Cooperative Design, Cooperative Science: Investigating Collaborative Research through Design with Floracaching

This dissertation presents a case study of collaborative research through design with Floracaching, a gamified mobile application for citizen science biodiversity data collection. One contribution of this study is the articulation of collaborative research through design (CRtD), an approach that blends cooperative design approaches with the research through design methodology (RtD). Collaborative research through design is thus defined as an iterative process of cooperative design, where the collaborative vision of an ideal state is embedded in a design. Applying collaborative research through design with Floracaching illustrates how a number of cooperative techniques—especially contextual inquiry, prototyping, and focus groups—may be applied in a research through design setting. Four suggestions for collaborative research through design (recruit from a range of relevant backgrounds; take flexibility as a goal; enable independence and agency; and, choose techniques that support agreement or consensus) are offered to help others who wish to experiment with this new approach. Applying collaborative research through design to Floracaching yielded a new prototype of the application, accompanied by design annotations in the form of framing constructs for designing to support mobile, place-based citizen science activities. The prototype and framing constructs, which may inform other designers of similar citizen science technologies, are a second contribution of this research.

A Generalized Methodology to Characterize Composite Materials for Pyrolysis Models

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.