Space shuttle main engine numerical modeling code modifications and analysis

The user of computational fluid dynamics (CFD) codes must be concerned with the accuracy and efficiency of the codes if they are to be used for timely design and analysis of complicated three-dimensional fluid flow configurations. A brief discussion of how accuracy and efficiency effect the CFD solution process is given. A more detailed discussion of how efficiency can be enhanced by using a few Cray Research Inc. utilities to address vectorization is presented and these utilities are applied to a three-dimensional Navier-Stokes CFD code (INS3D)

Progress towards Automated Human Factors Evaluation

Cao, S. (2015). Progress towards Automated Human Factors Evaluation. 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, 3, 4266–4272. https://doi.org/10.1016/j.promfg.2015.07.414 This work is made available through a CC-BY-NC-ND 4.0 license. The licensor is not represented as endorsing the use made of this work. https://creativecommons.org/licenses/by-nc-nd/4.0/Human factors tests are important components of systems design. Designers need to evaluate users’ performance and workload while using a system and compare different design options to determine the optimal design choice. Currently, human factors evaluation and tests mainly rely on empirical user studies, which add a heavy cost to the design process. In addition, it is difficult to conduct comprehensive user tests at early design stages when no physical interfaces have been implemented. To address these issues, I develop computational human performance modeling techniques that can simulate users’ interaction with machine systems. This method uses a general cognitive architecture to computationally represent human cognitive capabilities and constraints. Task-specific models can be built with the specifications of user knowledge, user strategies, and user group differences. The simulation results include performance measures such as task completion time and error rate as well as workload measures. Completed studies have modeled multitasking scenarios in a wide range of domains, including transportation, healthcare, and human-computer interaction. The success of these studies demonstrated the modeling capabilities of this method. Cognitive-architecture-based models are useful, but building a cognitive model itself can be difficult to learn and master. It usually requires at least medium-level programming skills to understand and use the language and syntaxes that specify the task. For example, to build a model that simulates a driving task, a modeler needs to build a driving simulation environment so that the model can interact with the simulated vehicle. In order to simply this process, I have conducted preliminary programming work that directly connects the mental model to existing task environment simulation programs. The model will be able to directly obtain perceptual information from the task program and send control commands to the task program. With cognitive model-based tools, designers will be able to see the model performing the tasks in real-time and obtain a report of the evaluation. Automated human factors evaluation methods have tremendous value to support systems design and evaluatio

Supercomputer optimizations for stochastic optimal control applications

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations

Employees\u27 cognitive load and performance during multitasking use of Information Technology

Multitasking-based use of Information Technology, a term that we label MUIT, to accomplish work-related tasks has become a common behavior for employees in organizations. Despite this reality, most research to date has focused on studying either the use of one IT at a time or multi-tasking behaviors in experimental laboratory settings. As a result, this study aims to fill these gaps. Building upon cognitive load theory and multiple resource theory, this paper theorizes that MUIT positively influences cognitive load, which in turn, has a curvilinear (concave, in-verted U) relation with performance. In order to test our hypotheses, we employed the Experi-ence Sampling Method (ESM), a special form of diary study, to gather data on employees at multiple occasions for two weeks. The collected data are hierarchical (multiple observations within individuals), and thus, we employed multi-level regression to test the hypotheses. Results show, as hypothesized, a positive relation between MUIT and cognitive load, and an inverted U relation between cognitive load and performance. Therefore, this study demonstrates that in work settings although MUIT increases cognitive load, cognitive load is not always detrimental: some cognitive load has positive effects on performance until it reaches a tipping point where performance starts to suffer

Queuing Network Modeling of Human Multitask Performance and its Application to Usability Testing of In-Vehicle Infotainment Systems.

