Yes / Advancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems. / Jaguar Land Rover funded research “Intelligent Personalised Powertrain Healthcare” 2016-2019

Through this research, a robust aircraft design methodology is developed for analysis and optimization of the Air Vehicle (AV) segment of Unmanned Aerial Vehicle (UAV) systems. The analysis functionality of the AV design is integrated with a Genetic Algorithm (GA) to form an integrated Multi-disciplinary Design Optimization (MDO) methodology for optimal AV design synthesis. This research fills the gap in integrated subsonic fixed-wing UAV AV MDO methods. No known single methodology captures all of the phenomena of interest over the wide range of UAV families considered here. Key advancements include: 1) parametric Low Reynolds Number (LRN) airfoil aerodynamics formulation, 2) UAV systems mass properties definition, 3) wing structural weight methods, 4) self-optimizing flight performance model, 5) automated geometry algorithms, and 6) optimizer integration. Multiple methods are provided for many disciplines to enable flexibility in functionality, level of detail, computational expediency, and accuracy. The AV design methods are calibrated against the High-Altitude Long-Endurance (HALE) Global Hawk, Medium-Altitude Endurance (MAE) Predator, and Tactical Shadow 200 classes, which exhibit significant variations in mission performance requirements and scale from one another. Technology impacts on the design of the three UAV classes are evaluated from a representative system technology year through 2025. Avionics, subsystems, aerodynamics, design, payloads, propulsion, and structures technology trends are assembled or derived from a variety of sources. The technology investigation serves the purposes of validating the effectiveness of the integrated AV design methods and to highlight design implications of technology insertion through future years. Flight performance, payload performance, and other attributes within a vehicle family are fixed such that the changes in the AV designs represent technology differences alone, and not requirements evolution. The optimizer seeks to minimize AV design gross weight for a given mission requirement and technology set. All three UAV families show significant design gross weight reductions as technology improves. The predicted design gross weight in 2025 for each class is: 1) 12.9% relative to the 1994 Global Hawk, 2) 6.26% relative to the 1994 Predator, and 3) 26.3% relative to the 2000 Shadow 200. The degree of technology improvement and ranking of contributing technologies differs among the vehicle families. The design gross weight is sensitive to technologies that directly affect the non-varying weights for all cases, especially payload and avionics/subsystems technologies. Additionally, the propulsion technology strongly affects the high performance Global Hawk and Predator families, which have high fuel mass fractions relative to the Tactical Shadow 200 family. The overall technology synergy experienced 10-11 years after the initial technology year is 6.68% for Global Hawk, 7.09% for Predator, and 4.22% for the Shadow 200, which means that the technology trends interact favorably in all cases. The Global Hawk and Shadow 200 families exhibited niche behavior, where some vehicles attained higher aerodynamic performance while others attained lower structural mass fractions. The high aerodynamic performance Global Hawk vehicles had high aspect ratio wings with sweep, while the low structural mass fraction vehicles had straight, relatively low aspect ratios and smaller wing spans. The high aerodynamic performance Shadow 200 vehicles had relatively low wing loadings and large wing spans, while the lower structural mass fraction counterparts sought to minimize physical size. / Ph. D.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance. / Master of Science

