A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system's heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.

A modular product architecture is a strategic means to deliver external variety and internal commonality. In this paper, we propose a new clustering-based method for product modularization that integrates product complexity and company business strategies. The proposed method is logically verified by a studied industrial case, where the architecture of a heavy truck driveline is analyzed in terms of how it has evolved over a couple of decades, due to changed business strategies and the evolution of new technology. The presented case indicates that the new methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company-specific strategies. Furthermore, the case study clearly shows that the business strategic reasons for a specific architecture can be found by analyzing how sensitive the clusters are to changes in the module drivers (MD).

In most city water distribution systems, a considerable amount of water is lost because of leaks occurring in pipes. Moreover, an unobservable fluid leakage fault that may occur in a hazardous industrial system, such as nuclear power plant cooling process or chemical waste disposal, can cause both environmental and economical disasters. This situation generates crucial interest for industry and academia due to the financial cost related with public health risks, environmental responsibility, and energy efficiency. In this paper, to find a reliable and economic solution for this problem, adaptive neuro fuzzy inference system (ANFIS) method which consists of backpropagation and least-squares learning algorithms is proposed for estimating leakage locations in a complex water distribution system. The hybrid algorithm is trained with acceleration, pressure, and flow rate data measured through the sensors located on some specific points of the complex water distribution system. The effectiveness of the proposed method is discussed comparing the results with the current methods popularly used in this area.

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Modern cyber-physical production systems (CPPS) connect different elements like machine tools and workpieces. The constituent elements are often equipped with high-performance sensors as well as information and communication technology, enabling them to interact with each other. This leads to an increasing amount and complexity of data that requires better analysis tools to support system refinement and revision performed by an expert. This paper presents a user-guided visual analysis approach that can answer relevant questions concerning the behavior of cyber-physical systems. The approach generates visualizations of aggregated views that capture an entire production system as well as specific characteristics of individual data features. To show the applicability of the presented methodologies, an exemplary production system is simulated and analyzed.

Simulation-based methods are emerging to address the challenges of complex systems risk assessment, and this paper identifies two problems related to the use of such methods. First, the methods cannot identify new hazards if the simulation model builders are expected to foresee the hazards and incorporate the abnormal behavior related to the hazard into the simulation model. Therefore, this paper uses the concept of deviation from design intent to systematically capture abnormal conditions that may lead to component failures, hazards, or both. Second, simulation-based risk assessment methods should explicitly consider what expertise is required from the experts that build and use the simulation models—the transfer of the methods to real engineering practice will be severely hindered if they must be performed by persons that are expert in domain safety as well as advanced computer simulation-based methods. This paper addresses both problems in the context of the functional failure identification and propagation (FFIP) method. One industrially established risk assessment method, hazard and operability study (HAZOP), is harnessed to systematically obtain the deviations from design intent in the application under study. An information system presents a user interface that is understandable to HAZOP professionals, so that their inputs are transparently entered to a data model that captures the deviations. From the data model, instructions for configuring FFIP simulation models are printed in a form that is understandable for FFIP experts. The method is demonstrated for discovering a hazard resulting from system-wide fault propagation in a boiling water reactor case.

The paper presents a formal representation for modeling function structure graphs in a consistent, grammatically controlled manner, and for performing conservation-based formal reasoning on those models. The representation consists of a hierarchical vocabulary of entities, relations, and attributes, and 33 local grammar rules that permit or prohibit modeling constructs thereby ensuring model consistency. Internal representational consistency is verified by committing the representation to a Protégé web ontology language (OWL) ontology and examining it with the Pellet consistency checker. External representational validity is established by implementing the representation in a Computer Aided Design (CAD) tool and using it to demonstrate that the grammar rules prohibit inconsistent constructs and that the models support physics-based reasoning based on the balance laws of transport phenomena. This representation, including the controlled grammar, can serve, in the future, as a basis for additional reasoning extensions.

