Although additive manufacturing technologies are often referred to as "3D printing," the family of technologies typically deposit material on a layer-by-layer basis. For material extrusion (ME) in particular, the deposition process results in weak inter- and intra-layer bonds that reduce mechanical performance in those directions. Despite this shortcoming, ME offers the opportunity to specifically and preferentially align the reinforcement of a composite material throughout a part by customizing the toolpath. Recent developments in multi-axis deposition have demonstrated the ability to place material outside of the XY-plane, enabling depositions to align to any 3D (i.e., non-planar) vector. Although mechanical property improvements have been demonstrated, toolpath planning capabilities are limited; the geometries and load paths are restricted to surface-based structures, rather than fully 3D load paths. By specifically planning deposition paths (roads) where the composite reinforcement is aligned to the load paths within a structure, there is an opportunity for a step-change in the mechanical properties of ME parts. To achieve this goal for arbitrary geometries and load paths, the author presents a design and process planning workflow that concurrently optimizes the topology of the part and the toolpath used to fabricate it. The workflow i) identifies the optimal structure and road directions using topology optimization (TO), ii) plans roads aligned to those optimal directions, iii) orders those roads for collision-free deposition, and iv) translates that ordered set of roads to a robot-interpretable toolpath. A TO algorithm, capable of optimizing 3D material orientations, is presented and demonstrated in the context of 2D and 3D load cases. The algorithm achieved a 38% improvement in final solution compliance for a 3D Wheel problem relative to existing TO algorithms with planar orientation optimization considerations. Optimized geometries and their associated orientation fields were then propagated with the presented alignment-focused deposition path planner and conventional toolpath planners. The presented method resulted in a 97% correlation between the road directions and the orientation field, while the conventional methods only achieved 77%. A planar multi-load case was then fabricated using each of these methods and tested in both tension and bending; the presented alignment-focused method resulted in a 108.24% and 29.25% improvement in each load case, respectively. To evaluate the workflow in a multi-axis context, an inverted Wheel problem was optimized and processed by the workflow. The resulting toolpaths were then fabricated on a multi-axis deposition platform and mechanically evaluated relative to geometrically similar structures using a conventional toolpath planner. While the alignment in the multi-axis specimen was improved from the conventional method, the mechanical properties were reduced due to limitations of the multi-axis deposition platform. / Doctor of Philosophy / The material extrusion additive manufacturing process is widely used by hobbyists and industry professionals to produce demonstration parts, but the process is often overlooked for end-use, load bearing parts. This is due to the layer-by-layer fabrication method used to create the desired geometries; the bonding between layers is weaker than the direction material is deposited. If load paths acting on the printed structure travel across those layer interfaces, the part performance will decrease. Whereas gantry-based systems are forced into this layer-by-layer strategy, robotic arms allow the deposition head to rotate, which enables depositions to be placed outside of the XY-plane (i.e., the typical layer). If depositions are appropriately planned using this flexibility, the layer interfaces can be oriented away from the load paths such that all of the load acts on the (stronger) depositions. Although this benefit has been demonstrated in literature, the existing methods for planning robotic toolpaths have limits on printability; certain load paths and geometries cannot be printed due to concerns that the robotic system will collide with the part being printed. This work focuses on increasing the generality of these toolpath planning methods by enabling any geometry and set of load paths to be printed. This is achieved through three objectives: i) identify the load paths within the structure, ii) plan roads aligned to those load paths, iii) order those roads such that collisions will not occur. The author presents and evaluates a design workflow that addresses each of these three objectives by simultaneously optimizing the geometry of the part as well as the toolpath used to fabricate it. Planar and 3D load cases are optimized, processed using the presented workflow, and then fabricated on a multi-axis deposition platform. The resulting specimens are then mechanically tested and compared to specimens fabricated using conventional toolpath planning methods.

