The realism of virtual surgery through a surgical simulator depends largely on the precision and reliability of the haptic device. The quality of perception depends on the design of the haptic device, which presents a complex design task due to the multi-criteria and conflicting character of the functional and performance requirements. In the model-based evaluation of the performance criteria of a haptic device, the required computational resources increase with the complexity of the device structure as well as with the increased level of detail that is created in the detail design phases. Due to uncertain requirements and a significant knowledge gap, the design task is fuzzy and more complex in the early design phases. The goal of this thesis is to propose a situated, i.e., flexible, scalable and efficient, methodology for multi-objective and multi-disciplinary design optimization of high-performing 6-DOF haptic devices. The main contributions of this thesis are: 1. A model-based and simulation-driven engineering design methodology and a flexible pilot framework are proposed for design optimization of high-performing haptic devices. The multi-disciplinary design optimization method was utilized to balance the conflicting criteria/requirements of a multi-domain design case and to solve the design optimization problems concurrently. 2. A multi-tool framework is proposed. The framework integrates metamodel-based design optimization with complementary engineering tools from different software vendors, which was shown to significantly reduce the total computationally effort. 3. The metamodeling methods and sampling sizes for specific performance indices found from case studies were shown to be applicable and usable for several kinds of 6-degrees-of-freedom haptic devices. 4. The multi-tool framework and the assisting methodology were further developed to enable computationally efficient and situated design multi-objective optimization of high-performing haptic devices. The design-of-experiment (DOE) and metamodeling techniques are integrated with the optimization process in the framework as an option to solve the design optimization case with a process that depends on the present system complexity. / <p>QC 20171108</p>

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.

At the ballistic limit velocity of a soft armor target pack, the impact response has been shown to be decoupled in the thickness direction, with the initial few plies behaving in an inelastic fashion via off-axis failure modes such as transverse shear or diametral compression. Past the initial few layers, the remaining plies dissipate energy via membrane-like responses, which only involve in-plane tensile failure modes of the constituent fibers. Since these initial plies only contribute to energy absorption via inelastic kinetic energy transfer, previous studies have shown that these plies may be replaced with another material with other desirable properties, such as lower manufacturing costs or stab-resistance.<div>However, the methodology of determining these parameters is still largely empirical. Armor panels are typically impacted and the shot outcomes subsequently evaluated in order to achieve a quantitative ballistic performance for the panel. Additionally, the ballistic performance is usually determined with respect to a particular projectile. Several models have been proposed to provide an efficient method of predicting ballistic limit determination, but results are sometimes difficult to translate across different projectile-target pairs.<br></div><div>The main research direction in the first volume looking at soft armor impact failure modes and design optimization is obviously of immediate relevance to this dissertation. We start off with an examination of the different types of failure modes that impact on fibrous armors may yield. Subsequently, building on these concepts, we take a deeper look into how different impact parameters cause different failure modes,and we end with a discussion of how the armor panel may be designed around these different failure modes. Although some rudimentary analytical and modeling efforts have been put forth, the current work places more emphasis heavily on experimental techniques and observations, as is the nature of the work typically produced by our research group.<br></div><div><br></div>