We present a framework for modeling and analysis of real-world business workflows. We present a formalized core subset of the business process modeling and notation (BPMN) and then proceed to extend this language with probabilistic nondeterministic branching and general-purpose reward annotations. We present an algorithm for the translation of such models into Markov decision processes (MDP) expressed in the syntax of the PRISM model checker. This enables precise quantitative analysis of business processes for the following properties: transient and steady-state probabilities, the timing, occurrence and ordering of events, reward-based properties, and best- and worst- case scenarios. We develop a simple example of medical workflow and demonstrate the utility of this analysis in accurate provisioning of drug stocks. Finally, we suggest a path to building upon these techniques to cover the entire BPMN language, allow for more complex annotations and ultimately to automatically synthesize workflows by composing predefined subprocesses, in order to achieve a configuration that is optimal for parameters of interest.

This paper validates that a previously published formal representation of function structure graphs actually supports the reasoning that motivated its development in the first place. In doing so, it presents the algorithms to perform those reasoning, provides justification for the reasoning, and presents a software implementation called Concept Modeler (ConMod) to demonstrate the reasoning. Specifically, the representation is shown to support constructing function structure graphs in a grammar-controlled manner so that logical and physics-based inconsistencies are prevented in real-time, thus ensuring logically consistent models. Further, it is demonstrated that the representation can support postmodeling reasoning to check the modeled concepts against two universal principles of physics: the balance laws of mass and energy, and the principle of irreversibility. The representation in question is recently published and its internal ontological and logical consistency has been already demonstrated. However, its ability to support the intended reasoning was not validated so far, which is accomplished in this paper.

Emergent behavior is a unique aspect of complex systems, where they exhibit behavior that is more complex than the sum of the behavior of their constituent parts. This behavior includes the propagation of faults between parts, and requires information on how the parts are connected. These parts can include software, electronic and mechanical components, hence requiring a capability to track emergent fault propagation paths as they cross the boundaries of technical disciplines. Prior work has introduced the functional failure identification and propagation (FFIP) simulation framework, which reveals the propagation of abnormal flow states and can thus be used to infer emergent system-wide behavior that may compromise the reliability of the system. An advantage of FFIP is that it is used to model early phase designs, before high cost commitments are made and before high fidelity models are available. This has also been a weakness in previous research on FFIP, since results depend on arbitrary choices for the values of model parameters and timing of critical events. Previously, FFIP has used a discrete set of flow state values and a simple behavioral logic; this has had the advantage of limiting the range of possible parameter values, but it has not been possible to model continuous process dynamics. In this paper, the FFIP framework has been extended to support continuous flow levels and linear modeling of component behavior based on first principles. Since this extension further expands the range of model parameter values, methods and tools for studying the impact of parameter value changes are introduced. The result is an evaluation of how the FFIP results are impacted by changes in the model parameters and the timing of critical events. The method is demonstrated on a boiling water reactor model (limited to the coolant recirculation and steam outlets) in order to focus the analysis of emergent fault behavior that could not have been identified with previously published versions of the FFIP framework.

Speed perception is an important task depending mainly on optic flow that the driver must perform continuously to control his/her vehicle. Unfortunately, it appears that in some driving simulators speed perception is under estimated, leading into speed production higher than in real conditions. Perceptual validity is then not good enough to study driver’s behavior. To solve this problem, a technique has recently seen the light, which consists of modifying the geometric field of view (GFOV) while keeping the real field of view (FOV) constant. We define our visual scale factor as the ratio between the GFOV and the FOV. The present study has been carried out on the SAAM dynamic driving simulator and aims at determining the precise effect of this visual scale factor on the speed perception. Twenty subjects have reproduced two speeds (50 and 90 km/h) without knowing the numerical values of these consigns, with five different visual scale factors: 0.70, 0.85, 1.00, 1.15, and 1.30. We show that speed perception significantly increases when the visual factor increases. A 0.15 modification of this factor is enough to obtain a significant effect. Furthermore, the relative variation of the speed perception is proportional to the visual scale factor. Besides, the modification of the geometric field of view remained unnoticed by all the subjects, which implies that this technique can be easily used to make drivers to reduce their speed in driving simulation conditions. However, this technique may also modify perception of distances.

