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The purpose of this paper is to present research into reducing the aircraft design cycle period, by reducing the necessary number of design cycle iterations. The design cycle period is one of the main characteristics of the design process and design cycle iterations play a major role in the design cycle period.

To achieve the above‐mentioned goal, the paper presents a mathematical model of iterations for the aircraft design process. This model describes the design coupled tasks as a discrete‐linear time invariant dynamic system. This model also helps identify tasks which are the most important for generating iterations. This new method basically helps break information cycles that create iterations among important tasks.

Studies conducted on a general aviation (GA) airplane (FAJR‐3) design process show the success of the suggested approach. This procedure eventually leads to an expedited convergence rate for the design iterations. That is, through proper breaking of information cycles, the convergence rate of the most dominant design mode could be increased by up to 31 percent. The process also leads to decoupling of the so‐called “coupled parts of design process,” which in turn leads to a more modular design with relatively easier management.

This method offers a new way of managing aircraft design processes while having to deal with constraints such as time and resources. The approach could be easily implemented as it manages any complex design‐process based on its resemblance to a dynamic system. The method can also be used as a component of an Integrated Airframe Design (IAD), as a tool for “Cycle time reduction”.

The advantage of this new approach, over other existing ones, lies in its ability to distinguish the important information cycles in a systematic manner. This helps to break the design process in a way that guarantees the increase in convergence speed of the whole design process.

Introducing technology at work presents a special challenge as learning is tightly integrated with workplace practices. Current design-based research (DBR) methods are focused on formal learning context and often questioned for a lack of yielding traceable research insights. This paper aims to propose a method that extends DBR by understanding tools as sociocultural artefacts, co-designing affordances and systematically studying their adoption in practice.

The iterative practice-centred method allows the co-design of cognitive tools in DBR, makes assumptions and design decisions traceable and builds convergent evidence by consistently analysing how affordances are appropriated. This is demonstrated in the context of health-care professionals’ informal learning, and how they make sense of their experiences. The authors report an 18-month DBR case study of using various prototypes and testing the designs with practitioners through various data collection means.

By considering the cognitive level in the analysis of appropriation, the authors came to an understanding of how professionals cope with pressure in the health-care domain (domain insight); a prototype with concrete design decisions (design insight); and an understanding of how memory and sensemaking processes interact when cognitive tools are used to elaborate representations of informal learning needs (theory insight).

The method is validated in one long-term and in-depth case study. While this was necessary to gain an understanding of stakeholder concerns, build trust and apply methods over several iterations, it also potentially limits this.

Besides generating traceable research insights, the proposed DBR method allows to design technology-enhanced learning support for working domains and practices. The method is applicable in other domains and in formal learning.

The purpose of this paper is to demonstrate how a strategy implementation workshop design can be developed and tested while minimizing the time spent on developing the design.

This multiple case study at a diesel engine company shows how iterative prototyping can be used to structure the design process of a strategy implementation workshop.

Strategy implementation workshop design can be developed in resource-constrained environments through iterative prototyping of the workshop design. Each workshop iteration can generate value in its own right and at the same time the workshop design can be optimized until the final, most effective, design is found which can then be rolled out.

In a strategy-as-practice perspective, this study shows how scholarly attention to micro-level strategy praxis at a company can be enlightening to strategy consultants who need to conduct strategy implementation workshops.

By selecting an iterative modular workshop design, the strategy consultant has at his/her disposal a strategy tool that is easily adaptable to organizational practice and one for which s/he can draw on his/her experience as well as add to his/her knowledge base.

Introducing iterative prototyping in an organizational context can facilitate fast yet structured development of a rigorous workshop design. Strategy consultants are provided with empirical examples of how an iterative prototyping process can be structured across multiple workshops.

Traditional parallel methods for structural design, as well as modern preconditioned iterative linear solvers, do not scale well. This paper discusses the application of massively scalable cellular automata (CA) techniques to structural design, specifically trusses. There are two sets of CA rules, one used to propagate stresses and strains, and one to perform design updates. These rules can be applied serially, periodically, or concurrently, and Jacobi or Gauss‐Seidel style updating can be done. These options are compared with respect to convergence, speed, and stability for an example, problem of combined sizing and topology design of truss domain structures. The central theme of the paper is that the cellular automaton paradigm is tantamount to classical block Jacobi or block Gauss‐Seidel iteration, and consequently the performance of a cellular automaton can be rigorously analyzed and predicted.