This study highlights the aeroelastic behavior of very flexible truss-braced wing (TBW) aircraft designs obtained through a multidisciplinary design optimization (MDO) framework. Several improvements to previous analysis methods were developed and validated. Firstly, a flutter constraint was developed and the effects of the constraint on the MDO of TBW transport aircraft for both medium-range and long-range missions were studied while minimizing the take-off gross weight (TOGW) and the fuel burn as the objective functions. Results show that when the flutter constraint is applied at 1.15 times the dive speed, it imposes a 1.5% penalty on the take-off weight and a 5% penalty on the fuel consumption while minimizing these two objective functions for the medium-range mission. For the long-range mission, the penalties imposed by the similar constraint on the minimum TOGW and minimum fuel burn designs are 3.5% and 7.5%, respectively. Importantly, the resulting TBW designs are still superior to equivalent cantilever designs for both of the missions as they have both lower TOGW and fuel burn. However, a relaxed flutter constraint applied at 1.05 times the dive speed can restrict the penalty on the TOGW to only 0.3% and that on the fuel burn to 2% for minimizing both the objectives, for the medium-range mission. For the long-range mission, a similar relaxed constraint can reduce the penalty on fuel burn to 2.9%. These observations suggest further investigation into active flutter suppression mechanisms for the TBW aircraft to further reduce either the TOGW or the fuel burn. Secondly, the effects of a variable-geometry raked wingtip (VGRWT) on the maneuverability and aeroelastic behavior of passenger aircraft with very flexible truss-braced wings (TBW) were investigated. These TBW designs obtained from the MDO environment while minimizing fuel burn resemble a Boeing 777-200 Long Range (LR) aircraft both in terms of flight mission and aircraft configuration. The VGRWT can sweep forward and aft relative to the wing with the aid of a Novel Control Effector (NCE) mechanism. Results show that the VGRWT can be swept judiciously to alter the bending-torsion coupling and the movement of the center of pressure of wing. Such behavior of the VGRWT is applied to both achieve the required roll control as well as to increase flutter speed, and thus, enable the operation of TBW configurations which have up to 10% lower fuel burn than comparable optimized cantilever wing designs. Finally, a transonic aeroelastic analysis tool was developed which can be used for conceptual design in an MDO environment. Routine transonic aeroelastic analysis require expensive CFD simulations, hence they cannot be performed in an MDO environment. The present approach utilizes the results of a companion study of CFD simulations performed offline for the steady Reynolds Averaged Navier Stokes equations for a variety of airfoil parameters. The CFD results are used to develop a response surface which can be used in the MDO environment to perform a Leishman-Beddoes (LB) indicial functions based flutter analysis. A reduced-order model (ROM) is also developed for the unsteady aerodynamic system. Validation of the strip theory based aeroelastic analysis with LB unsteady aerodynamics and the computational efficiency and accuracy of the ROM is demonstrated. Finally, transonic aeroelastic analysis of a TBW aircraft designed for the medium-range flight mission similar to a Boeing 737 next generation (NG) with a cruise Mach number of 0.8 is presented. The results show the potential of the present approach to perform a more accurate, yet inexpensive, flutter analysis for MDO studies of transonic transport aircraft which are expected to undergo flutter at transonic conditions. / Ph. D.

With rapid growth in the complexity of large scale engineering systems, the application of multidisciplinary analysis and design optimization (MDO) in the engineering design process has garnered much attention. MDO addresses the challenge of integrating several different disciplines into the design process. Primary challenges of MDO include computational expense and poor scalability. The introduction of a distributed, collaborative computational environment results in better utilization of available computational resources, reducing the time to solution, and enhancing scalability. SORCER, a Java-based network-centric computing platform, enables analyses and design studies in a distributed collaborative computing environment. Two different optimization algorithms widely used in multidisciplinary engineering design---VTDIRECT95 and QNSTOP---are implemented on a SORCER grid. VTDIRECT95, a Fortran 95 implementation of D. R. Jones' algorithm DIRECT, is a highly parallelizable derivative-free deterministic global optimization algorithm. QNSTOP is a parallel quasi-Newton algorithm for stochastic optimization problems. The purpose of integrating VTDIRECT95 and QNSTOP into the SORCER framework is to provide load balancing among computational resources, resulting in a dynamically scalable process. Further, the federated computing paradigm implemented by SORCER manages distributed services in real time, thereby significantly speeding up the design process. Results are included for an aircraft design application. / Master of Science