Recent transonic airliner designs have generally converged upon a common cantilever low-wing configuration. It is unlikely that further large strides in performance are possible without a significant departure from the present design paradigm. One such alternative configuration is the strut-braced wing, which uses a strut for wing bending load alleviation, allowing increased aspect ratio and reduced wing thickness to increase the lift to drag ratio. The thinner wing has less transonic wave drag, permitting the wing to unsweep for increased areas of natural laminar flow and further structural weight savings. High aerodynamic efficiency translates into reduced fuel consumption and smaller, quieter, less expensive engines with lower noise pollution. A Multidisciplinary Design Optimization (MDO) approach is essential to understand the full potential of this synergistic configuration due to the strong interdependency of structures, aerodynamics and propulsion. NASA defined a need for a 325-passenger transport capable of flying 7500 nautical miles at Mach 0.85 for a 2010 date of entry into service. Lockheed Martin Aeronautical systems (LMAS), our industry partner, placed great emphasis on realistic constraints, projected technology levels, manufacturing and certification issues. Numerous design challenges specific to the strut-braced wing became apparent through the interactions with LMAS, and modifications had to be made to the Virginia Tech code to reflect these concerns, thus contributing realism to the MDO results. The SBW configuration is 9.2-17.4% lighter, burns 16.2-19.3% less fuel, requires 21.5-31.6% smaller engines and costs 3.8-7.2% less than equivalent cantilever wing aircraft. / Master of Science

When designing aircraft wings shapes, it is important to ensure that the flight envelope does not overlap with regions of flutter or Limit Cycle Oscillation (LCO). A quick assessment of these dynamic aeroelastic for various design candidates is key to successful design. Flutter based design requires the sensitivity of flutter parameters to be known with the respect of design parameters. Traditionally, frequency domain based methods have been used to predict flutter characteristics and its sensitivity. However, this approach is only applicable for linear or linearized models and cannot be applied to systems undergoing LCO or other nonlinear effects. Though the time accurate approach can be implemented to overcome this problem, it is computationally expensive. Also, the unsteady adjoint formulation for sensitivity analysis, requires the state and adjoint variables to be stored at every time step, which prohibitively increases the memory requirement. In this work, these problems have been overcome by implementing a time spectral method based approach to compute flutter onset, LCOs and their design sensitivities in a computationally efficient manner. The time spectral based formulation approximates the solution as a discrete Fourier series and directly solves for the periodic steady state, leading to a steady formulation. This can lead to the time spectral approach to be faster than the time accurate approach. More importantly, the steady formulation of the time spectral method also eliminates the memory issues faced by the unsteady adjoint formulation. The time spectral based flutter/LCO prediction method was used to predict flutter and LCO characteristics of the AGARD 445.6 wing and pitch/plunge airfoil section with NACA 64A010 airfoil. Furthermore, the adjoint based sensitivity analysis was used to carry out aerodynamic shape optimization, with an objective of maximizing the flutter velocity with and without constraints on the drag coefficient. The resulting designs show significant increase in the flutter velocity and the corresponding LCO velocity profile. The resulting airfoils display a greater sensitivity to the transonic shock which in turn leads to greater aerodynamic damping and hence leading to an increase in flutter velocity. / Doctor of Philosophy / When designing aircrafts, dynamic aeroelastic effects such as flutter onset and Limit Cycle Oscillations need to considered. At low enough flight speeds, any vibrations arising in the aircraft structure are damped out by the airflow. However, beyond a certain flight speed, instead of damping out the vibrations, the airflow accentuates these vibrations. This is known as flutter and it can lead to catastrophic structural failure. Hence, during the aircraft design phase, it must be ensured that the aircraft would not experience flutter during the flight conditions. One of the contribution of this work has been to come up with a fast and accurate method to predict flutter using computational modelling. Depending on the scenario, it is also possible that during flutter, the vibrations in the structure increase to a certain amplitude before leveling off due to interaction of non-linear physics. This condition is known as limit cycle oscillation. While they can arise due to different kinds of non-linearities, in this work the focus has been on aerodynamic non-linearities arising from shocks in transonic flight conditions. While limit cycle oscillations are undesirable as they can cause structural fatigue, they can also save the aircraft from imminent structural fracture and hence it is important to accurately predict them as well. The main advantage of the method developed in this work is that the same method can be used to predict both the flutter onset condition and limit cycle oscillations. This is a novel development as most of the traditional approaches in dynamic aeroelasticity cannot predict both the effects. The developed flutter/LCO prediction method has then been used in design with the goal of achieving superior flutter characteristics. In this study, the shape of the baseline airfoil is changed with the goal of increasing the flutter velocity. This enables the designed system to fly faster without addition of weight. Since the design has been carried out using gradient based optimization approach, an efficient way to compute the gradient needs to be used. Traditional approaches to compute the gradient, such as Finite Difference Method, have computational cost proportional to the number of design variables. This becomes a problem for shape design optimization, where a large number of design variables are required. This has been overcome by developing an adjoint based sensitivity analysis method. The main advantage of the adjoint based sensitivity analysis is that it its computational cost is independent of the number of design variables, and hence a large number of design variables can be accommodated. The developed flutter/LCO prediction and adjoint based sensitivity analysis framework was used to carry out shape design for a pitch/plunge airfoil section. The objective of the design process was to maximize the flutter onset velocity with and without constraints on drag. The resulting optimized airfoils showed significant increase in the flutter velocity.

