Identification of Influential Modules Considering Design Change Impacts Based on Parallel Breadth-First Search and Bat Algorithm

Modular design is a widely used strategy that meets diverse customer requirements. Close relationships exist between parts inside a module and loose linkages between modules in the modular products. A change of one part or module may cause changes of other parts or modules, which in turn propagate through a product. This paper aims to present an approach to analyze the associations and change impacts between modules and identify influential modules in modular product design. The proposed framework explores all possible change propagation paths (CPPs), and measures change impact degrees between modules. In this article, a design structure matrix (DSM) is used to express dependence relationships between parts, and change propagation trees of affected parts within module are constructed. The influence of the affected part in the corresponding module is also analyzed, and a reachable matrix is employed to determine reachable parts of change propagation. The parallel breadth-first algorithm is used to search propagation paths. The influential modules are identified according to their comprehensive change impact degrees that are computed by the bat algorithm. Finally, a case study on the grab illustrates the impacts of design change in modular products.

Options to design more ethical and still successful default nudges: a review and recommendations

On the one hand, default nudges are proven to strongly influence behavior. On the other hand, a number of consumer autonomy and welfare concerns have been raised that hinder public policy applications. Both nudge success and ethical concerns depend heavily on the design of defaults. We identify six taxonomic characteristics that matter to the ethical and the nudge success dimension. We review the default nudge literature (N = 61) and review ethical studies to assess both dimensions concerning the taxonomy. When designing a default, a choice architect inevitably makes a decision concerning the characteristics. Among others, the results show three main findings. (1) The initial choice architecture regularly imposes welfare losses and impedes consumer autonomy. Forced active choosing can mitigate both issues. (2) Empirical evidence suggests that transparent defaults are similarly effective as the non-transparent counterparts. (3) The framing of the choice in combination with a choice structuring default leads to greater nudge success and tends to involve the reflective decision-making patterns. Choice architects can trade-off nudge success for legitimacy but a design change may also benefit one without harming the other. We discuss further options of choice architects to legitimize a default.

Causes of Design Change Depending on Power Project-Types in Ghana

Design change is a common but significant problem in construction projects. Issues of delay, cost overruns, claims, and disputes in projects occur as a result. However, design change studies in the power-project area are less often discussed. As a result, the primary objective of this study was to identify important cause factors of design changes according to different power-project types in Ghana. Following a thorough assessment of the literature, 36 potential causes were identified, which were narrowed down by expert reviews to 30. In this study, power projects were classified into three categories: power plant, renewable, and distribution and transmission. The results indicate owner-related financial problems as the most important cause of design change for all three project types, followed by the second and third most significant in each of the categories, respectively: errors and omission in design and problems or unforeseen site conditions in power plant projects; deficient quality and quantity of resources and inflation and changes in interest and exchange rates in renewable projects; and problems or unforeseen site conditions and changes of plans in distribution and transmission projects. Based on the findings, power-project stakeholders are able to comprehend the dynamics of design change and develop effective design management strategies to reduce impact.

Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives

We study the use of Hessian interaction terms to quickly identify design variables that reduce variability of system performance. To start we quantify the uncertainty and compute the variance decomposition to determine noise variables that contribute most, all at an initial design. Minimizing the uncertainty is next sought, though probabilistic optimization becomes computationally difficult, whether by including distribution parameters as an objective function or through robust design of experiments. Instead, we consider determining the more easily computed Hessian interaction matrix terms of the variance-contributing noise variables and the variables of any proposed design change. We also relate the Hessian term coefficients to subtractions in Sobol indices and reduction in response variance. Design variable changes that can reduce variability are thereby identified quickly as those with large Hessian terms against noise variables. Furthermore, the Jacobian terms of these design changes can indicate which design variables can shift the mean response, to maintain a desired nominal performance target. Using a combination of easily computed Hessian and Jacobian terms, design changes can be proposed to reduce variability while maintaining a targeted nominal. Lastly, we then recompute the uncertainty and variance decomposition at the more robust design configuration to verify the reduction in variability. This workflow therefore makes use of UQ/SA methods and computes design changes that reduce uncertainty with a minimal 4 runs per design change. An example is shown on a Stirling engine design where the top four variance-contributing tolerances are matched with two design changes identified through Hessian terms, and a new design found with 20% less variance.

Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix

PurposeAs the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).Design/methodology/approachThe paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.FindingsThe digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.Originality/valueThis research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.