A global/local framework for multidisciplinary optimization of generalized aircraft wing structure has been developed. The concept of curvilinear stiffening members (spars, ribs and stiffeners) has been applied in the optimization of a wing structure. A global wing optimization framework EBF3WingOpt, which integrates the static aeroelastic, flutter and buckling analysis, has been implemented for exploiting the optimal design at the wing level. The wing internal structure is optimized using curvilinear spars and ribs (SpaRibs). A two-step optimization approach, which consists of topology optimization with shape design variables and size optimization with thickness design variables, is implemented in EBF3WingOpt. A local panel optimization EBF3PanelOpt, which includes stress and buckling evaluation criteria, is performed to optimize the local panels bordered by spars and ribs for further structural weight saving. The local panel model is extracted from the global finite element model. The boundary conditions are defined on the edges of local panels using the displacement fields obtained from the global model analysis. The local panels are optimized to satisfy stress and buckling constraints. Stiffened panel with curvilinear stiffeners is implemented in EBF3PanelOpt to improve the buckling resistance of the local panels. The optimization of stiffened panels has been studied and integrated in the local panel optimization. EBF3WingOpt has been applied for the optimization of the wing structure of the Boeing N+2 supersonic transport wing and NASA common research model (CRM). The optimization results have shown the advantage of curvilinear spars and ribs concept. The local panel optimization EBF3PanelOpt is performed for the NASA CRM wing. The global-local optimization framework EBF3GLWingOpt, which incorporates global wing optimization module EBF3WingOpt and local panel optimization module EBF3PanelOpt, is developed using MATLAB and Python programming to integrate several commercial software: MSC.PATRAN for pre and post processing, MSC.NASTRAN for finite element analysis. An approximate optimization method is developed for the stiffened panel optimization so as to reduce the computational cost. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing which proves the methodology's application scaling with medium fidelity FEM analysis. Both the global wing design variables and local panel design variables are optimized to minimize the wing weight at an acceptable computational cost. / Ph. D.

This study utilizes Multidisciplinary Design Optimization (MDO) techniques to explore the effectiveness of the truss-braced (TBW) and strut-braced (SBW) wing configurations in enhancing the performance of medium range, transonic transport aircraft. The truss and strut-braced wing concepts synergize structures and aerodynamics to create a planform with decreased weight and drag. Past studies at Virginia Tech have found that these configurations can achieve significant performance benefits when compared to a cantilever aircraft with a long range, Boeing 777-200ER-like mission. The objective of this study is to explore these benefits when applied to a medium range Boeing 737-800NG-like aircraft with a cruise Mach number of 0.78, a 3,115 nautical mile range, and 162 passengers. Results demonstrate the significant performance benefits of the SBW and TBW configurations. Both configurations exhibit reduced weight and fuel consumption. Configurations are also optimized for 1990's or advanced technology aerodynamics. For the 1990's technology minimum TOGW cases, the SBW and TBW configurations achieve reductions in the TOGW of as much as 6% with 20% less fuel weight than the comparable cantilever configurations. The 1990's technology minimum fuel cases offer fuel weight reductions of about 13% compared to the 1990's technology minimum TOGW configurations and 11% when compared to the 1990's minimum fuel optimized cantilever configurations. The advanced aerodynamics technology minimum TOGW configurations feature an additional 4% weight savings over the comparable 1990's technology results while the advanced technology minimum fuel cases show fuel savings of 12% over the 1990's minimum fuel results. This translates to a 15% reduction in TOGW for the advanced technology minimum TOGW cases and a 47% reduction in fuel consumption for the advanced technology minimum fuel cases when compared to the simulated Boeing 737-800NG. It is found that the TBW configurations do not offer significant performance benefits over the comparable SBW designs. / Master of Science

