Use, potential, and showstoppers of models in automotive requirements engineering

Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g. to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g. restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multi-disciplinary environments. Additionally, we see the need for future work in the area of model use, i.e. information extraction from models by non-expert modellers

Setting sail towards predictive maintenance:developing tools to conquer difficulties in the implementation of maintenance analytics

Unexpected downtime of equipment is disruptive in complex manufacturing supply chains and imposes high costs due to forgone productivity. Executives in asset-intensive industries therefore regard such unexpected failures of their physical assets as a primary operational risks to their business. Predictive maintenance (PdM) (including condition-based maintenance) can aid practitioners in preventing these unexpected failures and getting insight into current and future behaviour of their assets. However, the use of PdM in practice seems to lag behind recent technological advancements and our theoretical understanding. The current study therefore aims to further develop our understanding on the use and adoption of predictive maintenance and, based on these observations, develop tools to better support the practical application of predictive maintenance. This research is guided by the following research question: How can the practical application of predictive maintenance better be supported? To be able to answer this question, an explorative multiple-case study is conducted including fourteen cases from various industries in the Netherlands to study successful applications of predictive maintenance. The focus in this multiple-case study lays on both the technical and the organizational aspects of PdM, because the organizational application process of PdM seems overlooked by the academic literature. The multiple case study reveals that almost all organizations who applied PdM successfully have followed a costly trial and error process. This appears to be the result of the technical and organizational complexity of the application of PdM and the absence of effective theoretical guidance in: (i) selecting the most suitable techniques for PdM; (ii) identifying the most suitable candidates for PdM; and (iii) evaluating the added value of PdM. To conquer the three main identified problems and to assist practitioners in the implementation of PdM, three corresponding decision support tools – which can be used together – have been designed in the remainder of this dissertation. The three solutions are designed using a structured design science process. Therefore, after studying the problems in-depth to define design criteria and select design principles, the developed solutions are demonstrated in practice using case studies in various industries. Future research should be guided towards the refinement and testing of the provided methods

A methodology for rapid vehicle scaling and configuration space exploration

Drastic changes in aircraft operational requirements and the emergence of new enabling technologies often occur symbiotically with advances in technology inducing new requirements and vice versa. These changes sometimes lead to the design of vehicle concepts for which no prior art exists. They lead to revolutionary concepts. In such cases the basic form of the vehicle geometry can no longer be determined through an ex ante survey of prior art as depicted by aircraft concepts in the historical domain. Ideally, baseline geometries for revolutionary concepts would be the result of exhaustive configuration space exploration and optimization. Numerous component layouts and their implications for the minimum external dimensions of the resultant vehicle would be evaluated. The dimensions of the minimum enclosing envelope for the best component layout(s) (as per the design need) would then be used as a basis for the selection of a baseline geometry. Unfortunately layout design spaces are inherently large and the key contributing analysis i.e. collision detection, can be very expensive as well. Even when an appropriate baseline geometry has been identified, another hurdle i.e. vehicle scaling has to be overcome. Through the design of a notional Cessna C-172R powered by a liquid hydrogen Proton Exchange Membrane (PEM) fuel cell, it has been demonstrated that the various forms of vehicle scaling i.e. photographic and historical-data-based scaling can result in highly sub-optimal results even for very small O(10-3) scale factors. There is therefore a need for higher fidelity vehicle scaling laws especially since emergent technologies tend to be volumetrically and/or gravimetrically constrained when compared to incumbents. The Configuration-space Exploration and Scaling Methodology (CESM) is postulated herein as a solution to the above-mentioned challenges. This bottom-up methodology entails the representation of component or sub-system geometries as matrices of points in 3D space. These typically large matrices are reduced using minimal convex sets or convex hulls. This reduction leads to significant gains in collision detection speed at minimal approximation expense. (The Gilbert-Johnson-Keerthi algorithm is used for collision detection purposes in this methodology.) Once the components are laid out, their collective convex hull (from here on out referred to as the super-hull) is used to approximate the inner mold line of the minimum enclosing envelope of the vehicle concept. A sectional slicing algorithm is used to extract the sectional dimensions of this envelope. An offset is added to these dimensions in order to come up with the sectional fuselage dimensions. Once the lift and control surfaces are added, vehicle level objective functions can be evaluated and compared to other designs. For each design, changes in the super-hull dimensions in response to perturbations in requirements can be tracked and regressed to create custom geometric scaling laws. The regressions are based on dimensionally consistent parameter groups in order to come up with dimensionally consistent and thus physically meaningful laws. CESM enables the designer to maintain design freedom by portably carrying multiple designs deeper into the design process. Also since CESM is a bottom-up approach, all proposed baseline concepts are implicitly volumetrically feasible. Furthermore the scaling laws developed from custom data for each concept are subject to less design noise than say, regression based approaches. Through these laws, key physics-based characteristics of vehicle subsystems such as energy density can be mapped onto key system level metrics such as fuselage volume or take-off gross weight. These laws can then substitute some historical-data based analyses thereby improving the fidelity of the analyses and reducing design time.Ph.D.Committee Chair: Dr. Dimitri Mavris; Committee Member: Dean Ward; Committee Member: Dr. Daniel Schrage; Committee Member: Dr. Danielle Soban; Committee Member: Dr. Sriram Rallabhandi; Committee Member: Mathias Emenet