Scheduling in information-driven design phase of construction projects is challenging due to multiple entity types (teams, components, deliverables, activities or parameters) and their dependencies/linkages. Established techniques such as dependency structure matrix (DSM), beeline diagramming method (BDM), multiple domain matrix (MDM), etc. have been independently utilized in past to model information dependencies/linkages and associated iteration. However, there has not been a holistic solution yet for scheduling multiple entity types and their relationships. Hence, an integrated solution needs to be developed that schedules information-driven projects accurately.

A case study data collection approach is utilized. With data from two projects, i.e. hostel design and highway design, a BDM–MDM integrated solution was developed and applied to the same. Feedback from experts was obtained for refinements.

The proposed solution is efficient for scheduling multiple entity types and their information dependencies/linkages.

The proposed integrated solution enables the project participants to schedule information-driven projects systematically. Application to two distinct design cases emphasizes that the concept is generic and can be applied to any information-driven project with multiple entity types.

The BDM–MDM integrated solution concept is investigated for scheduling multiple entity types in any information-driven projects. This study also explored the terminologies such as multiple entity types and information-driven scheduling.

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

The study's purpose was to examine the faculty-driven organization's design and development that supports faculty research needs, track the emergence of the community of practice (CoP) and provide greater insight into continued organizational design iterations.

In this longitudinal design case study, the authors employed different methods to collect and analyze archival, quantitative and qualitative data to capture the phenomenon's complexity.

The findings challenge the assumption that only formal organizational structures and top-down management approaches stimulate research and build research capacity in universities and propose a new sustainable and agile informal organizational structure and strategies to respond to faculty members' various research needs.

Future research is needed to investigate the tension between the individual researchers' and organizational needs, formal and informal organizational structures in universities, and the creation of a culture that would stimulate research.

Some of the recommended strategies and activities already have been implemented by the Research Consortium Committee (RCC), and faculty engagement in the RCC initiatives has increased. The practical implications are not limited to a College of Education (COE) context. The findings and the developed strategies could apply to many universities and colleges that desire to support their researchers. The research development officers, university administration and policymakers can consider the results of the present study to develop a comprehensive framework for research capacity and infrastructure building from not only organizational but individual perspectives.

This study provides one of the rare empirical investigations of the design, development and evolution of researchers' needs-driven informal organization in a higher education (HE) setting.

This study aims to review the development of six iterations of a master’s level course between the summers of 2013 and 2015, with a particular focus on the use of optional quests to engage and motivate student learning.

The comparative case study analysis draws on design-based research theory to consider learner activity, perceptions and commentary on course design.

Findings show students consistently exceeding expectations in the classroom, creating their own assignments, accepting custom challenges and, on average, sustaining a high regard for the learning process and format.

Positive results appear using free and available tools that can be adopted in any classroom setting.

Given the degree of voluntary engagement with course content, this local set of case studies implies that quest-based learning can drive an entire course design with positive results and provides a design model for others to adopt and build from.

Antenna miniaturization, multiband operation and wider operational bandwidth are vital to achieve optimal design for modern wireless communication devices. Using fractal geometries is recognized as one of the most promising solutions to attain these characteristics. The purpose of this paper is to present a unique structure of patch antenna using hybrid fractal technique to enhance the performance characteristics for various wireless applications and to achieve better miniaturization.

In this paper, the authors propose a novel hybrid fractal antenna by combining Koch and Minkowski (K-M) fractal geometries. A microstrip patch antenna (MPA) operating at 1.8 GHz is incorporated with a novel K-M hybrid fractal geometry. The proposed fractal antenna is designed and simulated in CST Microwave studio and compared with existing Koch fractal geometry. The prototype for the third iteration of the K-M fractal antenna is then fabricated on FR-4 substrate and tested through vector network analyzer for operating band/voltage standing wave ratio.

The third iteration of the proposed K-M fractal geometry results in achieving a 20% size reduction as compared to an ordinary MPA for the same resonant frequency with impedance bandwidth of 16.25 MHz and a directional gain of 6.48 dB, respectively. The operating frequency of MPA also lowers down to 1.44 GHz.

Further testing for the radiation patterns in an anechoic chamber shows good agreement to those of simulated results.