A variety of techniques are available enabling both invasive measurement, where the monitoring device is installed in the medium of interest, and noninvasive measurement where the monitoring system observes the medium of interest remotely. In this article we review the general techniques available, as well as specific instruments for particular applications. The issues of measurement criteria including accuracy, thermal disturbance and calibration are described. Based on the relative merits of different techniques, a guide for their selection is provided.

This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

Summary: Contamination poses a significant problem to the circular economy (CE), which derives much of its value from maintaining pure material flows. The aim of this article is to frame contaminated interaction among other forms of contamination and investigate its effects on the CE. The research is based on a review of the contamination literature and case studies. We differentiate between three types of contamination influencing circular material flows: technical, which deals with fitness for use; systemic, which deals with efficiency in processing; and interaction, which deals with user‐object interaction and decision making. Our focus is on developing a foundational understanding of contaminated interaction and how it influences circular processes. Through multiple examples, contaminated interaction is shown to create three barriers to the CE: downcycling, disposal, and hindered circulation. Among other proposals to address contaminated interaction, the research calls for the development of experientially transferrable design—products that can move between users and uses without negative consequences.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.