Application of special-purpose digital computers to rotorcraft real-time simulation

The use of an array processor as a computational element in rotorcraft real-time simulation is studied. A multilooping scheme was considered in which the rotor would loop over its calculations a number of time while the remainder of the model cycled once on a host computer. To prove that such a method would realistically simulate rotorcraft, a FORTRAN program was constructed to emulate a typical host-array processor computing configuration. The multilooping of an expanded rotor model, which included appropriate kinematic equations, resulted in an accurate and stable simulation

Review of research in feature-based design

Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made

A virtual engineering framework to support progressive interaction in engineering design

Engineering design encompasses a series of non-trivial decision making phases in generating initial solutions, developing mathematical models, performing analysis, and optimizing designs. Engineering analysis and optimization are the phases that often significantly slow down the design process. Thorough designer exploration on the solution space increases the likelihood of determining the most feasible solution but, at the expense of longer lead times. The exploratory capabilities of the designer could be enhanced by creating an interactive virtual engineering framework. This research presents progressive interaction with the designer-in-the-loop whose intelligence is blended with the computational power to suitably control the optimization. Progressive interaction is a human-guided preference articulation method where the designer intelligence continuously controls the engineering analysis and optimization by visualization, modification and controlled re-optimization. Based on the designer\u27s knowledge and the knowledge available from the interaction system, the designer preferences can be modified anytime to expedite optimization. Progressive interaction not only helps the designer discover the hidden relationship between the decision variables but it also uncovers the implicit constraints and other performance limitations of the design. In summary, this research work proposes human-guided, progressive interaction as a solution to complex engineering optimization problems. The proposed solution is demonstrated using three test cases: (1) Interactive image segmentation and optimization, (2) Designer interaction to support shape optimization of a finned dissipater, and (3) Interactive analysis, optimization and design of hydraulic mixing nozzle

Application of Artificial Intelligence (AI) methods for designing and analysis of Reconfigurable Cellular Manufacturing System (RCMS)

This work focuses on the design and control of a novel hybrId manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular Manufacturing System (CMS) and Reconfigurable Manufacturing System (RMS). In addition to inheriting desirable properties from CMS and RMS, RCMS provides additional benefits including flexibility and the ability to respond to changing products, product mix and market conditions during its useful life, avoiding premature obsolescence of the manufacturing system. The emphasis of this research is the formation of Reconfigurable Manufacturing Cell (RMC) which is the dynamic and logical clustering of some manufacturing resources, driven by specific customer orders, aiming at optimally fulfilling customers' orders along with other RMCs in the RCMS

Artificial intelligence as a tool for research and development in European patent law

Artificial intelligence (“AI”) is increasingly fundamental for research and development (“R&D”). Thanks to its powerful analytical and generative capabilities, AI is arguably changing how we invent. According to several scholars, this finding calls into question the core principles of European patent law—the field of law devoted to protecting inventions. In particular, the AI revolution might have an impact on the notions of “invention”, “inventor”, “inventive step”, and “skilled person”. The present dissertation examines how AI might affect each of those fundamental concepts. It concludes that European patent law is a flexible legal system capable of adapting to technological change, including the advent of AI. First, this work finds that “invention” is a purely objective notion. Inventions consist of technical subject-matter. Whether artificial intelligence had a role in developing the invention is therefore irrelevant as such. Nevertheless, de lege lata, the inventor is necessarily a natural person. There is no room for attributing inventorship to an AI system. In turn, the notion of “inventor” comprises whoever makes an intellectual contribution to the inventive concept. And patent law has always embraced “serendipitous” inventions—those that one stumbles upon by accident. Therefore, at a minimum, the natural person who recognizes an invention developed through AI would qualify as its inventor. Instead, lacking a human inventor, the right to the patent would not arise at all. Besides, the consensus among scholars is that, de facto, AI cannot invent “autonomously” at the current state of technology. The likelihood of an “invention without an inventor” is thus remote. AI is rather a tool for R&D, albeit a potentially sophisticated one. Coming to the “skilled person”, they are the average expert in the field that can rely on the standard tools for routine research and experimentation. Hence, this work finds that if and when AI becomes a “standard” research tool, it should be framed as part of the skilled person. Since AI is an umbrella term for a myriad of different technologies, the assessment of what is truly “standard” for the skilled person – and what would be considered inventive against that figure – demands a precise case-by-case analysis, which takes into account the different AI techniques that exist, the degree of human involvement and skill for using them, and the crucial relevance of data for many AI tools. However, while AI might cause increased complexities and require adaptations – especially to the inventive step assessment – the fundamental principles of European patent law stand the test of time

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Making design decisions under uncertainties: probabilistic reasoning and robust product design

Making design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made

The composition of first-year engineering curricula and its relationships to matriculation models and institutional characteristics

The preparation of technically excellent and innovative engineering graduates urges for a reform of the engineering curriculum to meet critical challenges in society (National Academy of Engineering, 2005). An examination of the current engineering curricula is needed to offer a baseline to further discuss if the curriculum reform meets the critical challenges. Meanwhile, concern about engineering retention prioritizes a review of the first-year engineering curricula. The existing literature does not include a nationwide examination of the first-year engineering curricula and introductory engineering courses. This study aspired to fill the gap by providing a detail description of the composition of first-year engineering curricula and introductory engineering courses of all ABET EAC-accredited programs. Furthermore, this study investigated the degree to which first-year engineering curricula and institutional characteristics varied by the matriculation policies of engineering programs. ^ To this end, this study analyzed the recommended first-year course sequences of 1,969 engineering programs and descriptions of 2,222 first-year engineering courses at all 408 U.S. institutions with ABET EAC-accredited programs. Keywords extracted from the engineering course descriptions were classified using a revised First-Year Engineering Course Classification Scheme (Reid, Reeping, & Spingola, 2013). In addition, institutional characteristics of 408 institutions grouped by matriculation models were examined. ^ There were five major findings. First, engineering courses took up 14-17% of total credit hours in the first year. Most first-year engineering courses were mandatory instead of elective or optional. Mathematics and science still formed the basis of the early engineering curriculum by accounting for more than half of the first-year credit hours. Second, the composition of first-year engineering curricula, the composition of first-year engineering courses, and the time when the first engineering course was required all varied by matriculation models. Third, topics related to engineering technologies and tools were listed most frequently in first-year engineering course descriptions, followed by topics related to design and the engineering profession. Topics related to global interest were seldom listed. Fourth, while first-year course composition varied by matriculation model, the most frequently listed topics were shared by programs with varied matriculation models, suggesting that content selection of first-year engineering courses was homogenous nationally. Lastly, institutions with different matriculation models had distinct characteristics, demonstrating the existence of relationships between institution-level and unit-level variables shown in the Model of Academic Plans in Context (Lattuca & Stark, 2009). ^ Findings of this study addressed fundamental questions of engineering education research, and had the potential to help program administrators and instructors with program and curriculum planning purposes