We have been involved in research work in the field of finite element analysis (FEA) integration with computer aided design (CAD) for several years and have developed several concepts and tools that have aroused interest and shown efficiency. In the meantime, both the evolution of our research developments (on topics like geometry comparison, geometry reconstruction and simplification, mixed-dimensional analysis and topology optimization) and the evolution of CAD systems and CAD kernels made us reconsider our database organization. This led to the design of an original development environment and database organization referred to as the Unified Topological Model (UTM). The main interests of this new CAD/FEA database organization is its ability to tackle multi-platform CAD/FEA integration (handling geometries coming from different CAD kernels), mixed-dimensional modeling and analysis (3D solid geometry mixed and integrated with surface geometry and curvilinear geometry) and topology optimization (TO) procedures. The paper presents the structure of this new research development environment and the original concepts underlying it. The UTM environment is strongly designed around object-oriented computer programming concepts and it is focused towards generality, modularity and ability to evolve. The paper also briefly presents some of the most important features and algorithms that have been integrated, at this point, into the UTM environment.

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The traditional design process used within the automotive industry is typically accomplished with limited regard to the environment the vehicle will operate within. As a result there are many growing challenges relating to mobility that we now face. When systems thinking methodologies are integrated into the design process innovative and holistic concepts are developed, causing significant shifts in new products, processes, and services that are better suited for the environment. Systems thinking methodologies in combination with the Developmental Model for Designers enables the creation of solutions from a more informed perspective. As a result, we are discovering new and appropriate ways to develop products and systems that are holistic solutions for the challenges of today and the future.

In today's highly competitive market, CAD tools are widely used and thought to reduce time to market and increase engineering productivity. However, to take advantage of these putative benefits requires proper use of CAD tools. Merely teaching declarative knowledge (particular keystrokes and button picks) in CAD is not sufficient; students should acquire deeper procedural knowledge (design strategy) in CAD. This will allow them to gain a level of expertise that is adaptive in nature. Recent research in engineering education finds that experts demonstrate two distinct characteristics: adaptive versus routine expertise. Adaptive experts possess the content knowledge similar to routine experts in the field, but also the ability to effectively utilize and extend their content knowledge. Epistemological beliefs, metacognitive skills, multiple perspectives, and learning orientations are among the constructs that can define adaptive expertise. This work describes the implementation of an instrument used to measure adaptive expertise in two courses at two universities. The instrument contains questions covering four dimensions: multiple perspectives, meta-cognitive self-assessment, goals and beliefs, and epistemology. In one university setting, freshmen and sophomore engineering students were surveyed with the instrument; in the other, junior and senior level engineering students were surveyed. In addition to the student participants, practicing engineers from industry were surveyed using the instrument. Participant demographic, education, and engineering experience data were collected. These data were used to examine the relationships among expertise related responses and demographic variables. We report the factor analyses results and the reliability coefficients of the instrument and the observed differences between students' and engineers' responses to survey items.