The purpose of this research is to develop and architect a preliminary multidisciplinary design optimization (MDO) tool that creates multiple types of generalized rocket fairing models. These models are sized relative to input geometric models and are analyzed and optimized, taking into account the primary objectives, namely the structural, thermal, and aerodynamic aspects of standard rocket flights. A variety of standard nose cone shapes is used as optimization proof of concept examples, being sized and compared to determine optimal choices based on the input specifications, such as the rocket body geometry and the specified trajectory paths. Any input models can be optimized to their respective best nose cone style or optimized to each of the cone styles individually, depending on the desired constraints. Two proof of concept example rocket model studies are included with varying sizes and speeds. Both have been optimized using the processes described to provide delineative instances into how results are improved and time saved. This is done by optimizing shape and thickness of the fairings while ascertaining if the remaining length downstream on the designated rocket model remains within specified stress and temperature ranges. The first optimized example exhibits a region of high stress downstream on the rocket body model that champions how these tools can be used to catch weaknesses and improve the overall integrity of a rocket design. The second example demonstrates how more established rocket designs can decrease their weight and drag through optimization of the fairing design.

Small electric uninhabited aerial vehicles (UAV) represent a rapidly expanding market requiring optimization in both efficiency and weight; efficiency is critical during cruise or loiter where the vehicle operates at part power for up to 99% of the mission time. Of the four components (battery, motor, propeller, and electronic speed controller (ESC)) of the electric propulsion system used in small UAVs, the ESC has no accepted performance model and almost no published performance data. To collect performance data, instrumentation was developed to measure electrical power in and out of the ESC using the two wattmeter method and current sense resistors; data was collected with a differential simultaneous data acquisition system. Performance of the ESC was measured under different load, commanded throttle, bus voltage, and switching frequency, and it was found that ESC efficiency decreases with increasing torque and decreasing bus voltage and does not vary much with speed and switching frequency. The final instrumentation was limited to low-voltage systems and error propagation calculations indicate a great deal of error at low power measurements; despite these limitations, an understanding of ESC performance appropriate for conceptual design of these systems was obtained. MODELING AND TEST OF THE EFFICIENCY OF ELECTRONIC SPEED CONTROLLERS FOR BRUSHLESS DC MOTORS

A systematic customer value assessment process (CVAP) was developed to give system engineering teams the capability to qualitatively and quantitatively assess customer values. It also provides processes and techniques used to create and identify alternatives, evaluate alternatives in terms of effectiveness, cost, and risk. The ultimate goal is to provide customers (or decision makers) with objective and traceable procurement recommendations. The creation of CVAP was driven by an industry need to provide ballistic missile defense (BMD) customers with a value proposition of contractors’ BMD systems. The information that outputs from CVAP can be used to guide BMD contractors in formulating a value proposition, which is used to steer customers to procure their BMD system(s) instead of competing system(s). The outputs from CVAP also illuminate areas where systems can be improved to stay relevant with customer values by identifying capability gaps. CVAP incorporates proven approaches and techniques appropriate for military applications. However, CVAP is adaptable and may be applied to business, engineering, and even personal every-day decision problems and opportunities. CVAP is based on the systems decision process (SDP) developed by Gregory S. Parnell and other systems engineering faculty at the Unites States Military Academy (USMA). SDP combines Value-Focused Thinking (VFT) decision analysis philosophy with Multi-Objective Decision Analysis (MODA) quantitative analysis of alternatives. CVAP improves SDP’s qualitative value model by implementing Quality Function Deployment (QFD), solution design implements creative problem solving techniques, and the qualitative value model by adding cost analysis and risk assessment processes practiced by the U.S DoD and industry. CVAP and SDP fundamentally differ from other decision making approaches, like the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), by distinctly separating the value/utility function assessment process with the ranking of alternatives. This explicit value assessment allows for straightforward traceability of the specific factors that influence decisions, which illuminates the tradeoffs involved in making decisions with multiple objectives. CVAP is intended to be a decision support tool with the ultimate purpose of helping decision makers attain the best solution and understanding the differences between the alternatives. CVAP does not include any processes for implementation of the alternative that the customer selects. CVAP is applied to ballistic missile defense (BMD) to give contractors ideas on how to use it. An introduction of BMD, unique BMD challenges, and how CVAP can improve the BMD decision making process is presented. Each phase of CVAP is applied to the BMD decision environment. CVAP is applied to a fictitious BMD example.