Structural Batteries for Aeronautic Applications—State of the Art, Research Gaps and Technology Development Needs

Radical innovations for all aircraft systems and subsystems are needed for realizing future carbon-neutral aircraft, with hybrid-electric aircraft due to be delivered after 2035, initially in the regional aircraft segment of the industry. Electrical energy storage is one key element here, demanding safe, energy-dense, lightweight technologies. Combining load-bearing with energy storage capabilities to create multifunctional structural batteries is a promising way to minimize the detrimental impact of battery weight on the aircraft. However, despite the various concepts developed in recent years, their viability has been demonstrated mostly at the material or coupon level, leaving many open questions concerning their applicability to structural elements of a relevant size for implementation into the airframe. This review aims at providing an overview of recent approaches for structural batteries, assessing their multifunctional performance, and identifying gaps in technology development toward their introduction for commercial aeronautic applications. The main areas where substantial progress needs to be achieved are materials, for better energy storage capabilities; structural integration and aircraft design, for optimizing the mechanical-electrical performance and lifetime; aeronautically compatible manufacturing techniques; and the testing and monitoring of multifunctional structures. Finally, structural batteries will introduce novel aspects to the certification framework

An Empirical Investigation of Using Models During Requirements Engineering in the Automotive Industry

Context:The automotive industry is undergoing a major transformation from a manufacturing industry towards an industry that relies heavily on software. As one of the main factors for project success, requirements engineering (RE) plays a major role in this transition. Similar to other areas of automotive engineering, the use of models during RE has been suggested to increase productivity and tackle increasing complexity by means of abstraction. Existing modelling frameworks often prescribe a variety of different, formal models for RE, trying to maximise the benefit obtained from model-based engineering (MBE). However, these frameworks are typically based on assumptions from anecdotal evidence and experience, without empirical data supporting these assumptions.Objective:The overall aim of our research is to investigate the potential benefits and drawbacks of using model-based RE in an automotive environment based on empirical evidence. To do so, we present an investigation of the current industrial practice of MBE in the automotive industry, existing challenges in automotive RE, and potential use cases for model-based RE. Furthermore, we explore two use cases for model-based RE, namely the creation of behavioural requirements models for validation and verification purposes and the use of existing trace models to support communication.Method:We address the aims of this thesis using three empirical strategies: case study, design science and survey. We collected quantitative and qualitative data using interviews as well as questionnaires.Results:Our results show that using models during automotive RE can be beneficial, if restricted to certain aspects of RE. In particular, models supporting communication and stakeholder interaction are promising. We show that the use of abstract models of behavioural requirements are considered beneficial for system testing purposes, even though they abstract from the detailed functional requirements. Furthermore, we demonstrate that existing data can be understood as a model to uncover dependencies between stakeholders. Conclusions:Our results question the feasibility to construct and maintain large amounts of formal models for RE. Instead, models during RE should be used for a few, important use cases. Additionally, MBE can be used as a means to understand existing problems in software engineering

Human factors in developing automated vehicles: A requirements engineering perspective

Automated Vehicle (AV) technology has evolved significantly both in complexity and impact and is expected to ultimately change urban transportation. Due to this evolution, the development of AVs challenges the current state of automotive engineering practice, as automotive companies increasingly include agile ways of working in their plan-driven systems engineering—or even transition completely to scaled-agile approaches. However, it is unclear how knowledge about human factors (HF) and technological knowledge related to the development of AVs can be brought together in a way that effectively supports today\u27s rapid release cycles and agile development approaches. Based on semi-structured interviews with ten experts from industry and two experts from academia, this qualitative, exploratory case study investigates the relationship between HF and AV development. The study reveals relevant properties of agile system development and HF, as well as the implications of these properties for integrating agile work, HF, and requirements engineering. According to the findings, which were evaluated in a workshop with experts from academia and industry, a culture that values HF knowledge in engineering is key. These results promise to improve the integration of HF knowledge into agile development as well as to facilitate HF research impact and time to market

Exploring Blockchain Adoption Supply Chains: Opportunities and Challenges

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThe 2018 release of the DoD’s Digital Engineering (DE) strategy and the success of applying DE methods in the mechanical and electrical engineering domains motivate application of DE methods in other product development workflows, such as systems and/or software engineer-ing. The expected benefits of this are improved communication and traceability with reduced rework and risk. Organizations have demonstrated advantages of DE methods many times over by using model-based design and analysis methods, such as Finite Element Analysis (FEA) or SPICE (Simulation Program with Integrated Circuit Emphasis), to conduct detailed evaluations earlier in the process (i.e., shifting left). However, other domains such as embedded computing resources for cyber physical systems (CPS) have not yet effectively demonstrated how to in-corporate relevant DE methods into their development workflows. Although there is broad sup-port for SysML and there has been significant advancement in specific tools, e.g., MathWorks®, ANSYS®, and Dassault tool offerings, and standards like Modelica and AADL, the DE benefits to CPS engineering have not been broadly realized. In this paper, we will explore why CPS devel-opers have been slow to embrace DE, how DE methods should be tailored to achieve their stakeholders’ goals, and how to measure the effectiveness of DE-enabled workflows.Approved for public release; distribution is unlimited