Iterative learning control for improved tracking of fluid percussion injury device

Traumatic brain injury (TBI) afflicts over 10 million people around the world. Injury to the brain can occur from a variety of physical insults and the degree of disability can greatly vary from person to person. It is likely that the wide range of TBI outcomes may be due to the magnitude, direction, and forces of biomechanical insult acting on the head during such TBI events. Lateral Fluid Percussion (FPI) brain injury is one of the most commonly used and well-characterized experimental models of TBI. A Fluid Percussion Injury (FPI) device in the laboratory is used to replicate the injury but does not execute the desired pressure profile. The controller used is a QCI-S3-IG Silver Sterling from Quick Silver Controls. A limitation innate to the controller was a 3-millisecond sampling of the input signal that proved challenging for developing fast, accurate FPI pulses with periods as fast as 18-milliseconds. Iterative Learning Control is implemented which conditions the input signal to the open loop system offline such that the desired pressure profile is attained

Multi-Objective Drive-Cycle Based Design Optimization of Permanent Magnet Synchronous Machines

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this thesis, a vehicle dynamics model for a direct-drive machine and its associated vehicle parameters is implemented for the urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, power, and energy. K-means clustering and Gaussian mixture modeling (GMM) are two clustering techniques used to reduce the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle. These methods offer high computational efficiency and low computational time cost while optimizing an electric machine. Differential evolution (DE) is employed to optimize the baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM). A computationally efficient finite element analysis (CEFEA) technique is developed to evaluate the machine at the representative drive-cycle points elicited from the clustering approaches. In addition, a steady-state thermal model is established to assess the electric motor temperature variation between optimization design candidates. In an alternative application, the drive-cycle cluster points are utilized for a computationally efficient drive-cycle system simulation that examines the effects of inverter time harmonics on motor performance. The motor is parameterized and modeled in a PSIM motor-inverter simulation that determines the current excitation harmonics that are injected into the machine during drive-cycle operation. These current excitations are inserted into the finite element analysis motor simulation for accurate analysis of the harmonic effects. The analysis summarizes the benefits of high-frequency devices such as gallium nitride (GaN) in comparison to insulated gate bipolar transistors (IGBT) in terms of torque ripple and motor efficiency on a drive-cycle

Design as interactions of problem framing and problem solving: a formal and empirical basis for problem framing in design

In this thesis, I present, illustrate and empirically validate a novel approach to modelling and explaining design process. The main outcome of this work is the formal definition of the problem framing, and the formulation of a recursive model of framing in design. The model (code-named RFD), represents a formalisation of a grey area in the science of design, and sees the design process as a recursive interaction of problem framing and problem solving. The proposed approach is based upon a phenomenon introduced in cognitive science and known as (reflective) solution talkback. Previously, there were no formalisations of the knowledge interactions occurring within this complex reasoning operation. The recursive model is thus an attempt to express the existing knowledge in a formal and structured manner. In spite of rather abstract, knowledge level on which the model is defined, it is a firm step in the clarification of design process. The RFD model is applied to the knowledge-level description of the conducted experimental study that is annotated and analysed in the defined terminology. Eventually, several schemas implied by the model are identified, exemplified, and elaborated to reflect the empirical results. The model features the mutual interaction of predicates ‘specifies’ and ‘satisfies’. The first asserts that a certain set of explicit statements is sufficient for expressing relevant desired states the design is aiming to achieve. The validity of predicate ‘specifies’ might not be provable directly in any problem solving theory. A particular specification can be upheld or rejected only by drawing upon the validity of a complementary predicate ‘satisfies’ and the (un-)acceptability of the considered candidate solution (e.g. technological artefact, product). It is the role of the predicate ‘satisfies’ to find and derive such a candidate solution. The predicates ‘specifies’ and ‘satisfies’ are contextually bound and can be evaluated only within a particular conceptual frame. Thus, a solution to the design problem is sound and admissible with respect to an explicit commitment to a particular specification and design frame. The role of the predicate ‘acceptable’ is to compare the admissible solutions and frames against the ‘real’ design problem. As if it answered the question: “Is this solution really what I wanted/intended?” Furthermore, I propose a set of principled schemas on the conceptual (knowledge) level with an aim to make the interactive patterns of the design process explicit. These conceptual schemas are elicited from the rigorous experiments that utilised the structured and principled approach to recording the designer’s conceptual reasoning steps and decisions. They include the refinement of an explicit problem specification within a conceptual frame; the refinement of an explicit problem specification using a re-framed reference; and the conceptual re-framing (i.e. the identification and articulation of new conceptual terms) Since the conceptual schemas reflect the sequence of the ‘typical’ decisions the designer may make during the design process, there is no single, symbol-level method for the implementation of these conceptual patterns. Thus, when one decides to follow the abstract patterns and schemas, this abstract model alone can foster a principled design on the knowledge level. It must be acknowledged that for the purpose of computer-based support, these abstract schemas need to be turned into operational models and consequently suitable methods. However, such operational perspective was beyond the time and resource constraints placed on this research

Academic Performance as a Predictor of Student Growth in Achievement and Mental Motivation During an Engineering Design Challenge in Engineering and Technology Education

The purpose of this correlational research study was to determine if students’ academic success was correlated with: (a) the student change in achievement during an engineering design challenge; and (b) student change in mental motivation toward solving problems and critical thinking during an engineering design challenge. Multiple experimental studies have shown engineering design challenges increase student achievement and attitude toward learning, but conflicting evidence surrounded the impact on higher and lower academically achieving students. A high school classroom was chosen in which elements of engineering design were purposefully taught. Eleventh-grade student participants represented a diverse set of academic backgrounds (measured by grade point average [GPA]). Participants were measured in terms of achievement and mental motivation at three time points. Longitudinal multilevel modeling techniques were employed to identify significant predictors in achievement growth and mental motivation growth during the school year. Student achievement was significantly correlated with science GPA, but not math or communication GPA. Changes in achievement score over time are not significantly correlated with science, math, or communication. Mental motivation was measured by five subscales. Mental focus was correlated with math and science GPA. Mental focus increases over time were negatively correlated with science GPA, which indicated that the initial score differential (between higher and lower science GPA students) was decreased over time. Learning orientation and cognitive integrity were not correlated with GPA. Creative problem solving was correlated with science GPA, but gains over time were not correlated with GPA. Scholarly rigor was correlated with science GPA, but change over time was not correlated with GPA