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Creativity plays an important role in engineering problem solving, particularly when solving an ill-structured problem, and has been a topic of increasing research interest in recent years. Prior research on creativity has been conducted in problem solving settings, predominantly focusing on undergraduate engineering students, including how faculty can foster creativity in engineering students, how engineering faculty perceive their students’ creativity, and how to measure it. However, more work is needed to examine engineering faculty and practitioner perspectives on the role of creativity when they solve an engineering problem themselves. Since engineering students learn problem solving, at least initially, mainly from their professors, it is essential to understand how faculty perceive their own creativity in problem solving. Similarly, given that practitioners solve ill-structured engineering problems on a regular basis in the workplace and that most of the students go on to work in the engineering industry when they graduate and ultimately become practitioners, it is also important to explore practitioner perspectives on creativity in problem solving settings. As part of an ongoing NSF-funded study, this paper investigates how engineering faculty’s and practitioners’ creativity influences their problem solving processes, how their perspectives on creativity in a problem solving environment differ, and what factors impact their creativity. Five tenure-track faculty in civil engineering and five practitioners were interviewed after they solved an ill-structured engineering problem. Participants’ responses were transcribed and coded using initial coding. This paper discusses their responses to semi-structured interview questions. The findings suggest that faculty and practitioners feel more creative when they are familiar with the subject area of a problem. If they are aware of a particular solution that has been developed and used before or have access to resources to look them up, they may not necessarily embrace creativity. The findings indicated differences not only across faculty and practitioners but also within the faculty and practitioner participants. Similarities and differences between faculty and practitioners in creative problem solving and the themes emerged are discussed and recommendations for educators are provided. 

Building energy consumption is highly influenced by weather conditions, thus having appropriate weather data is important for improving the accuracy of building energy models. Typically local weather station data from the nearest airport or military base is used for weather data input. However this is generally known to differ from the actual weather conditions experienced by an urban building, particularly considering most weather stations are located far from urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides a means to be able to predict more localized variations in weather conditions. However, one of the main challenges associated with the assessment of the use of this model is the lack of availability of ground based weather station data with which to compare its results. This has generally limited the ability to assess the level of agreement between WRF-UCM weather predictions and measured weather data in urban locations. In this study, a network of 40 ground based weather stations located in Austin, TX are compared to WRF/UCM-predicted weather data, to assess similarities and differences between model-predicted results and actual data. Given that the WRF-UCM method also takes into account many input parameters and assumptions, including the urban fraction which can be measured at different scales, this work also considers the relative impact of the granularity of the urban fraction data on WRF-UCM predicted weather. As a case study, a building energy model of a typical residential building is then developed and used to assess the differences in predicted building energy use and demands between the WRF-UCM weather and measured weather conditions during an extreme heatwave event in Austin, TX 

The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques.