Implementation of the conjugate heat transfer code in KIVA-4

KIVA is an open Computational Fluid Dynamics (CFD) source code that is capable to compute the transient two and three-dimensional chemically reactive fluid flows with spray. The latest version in the family of KIVA codes is the KIVA-4 which is capable of handling the unstructured mesh. This project focuses on the implementation of the Conjugate Heat Transfer code (CHT) in KIVA-4. The previous version of KIVA code with conjugate heat transfer code has been developed at Michigan Technological University by Egel Urip and is be used in this project. During the first phase of the project, the difference in the code structure between the previous version of KIVA and the KIVA-4 has been studied, which is the most challenging part of the project. The second phase involves the reverse engineering where the CHT code in previous version is extracted and implemented in KIVA-4 according to the new code structure. The validation of the implemented code is performed using a 4-valve Pentroof engine case. A solid cylinder wall has been developed using GRIDGEN which surrounds 3/4th of the engine cylinder and heat transfer to the solid wall during one engine cycle (0-720 Crank Angle Degree) is compared with that of the reference result. The reference results are nothing but the same engine case run in the previous version with the original code developed by Egel. The results of current code are very much comparable to that of the reference results which verifies that successful implementation of the CHT code in KIVA-4.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.