The Horizon Simulation Framework (HSF) is an open-source community driven mod- eling and simulation tool developed over 15 years by a lineage of Cal Poly graduate students. The tool excels in its flexibility to model an assortment of complex systems, with prebuilt modeling elements available for the simulation of space missions. A high-level simulation tool like HSF lends itself to an agile development cycle as system constraints can be quickly identified through day in the life simulation of the modeled system. The objective of the work presented in this thesis is to refactor the way in which several modeling elements are loaded in the simulation framework. A focus is placed on improving how relationships between various modeling elements are initialized to allow the flow of information between distant assets that was previously not possible. Further improvements were made to the framework with the objective of standardizing how information is communicated from user input files to locations in the framework that depend on the inputs. After implementing these updates, a demonstration scenario was created to validate the developments implemented.

No description available.

The primary objective of this study is to develop new computational methods for robust design optimization (RDO) and reliability-based design optimization (RBDO) of high-dimensional, complex engineering systems. Four major research directions, all anchored in polynomial dimensional decomposition (PDD), have been defined to meet the objective. They involve: (1) development of new sensitivity analysis methods for RDO and RBDO; (2) development of novel optimization methods for solving RDO problems; (3) development of novel optimization methods for solving RBDO problems; and (4) development of a novel scheme and formulation to solve stochastic design optimization problems with both distributional and structural design parameters. The major achievements are as follows. Firstly, three new computational methods were developed for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random inputs. The first method represents a novel integration of PDD of a multivariate stochastic response function and score functions, leading to analytical expressions of design sensitivities of the first two moments. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-SPA method, entailing the saddlepoint approximation (SPA) and score functions; and the PDD-MCS method, utilizing the embedded Monte Carlo simulation (MCS) of the PDD approximation and score functions. For all three methods developed, both the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Secondly, four new methods were developed for RDO of complex engineering systems. The methods involve PDD of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. The methods, depending on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompass direct, single-step, sequential, and multi-point single-step design processes. Thirdly, two new methods were developed for RBDO of complex engineering systems. The methods involve an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response for reliability analysis, a novel integration of AS-PDD and score functions for calculating the sensitivities of the failure probability with respect to design variables, and standard gradient-based optimization algorithms, resulting in a multi-point, single-step design process. The two methods, depending on how the failure probability and its design sensitivities are evaluated, exploit two distinct combinations built on AS-PDD: the AS-PDD-SPA method, entailing SPA and score functions; and the AS-PDD-MCS method, utilizing the embedded MCS of the AS-PDD approximation and score functions. In addition, a new method, named as the augmented PDD method, was developed for RDO and RBDO subject to mixed design variables, comprising both distributional and structural design variables. The method comprises a new augmented PDD of a high-dimensional stochastic response for statistical moment and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms, leading to a multi-point, single-step design process. The innovative formulations of statistical moment and reliability analysis, design sensitivity analysis, and optimization algorithms have achieved not only highly accurate but also computationally efficient design solutions. Therefore, these new methods are capable of performing industrial-scale design optimization with numerous design variables.

Multidisciplinary design optimization (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously for reasons of efficiency. When performing MDO, a large number of designs need to be rated. Detailed simulation models used to assess automotive design proposals are often computationally expensive to evaluate. A useful MDO process must distribute work to the groups involved and be computationally efficient. In this thesis, MDO methods are assessed in relation to the characteristics of automotive structural applications. Single-level optimization methods have a single optimizer, while multi-level optimization methods have a distributed optimization process. Collaborative optimization and analytical target cascading are possible choices of multi-level optimization methods for automotive structures. They distribute the design process, but are complex. One approach to handle the computationally demanding simulation models involves metamodel-based design optimization (MBDO), where metamodels are used as approximations of the detailed models during optimization studies. Metamodels can be created by individual groups prior to the optimization process, and therefore also offer a way of distributing work. A single-level optimization method in combination with metamodels is concluded to be the most straightforward way of implementing MDO into the development of automotive structures.