This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-09-2-3938. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government. / This work seeks to develop multidisciplinary design optimization (MDO) methods to find the optimal design of a particular aircraft called an Efficient Supersonic Air Vehicle (ESAV). This is a long-range military bomber type of aircraft that is to be designed for high speed (supersonic) flight and survivability. The design metric used to differentiate designs is minimization of the take-off gross weight. The usefulness of MDO tools, rather than compartmentalized design practices, in the early stages of the design process is shown. These tools must be able to adequately analyze all pertinent physics, simultaneously and collectively, that are important to the aircraft of interest. Low-fidelity and higher-fidelity ESAV MDO frameworks have been constructed. The analysis codes in the higher-fidelity framework were validated by comparison with the legacy B-58 supersonic bomber aircraft. The low-fidelity framework used a computationally expensive process that utilized a large design of computer experiments study to explore its design space. This resulted in identifying an optimal ESAV with an arrow wing planform. Specific challenges to designing an ESAV not addressed with the low-fidelity framework were addressed with the higher-fidelity framework. Specifically, models to characterize the effects of the low-observable ESAV characteristics were required. For example, the embedded engines necessitated a higher-fidelity propulsion model and engine exhaust-washed structures discipline. Low-observability requirements necessitated adding a radar cross section discipline. A relatively less costly computational process utilizing successive NSGA-II optimization runs was used for the higher-fidelity MDO. This resulted in an optimal ESAV with a trapezoidal wing planform. The NSGA-II optimizer considered arrow wing planforms in early generations during the process, but these were later discarded in favor of the trapezoidal planform. Sensitivities around this optimal design were computed using the well-known ANOVA method to characterize the surrounding design space. The lower and higher fidelity frameworks could not be combined in a mixed-fidelity optimization process because the low-fidelity was not faithful enough to the higher-fidelity analysis results. The low-fidelity optimum was found to be infeasible according to the higher-fidelity framework and vice versa. Therefore, the low-fidelity framework was not capable of guiding the higher-fidelity framework to the eventual trapezoidal planform optimum. / Air Force Research Laboratory / Ph. D.

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Modeling and Simulation is a crucial element of the aerospace engineering design pro- cess because it allows designers to thoroughly test their solution before investing in the resources to create it. The Horizon Simulation Framework (HSF) v3.0 is an aerospace modeling and simulation tool that allows the user to verify system level requirements in the early phases of the design process. A low fidelity model of the system that is created by the user is exhaustively tested within the built-in Day-in-the-Life simulator to provide useful information in the form of failed requirements, system bottle necks and leverage points, and potential schedules of operations. The model can be stood up quickly with Extended Markup Language (XML) input files or can be customly created with Python Scripts that interact with the framework at runtime. The goal of the work presented in this thesis is to progress HSF from v2.3 to v3.0 in order to take advantage of current software development technologies. This includes converting the codebase from C++ and Lua scripting to C♯ and Python Scripting. The particulars of the considerations, benefits, and implementation of the new framework are discussed in detail. The simulation data and performance run time of the new framework were compared to that of the old framework. The new framework was found to produce similar data outputs with a faster run time.

Model-Based Systems Engineering in industry has been constantly increasing its presence within the aerospace industry. SysML is one such MBSE tool that shows complex system organization and relationships. The Horizon Simulation Framework is another MBSE tool, created by Cal Poly students, that gives users the ability to run “day-in-the-life” simulations of systems. Finding a way to link these two tools could allow systems engineers to reap the benefits of both. This thesis investigates the background and design process involved with developing the code that can convert an output file generated in SysML, into a format specifically made for the Horizon Simulation Framework. The goal was to create an interface that can allow users to model a system in SysML, and analyze the model and verify system requirements using HSF. Another goal was to expand the capabilities of the Horizon Simulation Framework by designing and develop a module that would allow users to define and analyze system-level requirements. To evaluate the effectiveness of both codes, the Aeolus example case was used. A SysML model of the system was created as the product of another thesis; SysML based CubeSat Model Design and Integration with the Horizon Simulation Framework. The Aeolus SysML model was converted and used as input in an HSF simulation. The SysML model simulation data was compared against those of the original test case. To test the requirement module, system level requirements were formulated within the Aeolus system and run in simulation, providing an analysis of the results. The results of the analysis confirmed a successful conversion of the SysML model into an equivalent HSF model and a successful analysis of system-level requirements.