The Cal Poly CubeSat Laboratory (CPCL) is currently facing unprecedented engineering challenges—both technically and programmatically—due to the increasing cost and complexity of CubeSat flight missions. In responding to recent RFPs, the CPCL has been forced to find commercially available solutions to entire mission critical spacecraft subsystems such as propulsion and attitude determination & control, because currently no in-house options exist for consideration. The commercially available solutions for these subsystems are often extremely expensive and sometimes provide excessively good performance with respect to mission requirements. Furthermore, use of entire commercial subsystems detracts from the hands-on learning objectives of the CPCL by removing engineering responsibility from students. Therefore, if these particular subsystems can be designed, tested, and integrated in-house at Cal Poly, the result would be twofold: 1) the space of missions supportable by the CPCL under tight budget constraints will grow, and 2) students will be provided with unique, hands-on guidance, navigation, and control learning opportunities. In this thesis, the CPCL’s attitude determination and control system design and analysis toolkit is significantly improved to support in-house ADCS development. The toolkit—including the improvements presented in this work—is then used to complete the existing, partially complete CPCL ADCS design. To fill in missing gaps, particular emphasis is placed on guidance and control algorithm design and selection of attitude actuators. Simulation results show that the completed design is competitive for use in a large class of small satellite missions for which pointing accuracy requirements are on the order of a few degrees.

On-orbit satellite servicing is critical to maximizing space utilization and sustainability and is of growing interest for commercial, civil, and defense applications. Reliance on astronauts or anchored robotic arms for the servicing of next-generation large, complex space structures operating beyond Low Earth Orbit is impractical. Substantial literature has investigated the mission design and analysis of robotic servicing missions that utilize a single servicing satellite to approach and service a single target satellite. This motivates the present research to investigate a fleet of servicing satellites performing several operations for a large, central space structure. This research leverages a distributed control approach, implemented using the Horizon Simulation Framework (HSF), to develop a tool capable of integrated mission modeling and task scheduling for a servicing satellite fleet. HSF is a modeling and simulation framework for verification of system level requirements with an emphasis on state representations, modularity, and event scheduling. HSF consists of two major modules: the main scheduling algorithm and the system model. The distributed control architecture allocates processing and decision making for this multi-agent cooperative control problem across multiple subsystem models and the main HSF scheduling algorithm itself. Models were implemented with a special emphasis on the dynamics, control, trajectory constraints, and trajectory optimization for the servicing satellite fleet. The integrated mission modeling and scheduling tool was applied to a sample scenario in which a fleet of 3 servicing assets is tasked with performing 12 servicing activities for a large satellite in Geostationary Orbit. The tool was able to successfully determine a schedule in which all 12 servicing activities were completed in under 32 hours, subject to the fuel and trajectory constraints of the servicing assets.