No description available.

In all common hydrostatic pumps, compressibility affects the commutation phases of the displacing chambers, as they switch their connection from/to the inlet to/from the outlet port, leading to pressure peaks, localized cavitation, additional port flow fluctuations and volumetric efficiency reduction. In common pumps, these effects are reduced by proper grooves that realizes gradual port area variation in proximity of these transition regions. This paper presents a method to automatically find the optimal designs of these grooves, taking as reference the case of external gear pumps. The proposed procedure does not assume a specific geometric morphology for the grooves, and it determines the best feasible designs through a multi-objective optimization procedure. A commercial gear pump is used to experimentally demonstrate the potentials of the proposed method, for a particular case aimed at reducing delivery flow oscillations.

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Recent studies have shown that considering proof test data in a Reliability Based Design Optimization (RBDO) environment can result in design improvement. Proof testing involves the physical testing of each and every component before it enters into service. Considering the proof test data as part of the RBDO process allows for improvement of the original design, such as weight savings, while preserving high reliability levels. Composite Over-Wrapped Pressure Vessels (COPV) is used as an example application of achieving weight savings while maintaining high reliability levels. COPVs are light structures used to store pressurized fluids in space shuttles, the international space station and other applications where they are maintained at high pressure for extended periods of time. Given that each and every COPV used in spacecraft is proof tested before entering service and any weight savings on a spacecraft results in significant cost savings, this thesis put forward an application of RBDO that accounts for proof test data in the design of a COPV. The method developed in this thesis shows that, while maintaining high levels of reliability, significant weight savings can be achieved by including proof test data in the design process. Also, the method enables a designer to have control over the magnitude of the proof test, making it possible to also design the proof test itself depending on the desired level of reliability for passing the proof test. The implementation of the method is discussed in detail. The evaluation of the reliability was based on the First Order Reliability Method (FORM) supported by Monte Carlo Simulation. Also, the method is implemented in a versatile way that allows the use of analytical as well as numerical (in the form of finite element) models. Results show that additional weight savings can be achieved by the inclusion of proof test data in the design process. / AFRIKAANSE OPSOMMING: Onlangse studies het getoon dat die gebruik van ontwerp spesifieke proeftoets data in betroubaarheids gebaseerde optimering (BGO) kan lei tot 'n verbeterde ontwerp. BGO behels vele aspekte in die ontwerpsgebied. Die toevoeging van proeftoets data in ontwerpsoptimering bring te weë; die toetsing van 'n ontwerp en onderdele voor gebruik, die aangepaste en verbeterde ontwerp en gewig-besparing met handhawing van hoë betroubaarsheidsvlakke. 'n Praktiese toepassing van die BGO tegniek behels die ontwerp van drukvatte met saamgestelde materiaal bewapening. Die drukvatontwerp is 'n ligte struktuur wat gebruik word in die berging van hoë druk vloeistowwe in bv. in ruimtetuie, in die internasionale ruimtestasie en in ander toepassings waar hoë druk oor 'n tydperk verlang word. Elke drukvat met saamgestelde materiaal bewapening wat in ruimtevaartstelsels gebruik word, word geproeftoets voor gebruik. In ruimte stelselontwerp lei massa besparing tot 'n toename in loonvrag. Die tesis beskryf 'n optimeringsmetode soos ontwikkel en gebaseer op 'n BGO tegniek. Die metode word toegepas in die ontwerp van drukvatte met saamgestelde materiaal bewapening. Die resultate toon dat die gebruik van proeftoets data in massa besparing optimering onderhewig soos aan hoë betroubaarheidsvlakke moontlik is. Verdermeer, die metode laat ook ontwerpers toe om die proeftoetsvlak aan te pas om sodoende by ander betroubaarheidsvlakke te toets. In die tesis word die ontwikkeling en gebruik van die optimeringsmetode uiteengelê. Die evaluering van betroubaarheidsvlakke is gebaseer op 'n eerste orde betroubaarheids-tegniek wat geverifieer word met talle Monte Carlo simulasie resultate. Die metode is ook so geskep dat beide analitiese sowel as eindige element modelle gebruik kan word. Ten slotte, word 'n toepassing getoon waar resultate wys dat die gebruik van die optimeringsmetode met die insluiting van proeftoets data wel massa besparing kan oplewer.