Interplanetary space exploration has thus far consisted of single, expensive spacecraft missions. Mission costs are particularly high on missions to the outer planets and while invaluable, finite budgets limit our ability to perform extensive and frequent investigations of the planets. Planetary systems such as Jupiter and Saturn provide extremely complex exploration environments with numerous targets of interest. Exploring these targets in addition to the main planet requires multiple fly-bys and long mission timelines. In LEO, CubeSats have changed the exploration paradigm, offering a fast and low cost alternative to traditional space vehicles. This new mission development philosophy has the potential to significantly change the economics of interplanetary exploration and a number of missions are being developed to utilize CubeSat class spacecraft beyond earth orbit (e.g., NEAScout, Lunar Ice Cube, Marco and BioSentinel). This paper takes the CubeSat philosophical approach one step further by investigating the potential for small satellite swarms to provide extensive studies of the Saturn system. To do this, an architecture was developed to best replicate the Cassini Primary Mission science objectives using swarms of CubeSats. Cassini was chosen because of its complexity and it defines a well-understood baseline to compare against. The paper outlines the overall mission architecture developed and provides a feasible initial design for the spacecraft in the architecture. The number of swarms needed, number of CubeSats per swarm, size of the CubeSats, overall science output and estimated mission cost are all presented. Additional science objectives beyond Cassini's capabilities are also proposed. Significant scientific returns can be achieved by the swarm based architecture and the risk tolerance afforded by the utilization of large numbers of low-cost sensor carriers. This study found a potential architecture that could reduce the cost of replicating Cassini by as much as 63%. The results of this investigation are not constrained to Saturn and can be easily translated to other targets such as Uranus, Neptune or the asteroid belt.

Powering spacecraft with electric propulsion is becoming more common, especially in CubeSat-class satellites. On account of the risk of spacecraft interactions, it is important to have robust analysis and modeling tools of electric propulsion engines, particularly of the plasma plume. The Navier-Stokes equations used in classic continuum computational fluid dynamics do not apply to the rarefied plasma, and therefore another method must be used to model the flow. A good solution is to use the DSMC method, which uses a combination of particle modeling and statistical methods for modeling the simulated molecules. A DSMC simulation known as SINATRA has been developed with the goal to model electric propulsion plumes. SINATRA uses an octree mesh, is written in C++, and is designed to be expanded by further research. SINATRA has been initially validated through several tests and comparisons to theoretical data and other DSMC models. This thesis examines expanding the functionality of SINATRA to simulate charged particles and make SINATRA a DSMC-PIC hybrid. The electric potential is calculated through a 7-point 3D stencil on the mesh nodes and solved with a Gauss-Seidel solver. It is validated through test cases of charged particles to demonstrate the accuracy and capabilities of the model. An ambipolar diffusion test case is compared to a neutral diffusion case and the electric field is shown to stabilize the diffusion rate. A steady state flow test case shows the simulation is able to stabilize and solve the electric potential for a plume-like scenario. It includes additional features to simplify further research including a comprehensive user manual, industry-standard version control, text file inputs, GUI control, and simple parallelism of the simulation. Compilation and execution are standardized to be simple and platform independent to allow longevity of the code base. Finally, the execution bottlenecks of linking particles to cells and particle moving were removed to reduce the simulation time by 95%.

The CubeSat Astronomy Network is a proposed system of multiple CubeSat spacecraft capable of performing follow-up observations of astronomical targets of interest. The system is intended to serve as a space-borne platform that can complement existing systems utilized for astronomical research by undergraduate and high school students. Much research and development work has been performed to develop model-based system engineering methodologies and products for CubeSat missions, including the Horizon Simulation Framework. The Horizon Simulation Framework enables the development of system models using the Extended Markup Language (XML), and its simulation program can generate system simulations over model-specified timespans. System requirements and constraints, as well as subsystem dependencies and functions, can also be directly specified in these models. Previous work using the framework has been performed to characterize “day-in-the-life” operations for Earth-observing spacecraft. A similar goal is intended for modeling the CubeSat Astronomy Network: simulating mission operations during nominal conditions to validate system and subsystem requirements. By developing this model, system and subsystem requirements derived in the course of preliminary design for the Network can be analyzed, modelled, and evaluated for feasibility. These results can then be used to inform design decisions related to system architecture and concept of operations at the early stages of design, while the models themselves can grow and mature alongside project development and be re-used for future design work.