Input‐output mathematics, which allows a modeler to fully consider direct and indirect relationships among conserved flows in a system, has a long history in economics with prominent use dating to Leontief in the 1930s. Nearly all previous industrial applications of input‐output analysis have been grounded in the monetary flows of an economy. Here however, because of the central nature of physical flows in the environmental impact of industry, we consider physical flows to be a fundamental component of an industrial economy. Hence, we propose an input‐output based approach for modeling physical flows in industry independent of their monetary implications. In this first part of a two‐part article, a framework for using input‐output mathematics to model material and energy flows is constructed from a foundation laid by previous research in nutrient and energy cycling in natural ecosystems. The mathematics of input‐output flow analysis is presented from an ecological perspective, culminating in two core capabilities: tracing of flows with environs (investigated in this article) and characterizing system behavior with flow metrics (presented in the second article). We assert that environ analysis is an effective means for tracing flows through industrial systems while fully considering direct and indirect flow paths. We explore material flows of aluminum and five other metals in depth using environ analysis in this article.

Validation of engineering research is typically anchored in the scientific inquiry tradition that is based primarily on logical induction and / or deduction. Since much engineering research is based on mathematical modeling, this kind of validation has worked — and still works — very well. There are, however, other areas of engineering research that rely on subjective statements as well as mathematical modeling, which makes this type of validation problematic. One such area is that of design methods within the field of engineering design. In this paper, we explore the question of how one validates design research in general, and design methods in particular. Being anchored in the scientific inquiry tradition, research validation is strongly tied to a fundamental problem addressed in epistemology, namely, what is scientific knowledge and how is new knowledge confirmed? Thus, we first look to epistemology for answers to why an approach solely based on ‘formal, rigorous and quantifiable’ validation constitutes a problem, and for an augmented approach to research validation. We then propose the ‘Validation Square’ which we validate by testing its internal consistency based on logic in addition to testing its external relevance based on its usefulness with respect to a purpose. We recognize that no one has the complete answer to the question we pose. To help us converge on an answer to these questions we “think aloud” and invite you to join us in doing the same. It is our hope that in so doing we, the members of this design research community, will all be the richer for it.

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While prior work indicates that seniors near the end of their capstone design course know more about design than first-year students, it is unclear where this knowledge is gained. We study two possible sources of seniors’ greater design knowledge: coursework during sophomore and junior years and industrial experience. The design process knowledge of seniors at the beginning of their capstone class was assessed and information about their industrial experience obtained. These data were compared to assessment data of first-year students at the end of an introduction to engineering design course. The results indicate that industrial experience greatly increases students’ recognition that documentation needs to occur throughout the design process. Seniors with industrial experience, however, are less aware that idea generation is an important part of design and are less able to allot time to different design activities than first-year students at the end of a hands-on introduction to engineering design course. For the remaining four aspects of design process knowledge assessed—namely, identifying the requirements for a project at the project’s outset, making decisions with a systematic process based on analysis, building and testing prototypes and final designs, and the overall layout of design including iteration—no differences are found between seniors with industrial experience and first-year students at the end of an introduction to engineering design course. One explanation for why industrial experience does not impact student’s design process knowledge positively in more areas than documentation is that students on internships only experience a small portion of a design process. Due to this “snapshot” experience, either (1) students are not able to learn a significant amount about the bigger picture design concepts or (2) students each learn about different aspects of design but, as a population, do not show any significant increase in design process knowledge. The one activity that all interns will experience is the necessity to document their work. Furthermore, seniors without industrial experience scored no differently than first-year students on any single aspect of design process knowledge measured. This indicates that analysis-heavy sophomore and junior classes do not impact design process knowledge.

Interdisciplinary undergraduate engineering programs have increased in importance and significance over the past decade. To understand the strengths and weaknesses of these interdisciplinary courses and programs, it is important to examine how students' perceptions develop through the duration of the course or program, specifically in regards to their approach to an interdisciplinary engineering problem. The overall purpose of this research is to assess second-year undergraduate engineering students' perceptions of interdisciplinary engineering work. In this study, students applied their knowledge to a real-world design problem. Students' responses were analyzed using verbal protocol analysis and a pre-defined coding scheme, which focused on four dimensions of interdisciplinary understanding. While the quantitative analysis does not reveal many statistically significant associations, these results reinforced those published in previous studies. Qualitative analysis of the data identified trends due to gender effects and interactions. In addition, the findings indicated students' awareness of the interdisciplinary approach and the importance of team dynamics within interdisciplinary projects.