<p>  </p> <p>With rising concerns over commercial aviation’s contribution to global carbon emissions, there exists a tremendous pressure on the aviation industry to find advanced technological solutions to reduce its share of CO2 emissions. Single-aisle (or narrowbody) aircraft are the biggest contributors to CO2 emissions by number of operations, insisting a need to reduce / eliminate their aircraft-level fuel consumption as soon as possible. A potential solution for this is to operate fully-electric single-aisle aircraft; however, the limitations of the current (and predicted future) battery technology is forcing the industry to explore hybrid-electric aircraft as a possible mid-term solution.</p> <p>Modeling hybrid-electric aircraft comes with its own challenges due to the presence of two different propulsion sources – gas turbine engines (powered by Jet-A fuel) and electric motors (powered by batteries). Since traditional sizing approaches and legacy sizing tools do not seem to work well for hybrid-electric aircraft, this work presents a “flight-mechanics-based” conceptual sizing tool for hybrid-electric aircraft, set up as a Multidisciplinary Design Optimization (MDO) toolbox. Some of the key features of the sizing tool include concurrently sizing the electric motors and downsizing the gas turbine engines while meeting the one-engine-inoperative (OEI) and top-of-climb constraints, and re-sizing the fuselage to account for the volumetric constraints associated with required batteries.</p> <p>Current work considers a parallel hybrid-electric single-aisle aircraft with a 900 nmi design range, with electric power augmentation (with electric motors operating at full throttle) available only for the takeoff and climb segments when sizing the aircraft. Four hybrid-electric propulsion technology cases are considered, and the resulting hybrid-electric aircraft show 15.0% to 22.5% reduction in fuel burn compared to a Boeing 737-800 aircraft.</p> <p>Another challenge with modeling hybrid-electric aircraft is determining their off-design performance characteristics (considering a different payload or mission range, or both). This work presents an energy management tool – set up as a nonlinear programming optimization problem – to minimize the fuel burn for a payload-range combination by identifying the optimal combination of throttle settings for the gas turbine engines and the electric motors during takeoff, climb, and cruise, along with identifying an optimal flight path. The energy management tool enables fuel savings of at least of 2%, with actual savings ranging from 142.1 lbs to 276.1 lbs per trip for a sample route (LGA–ORD) at a 80% load factor.</p> <p>Although the hybrid-electric aircraft sizing and performance analysis studies show encouraging results about the potential reduction in carbon emissions at an aircraft level, the future fleet-level carbon emissions are not expected to reduce proportionally to these aircraft level emission reductions. This work predicts the fleet-level environmental impacts of future single-aisle parallel hybrid-electric aircraft by modeling the behavior of a profit-seeking airline (with a mixture of conventional all Jet-A fuel burning and hybrid electric aircraft in its fleet) using the Fleet-Level Environmental Evaluation Tool (FLEET). FLEET’s model-based predictions rely upon historically-based information about US-touching airline routes and passenger demand served by US flag-carrier airlines from the Bureau of Transportation Statistics to initiate model-based predictions of future demand, aircraft fleet mix, and aircraft operations. Using the aircraft performance coefficients from the energy management tool to represent the behavior of a single-aisle parallel hybrid-electric aircraft, the FLEET simulation predicts the changes in the fleet-wide carbon emissions due to the introduction of this new aircraft in an airline fleet in the year 2035. By 2055, FLEET results predict that the fleet-wide CO2 emissions with hybrid-electric aircraft in the fleet mix are at least 1.2% lower than the fleet-wide CO2 emissions of a conventional (all Jet-A fuel burning) aircraft-only airline. The rather limited reduction in emissions is an attribute of the reduced range capability and higher operating cost of the hybrid-electric aircraft (relative to a conventional aircraft of similar size). This causes the airline to change the usage, acquisition and retirement of its conventional aircraft when hybrid-electric aircraft are available; this is most notable to serve passenger demand on certain predominantly single-aisle service routes that cannot be flown by the future single-aisle hybrid-electric aircraft. </p>

Multidisciplinary design optimization (MDO) requires many designs to be evaluated while searching for an optimum. As a result, the calculations done to evaluate the designs must be quick and simple to have a reasonable turn-around time. This makes aerodynamic calculations in the transonic regime difficult. Running computational fluid dynamics (CFD) calculations within the MDO code would be too computationally expensive. Instead, CFD is used outside the MDO to find two-dimensional aerodynamic properties of a chosen airfoil shape, BACJ, at a number of points over a range of thickness-to-chord ratios, free-stream Mach numbers, and lift coefficients. These points are used to generate surrogate models which can be used for the two-dimensional aerodynamic calculations required by the MDO computational design environment. Strip theory is used to relate these two-dimensional results to the three-dimensional wing. Models are developed for the center of pressure location, the lift curve slope, the wave drag, and the maximum allowable lift coefficient before buffet. These models have good agreement with the original CFD results for the airfoil. The models are integrated into the aerodynamic and aeroelastic sections of the MDO code. / Master of Science

