and NASA MDL teams reveals that the engine experiment preserves some but not all of the communication patterns of interest. We find that the representativeness depends not on matching subjects, tasks, and context separately, but rather on the behavior that results from the interactions of these three dimensions. This work provides lessons for designing representative experiments in the future.