The space–time characteristics of a compliant wall in a turbulent channel flow are investigated using direct numerical simulation (DNS). The compliant wall is modelled as a homogeneous plane supported by spring-and-damper arrays and is passively driven by wall-pressure fluctuations. The frequency/wavenumber spectra and convection velocities of the wall-pressure fluctuations, wall displacement and wall velocity are obtained from the present simulation. As the spring, damping, and tension coefficients decrease, the wall becomes softer and the wall displacement and velocity fluctuations increase. For a relatively stiff compliant wall (i.e. large spring, damping and streamwise tension coefficients), there are few changes in the skin-friction drag and near-wall turbulence structures. However, when a compliant wall is soft (i.e. small spring, damping and streamwise tension coefficients), the wall moves in the form of a large-amplitude quasi-two-dimensional wave travelling in the downstream direction. This wave is generated by the resonance of the wall property and the near-wall flow is significantly activated by this wall motion. The power spectra of wall variables show distinct peaks near the resonance frequencies. The convection velocities of the wall motion and wall-pressure fluctuations become smaller with a softer wall.
NGOs have been a major actor in South Korea's democratic governance and have sometimes played key roles in cooperation with the government. However, their once prominent status is in decline, attributed here to their structural weakness, over-politicization, and the risk of being potentially co-opted by the government
Technology is rapidly changing, and customers are seeking not just products and services but experiences. Traditional approaches to strategic planning using product and technology roadmaps are no longer sufficient. This article describes the emergence of what the authors call "design roadmapping" approaches that are grounded in the creation of customer experiences, and which drive the choices of features, functionality, and technology. Design roadmapping provides stability for the future, even as technologies are changing, and keeps organizations more focused on the customers they are serving.
We propose a framework for design roadmapping that parallels existing product roadmapping and technology roadmapping processes. It leverages three needs we have observed in organizations as they use existing roadmapping processes: (1) to focus on development of customer and user experiences (UX), not just on features; (2) to increase engagement of designers early in the planning process; and (3) to provide a means for rapidly responding to changes in the environment. Design roadmapping is an attempt to reconcile differences that arise when customer/user needs are not considered simultaneously with technology choices. The proposed design roadmapping process assists project prioritization and selection. The process aggregates design experience elements along a timeline that associates key user needs with the products, services, and/or systems the organization wishes to deliver. To illustrate the design roadmapping process, we conducted a case study in which we applied the design roadmapping process to projects undertaken by a large corporation's innovation lab located in research centers in San Francisco and Mountain View, CA, in partnership with corporate stakeholders located in Asia. The five-step design roadmapping procedure is provided along with detailed information. The decisions from the design roadmapping process have been incorporated into the company's commercial plans. Key findings in this corporate case study bolster the positive impact of design roadmapping in moving strategic thinking from a technology/ feature-driven process to one that is design/experience-driven. It shows how firms might weigh choices between user needs, design principles, and technological innovation.
Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation processes.
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