A formal framework is developed and implemented in this research for preliminary rotorcraft design using IPPD methodology. All the technical aspects of design are considered including the vehicle engineering, dynamic analysis, stability and control, aerodynamic performance, propulsion, transmission design, weight and balance, noise analysis and economic analysis. The design loop starts with a detailed analysis of requirements. A baseline is selected and upgrade targets are identified depending on the mission requirements. An Overall Evaluation Criterion (OEC) is developed that is used to measure the goodness of the design or to compare the design with competitors. The requirements analysis and baseline upgrade targets lead to the initial sizing and performance estimation of the new design. The digital information is then passed to disciplinary experts. This is where the detailed disciplinary analyses are performed. Information is transferred from one discipline to another as the design loop is iterated. To coordinate all the disciplines in the product development cycle, Multidisciplinary Design Optimization (MDO) techniques e.g. All At Once (AAO) and Collaborative Optimization (CO) are suggested. The methodology is implemented on a Light Turbine Training Helicopter (LTTH) design. Detailed disciplinary analyses are integrated through a common platform for efficient and centralized transfer of design information from one discipline to another in a collaborative manner. Several disciplinary and system level optimization problems are solved. After all the constraints of a multidisciplinary problem have been satisfied and an optimal design has been obtained, it is compared with the initial baseline, using the earlier developed OEC, to measure the level of improvement achieved. Finally a digital preliminary design is proposed. The proposed design methodology provides an automated design framework, facilitates parallel design by removing disciplinary interdependency, current and updated information is made available to all disciplines at all times of the design through a central collaborative repository, overall design time is reduced and an optimized design is achieved.

The multi-million-dollar medical uniform industry has not utilized advancements in garment and textile technology that could positively impact the protection of healthcare professionals and patients. In most cases the uniforms meet basic requirements – they clothe the professional in a recognizable way. Little innovation in design, function and performance, has been applied to these garments. This is particularly evident in the case of the stereotypical white lab coat worn by many physicians, despite evidence indicating that these lab coats may carry contamination and play a role in the spread of deadly bacteria. Healthcare Associated Infections (HAIs) are among the most serious problems facing modern medical care, costing millions of lives and dollars annually worldwide. This research investigates the design and use of the physician’s lab coat – an immediately recognizable symbol of Western medicine. The research identifies the medical, functional, cultural and symbolic roles of the lab coat within the hospital environment and beyond, to the larger the global society. This thesis examines the extent to which the design of medical wear can impact the effect of hospital-acquired infections, support doctor/patient relationships and enhance the performance and behavior of the healthcare professional by envisioning a future lab coat which offers increased protection for physician and patient, aids in communication and enhances the performance of the doctor by utilizing digital technologies incorporated into the lab coat whereby the lab coat becomes the only tool necessary for the physician.

In this work, the data analysis of oscillating flapping fins is conducted for mathematical model. Data points of heave and surge force obtained by the CFD (Computational Fluid Dynamics) for different geometrical kinds of flapping fins. The fin undergoes a combination of vertical and angular oscillatory motion, while travelling at constant forward speed. The surge thrust and heave lift are generated by the combined motion of the flapping fins, especially due to the carrier vehicle’s heave and pitch motion will be investigated to acquire system identification with CFD data available while the fin pitching motion is selected as a function of fin vertical motion and it is imposed by an external mechanism. The data series applied to model unsteady lifting flow around the system will be employed to develop an optimization algorithm to establish an approximation transfer function model for heave force and obtain a predicting black box system with nonlinear theory for surge force with fin motion control synthesis.