Human performance of a primary continuous task (e.g., steering a vehicle) and a secondary discrete task (e.g., tuning radio stations) simultaneously is a common scenario in many domains. It is of great importance to have a good understanding of the mechanisms of human multitasking behavior in order to design the task environments and user interfaces (UIs) that facilitate human performance and minimize potential safety hazards. In this dissertation I investigated and modeled human multitask performance with a vehicle-steering task and several typical in-vehicle secondary tasks. Two experiments were conducted to investigate how various display designs and control modules affect the driver's eye glance behavior and performance. A computational model based on the cognitive architecture of Queuing Network-Model Human Processor (QN-MHP) was built to account for the experiment findings. In contrast to most existing studies that focus on visual search in single task situations, this dissertation employed experimental work that investigates visual search in multitask situations. A modeling mechanism for flexible task activation (rather than strict serial activations) was developed to allow the activation of a task component to be based on the completion status of other task components. A task switching scheme was built to model the time-sharing nature of multitasking. These extensions offer new theoretical insights into visual search in multitask situations and enable the model to simulate parallel processing both within one task and among multiple tasks. The validation results show that the model could account for the observed performance differences from the empirical data. Based on this model, a computer-aided engineering toolkit was developed that allows the UI designers to make quantitative prediction of the usability of design concepts and prototypes. Scientifically, the results of this dissertation research offer additional insights into the mechanisms of human multitask performance. From the engineering application and practical value perspective, the new modeling mechanism and the new toolkit have advantages over the traditional usability testing methods with human subjects by enabling the UI designers to explore a larger design space and address usability issues at the early design stages with lower cost both in time and manpower.PHDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113590/1/fredfeng_1.pd

Patterns of multi-device use with the smartphone a video-ethnographic study of young adults’ multi-device use with smartphones in naturally occurring contexts

Using multiple devices at the same time is becoming increasingly common in the daily lives of users, be it for work or for leisure. This paper presents in situ qualitative and quantitative evidence of multi-device use from a dataset of over 200h of first-person and interview recordings (n = 41). We discuss three different ‘patterns’ of multi device use (work, leisure, mixed use) and illustrate the user experience in detail with three participant journeys. We find that the smartphone was always ‘in the mix’; we did not observe multi-device use without the smartphone, or isolated use of other devices. Overall, we suggest that looking at transitions between activities users engage in rather than devices they use is more effective to understand multi-device use. Based on this analysis, we highlight issues around the patterns and experiences of multi-device use in everyday life and provide recommendations for design and further research

AUTOMATED CODE GENERATION FOR SAFETY-RELATED APPLICATIONS: A CASE STUDY

This paper addresses issues relating to the suitability of using automated code generation(ACG) technologies for the development of real-time, safety-critical systems. This researchexplored the characteristics of model-based software development methodologies and the automatedcode generation tools that support them. Specifically, data related to the engineeringchallenges, skills, and effort associated with ACG practices and technologies were collectedas part of a case study. Characteristics such as the generated code’s organization, size, readability,traceability to model, real-time constructs, and exception handling were identified. Inaddition, the case study involved software engineering practices that incorporate integratedanalysis and design iterations throughout a model-based development process. The researchinvestigated both the static and dynamic characteristics of the selected techniques and tools,identified characteristics of ACG tools with potential impact on safety, and considered thesemantic consistency between representations

Examining the Effects of Distractive Multitasking with Peripheral Computing in the Classroom

The growing use of information and communication technologies (ICTs) in college campuses has dramatically increased the potential for multitasking among students who have to juggle classes, school assignments, work, and recreational activities. These students believe that they have become more efficient by performing two or more tasks simultaneously. The use of technology, however, has changed the student’s ability to focus and attend to what they need to learn. Research has shown that multitasking divides students’ attention, which could have a negative impact on their cognition and learning. The purpose of this study was to examine the effects of distractive multitasking on students’ attention and academic performance in a classroom setting. Several studies in cognitive psychology have focused on individuals’ divided attention between simultaneously occurring tasks. Such research has found that, because human attention and capacity to process information are selective and limited, a performance decrement often results when task performance requires divided attention. Distractive tasks are defined as tasks or activities for which cognitive resources are used to process information that is not related to the course material. Multitasking is defined as the engagement in individual tasks that are performed in succession through a process of context switching. Using a non-experimental, correlational research design, the researcher examined the effects of distractive multitasking, with computer devices, during classroom lectures, on students’ academic performance. This study used a monitoring system to capture data that reflected actual multitasking behaviors from students who used computers while attending real-time classroom lectures. The findings showed that there was no statistically significant relationship between the frequency of distractive multitasking (predictor variable) and academic performance (criterion variable), as measured by the midterm and final evaluation scores. The results did not support the hypothesis that distractive computer-based multitasking could have a negative impact on academic performance