This paper presents a formalization of the notion of function as operation on flows as advanced in the Functional Basis approach of Stone and Wood. We first analyze the modeling of functions in this approach and identify the notions that are ontological significant for their formalization within the foundational ontology DOLCE. Then, we build the logical system in which this engineering notion of function is formally translated and connected to the ontology. Furthermore, we posit a number of constraints for a correct interpretation of the formal system and also provide a web ontology language version. We conclude with an assessment of our results and a discussion of our larger project aimed at analysing functional descriptions of technical artifacts, and at translating functional descriptions using different engineering notions of function.

The role of virtual environments (VEs) is crucial in efficient design and operation of unmanned vehicles. VEs are extensively used in operator training for tele-operation, planning using programming by demonstration, and hardware and software designs. VE for unmanned sea surface vehicles (USSV) requires a 6 degree of freedom dynamics simulation in the time domain. In order to be interactive, the VE requires real-time performance of the underlying dynamics simulator. In general, the dynamics simulation of USSVs involves the following four main operations: (1) computation of dynamic pressure head due to fluid flow around the hull under the ocean wave, (2) computation of wet surface, (3) computing the surface integral of the dynamic pressure head over the wet surface, and (4) solving the rigid body dynamics equation. The first three operations depend upon the boat geometry complexity and need to be performed at each time step, making the simulation run very slow. In this paper, we address the problem of physics preserving model simplification for real-time potential flow based simulator for a USSV in the time domain, with an arbitrary hull geometry. This paper reports model simplification algorithms based on clustering, temporal coherence, and hardware acceleration using parallel computing on multiple cores to obtain real time simulation performance for the developed VE.

Defining or understanding a product in terms of its functions facilitates a wide variety of tasks such as design synthesis, modeling, and analysis. However, the lack of a semantically correct formal representation of product functions creates a barrier to their effective capture, exchange, and reuse. This paper presents Function Semantics Representation, a rule-based ontological formalism that is consistent with the Semantic Web standards to capture different components of a product function. In particular, the Semantic Web Rule Language is used to overcome limitations in using the basic Web Ontology Language ontology to explicitly capture advanced semantics essential to completely represent product functions. This enables support for an effective reasoning mechanism to develop and validate the product function (or functional model). We present examples that demonstrate consistency checking and the ability to retrieve functionally similar products from a repository.

In this paper, two approaches for computing the topological information content of function models in mechanical engineering design are developed and compared. Previously, a metric for computing information content of functions and flows within function models was proposed. Here, this metric is evolved to include the information contained in the connections between flows and functions in a function model. The first approach is based on uniform unconditional probability of a flow connecting any two functions within the model. The second approach is based on additional knowledge that the functions and flows in a model have limited compatibility, thereby, reducing the choices for origin and destination functions for each flow. This additional knowledge is represented using a new graphical representation supported by syntactic grammar rules. Both approaches are then applied to an example function model. Comparison between the approaches shows that the inclusion of this additional knowledge increases the expressiveness by reducing the uncertainty associated with function models.

The acceleration of the product development cycle continues to be a significant challenge for manufacturing enterprises around the world. This paper describes a task planning method that minimizes the number of trial and error to reduce the development time for large-scale and complex products at the early stage of product development. The proposed method matches groups of product components and determines the development sequence for each component to minimize the amount of feedback information required across task groups. The method provides, as evaluation indices for task prioritization, the product-sum of engineering interaction among components and worth of each component, which the authors define as the “worth flow.” A generic hair dryer with simple mechanical structure serves as an example, illustrating that the proposed method contributes to the reduction in the amount of information required for setting the interface links by 65% compared with the conventional planning methods.

Freeform deformation (FFD) is a well established technique for 3D animation applications, used to deform two—or three-dimensional geometrical entities. Over the past few years, FFD technique has aroused growing interest in several scientific communities. In this work, an extensive bibliographic survey of the FFD technique is initially introduced, in order to explore its capabilities in shape parametrization. Moreover, FFD technique is compared to the classical parametrization technique using B-spline curves, in the context of the airfoil design optimization problem, by performing inverse airfoil design tests, with a differential evolution algorithm to serve as the optimizer. The criterion of the comparison between the two techniques is the achieved accuracy in the approximation of the reference pressure distribution. Experiments are presented, comparing FFD to B-spline techniques under the same flow conditions, for various numbers of design variables. Sensitivity analysis is applied for providing further insight into the differences in the performance of the two techniques.

An automatic transmission (AT) hydraulic control system includes many spool-type valves that have highly asymmetric flow geometry. A simplified flow field model based on a lumped geometry is computationally efficient. However, it often fails to account for asymmetric flow characteristics, leading to an inaccurate analysis. An accurate analysis of their flow fields typically requires using the computational fluid dynamics (CFD) technique, which is numerically inefficient and time consuming. In this paper, a new hydraulic valve fluid field model is developed based on non-dimensional artificial neural networks (NDANNs) to provide an accurate and numerically efficient tool in AT control system design applications. A grow-and-trim procedure is proposed to identify critical non-dimensional inputs and optimize the network architecture. A hydraulic valve testing bench is designed and built to provide data for neural network model development. NDANN-based fluid force and flow rate estimators are established based on the experimental data. The NDANN models provide more accurate predictions of flow force and flow rates under broad operating conditions (such as different pressure drops and valve openings) compared with conventional lumped flow field models. Because of its non-dimensional characteristic, the NDANN fluid field estimator also exhibits good input-output scalability, which allows the NDANN model to estimate the fluid force and flow rate even when the operating condition parameter or design geometry parameters are outside the range of the training data. That is, although the operating/geometry parameter values are outside the range of the training sets, the non-dimensional values of the specific operating/geometry parameters are still within the training range. This feature makes the new model a potential candidate as a system design tool.

This paper presents a framework and information model for the development of SECOND-CAD (Systems Engineering CONceptual Design-CAD), or 2nd-CAD, a Computer Aided Conceptual Design (CACD) tool for Electromechanical Systems. The conceptual design tasks supported include functional design, behavior modeling, and component selection from standard industrial supply catalogs for mechanical, fluid, and electric engineering domains. 2nd-CAD is composed of three entity catalogs the designer uses to create three interconnected structures for function, behavior, and component. The logical model behind 2nd-CAD is one of the major contributions of this research. It allows the user to define entities based on popular taxonomies; this eases data exchange with other tools. When constructing structures, only technically feasible relationships are permitted and if an element in a structure is modified, the change is propagated throughout the structure. It reuses the entities’ information content to create new structures and since the three structures are interconnected, changes can be traced for design validation. 2nd-CAD’s functional requirements, logical design, and physical implementation are discussed in this paper.

The research presented in this paper is the design and implementation of a force feedback hand master called AirGlove. The device uses six ports arranged in a Cartesian coordinate frame setting to apply a point force to the user’s hand. Compressed air is exhausted through the ports, creating thrust forces. The magnitude and direction of the resultant force are controlled by changing the flow rate of the air jets and by activating different ports. The AirGlove can apply an arbitrary point force to the user’s hand. However, the main goal of this research is to reflect gravitational forces to the user so that sensation of weight of a virtual object can be created. After introduction of the main concept of the AirGlove, the paper presents design and implementation details of the device. Integration of the AirGlove with a virtual assembly system called VADE is explained next. Finally, details of experiments with the device are presented and discussed. Results indicate that users wearing the AirGlove can feel a minimum mass of about 100 grams (∼1N weight) and the device can create a fairly realistic weight sensation.