This study presents a new Python-based novel framework, in a distributed computing environment for multidisciplinary design optimization (MDO) called DELWARX. DELWARX also includes a transonic flutter analysis approach that is computationally very efficient, yet accurate enough for conceptual design and optimization studies. This transonic flutter analysis approach is designed for large aspect-ratio wings and attached flow. The framework employs particle swarm optimization with penalty functions for exploring optimal Transonic Truss Braced Wing (TTBW) aircraft design, similar to the Boeing 737-800 type of mission with a cruise Mach of 0.8, a range of 3115 n miles, and 162 passengers, with two different objective functions, the fuel weight and the maximum take-off gross weight, while satisfying all the required constraints. Proper memory management is applied to effectively address memory-related issues, which are often a limiting factor in distributed computing. The parallel implementation in MDO using 60 processors allowed a reduction in the wall-clock time by 96% which is around 24 times faster than the optimization using a single processor. The results include a comparison of the TTBW designs for the medium-range missions with and without the flutter constraint. Importantly, the framework achieves extremely low computation times due to its parallel optimization capability, retains all the previous functionalities of the previous Virginia Tech MDO framework, and replaces the previously employed linear flutter analysis with a more accurate nonlinear transonic flutter computation. These features of DELWARX are expected to facilitate a more accurate MDO study for innovative transport aircraft configurations operating in the transonic flight regime. High-fidelity CFD simulation is performed to verify the result obtained from extended Strip theory based aerodynamic analysis method. An approach is presented to develop a deep neural network (DNN)-based surrogate model for fast and accurate prediction of flutter constraints in multidisciplinary design optimization (MDO) of Transonic Truss Braced Wing (TTBW) aircraft in the transonic region. The integration of the surrogate model in the MDO framework shows lower computation times than the MDO with nonlinear flutter analysis. The developed surrogate models can predict the optimum design. The wall-clock time of the design analysis method was reduced by 1500 times as compared to the result implemented in the previous framework, DELWARX. / Doctor of Philosophy / The current study presents DELWARX, a novel Python-based framework specifically engineered for the optimization of aircraft designs, with a primary focus on enhancing the performance of aircraft wings under transonic conditions (speeds approaching the speed of sound). This advancement is particularly pertinent for aircraft with a mission analogous to the Boeing 737-800, which necessitates a harmonious balance between speed, range, passenger capacity, and fuel efficiency. A salient feature of DELWARX is its adeptness in analyzing and optimizing wing flutter, a critical issue where wings may experience hazardous vibrations at certain velocities. This is particularly vital for wings characterized by a high aspect ratio (wings that are long and narrow), presenting a substantial challenge in the domain of aircraft design. DELWARX surpasses preceding methodologies by implementing a sophisticated computational technique known as particle swarm optimization, analogous to the collective movement observed in bird flocks, integrated with penalty functions that serve to exclude design solutions that fail to meet predefined standards. This approach is akin to navigating through a maze with specific pathways rendered inaccessible due to certain constraints. The efficiency of DELWARX is markedly enhanced by its ability to distribute computational tasks across 60 processors, achieving a computation speed that is 24 times faster than that of a single-processor operation. This distribution results in a significant reduction of overall computation time by 96%, representing a substantial advancement in processing efficiency. Further, DELWARX introduces an enhanced level of precision in its operations. It supplants former methods of flutter analysis with a more sophisticated, nonlinear approach tailored for transonic speeds. Consequently, the framework's predictions and optimization strategies for aircraft wing designs are imbued with increased reliability and accuracy. Moreover, DELWARX also integrates a Deep Neural Network (DNN), an advanced form of artificial intelligence, to swiftly and precisely predict flutter constraints. This integration manifests as a highly intelligent system capable of instantaneously estimating the performance of various designs, thereby expediting the optimization process. DELWARX employs high-fidelity Computational Fluid Dynamics (CFD) simulations to verify its findings. These simulations utilize intricate models to simulate the aerodynamics of air flow over aircraft wings, thereby ensuring that the optimized designs are not only theoretically sound but also pragmatically effective. In conclusion, DELWARX represents a significant leap in the field of multidisciplinary design optimization. It offers a robust and efficient tool for the design of aircraft wings, especially in the context of transonic flight. This framework heralds a new era in the optimization of aircraft designs, enabling more innovative and efficient solutions in the aerospace industry.

In recent years, research has been conducted for alternative commercial transonic aircraft design configurations, such as the strut- braced wing and the truss-braced wing aircraft designs, in order to improve aircraft performance and reduce the impact of aircraft emissions as compared to a typical cantilever wing design. Research performed by Virginia Tech in conjunction with NASA Langley Research Center shows that these alternative configurations result in 20% or more reduction in fuel consumption, and thus emissions. Another option to reduce the impact of emissions on the environment is to reduce the aircraft cruise altitude, where less nitrous oxides are released into the atmosphere and contrail formation is less likely. The following study was performed using multidisciplinary design optimization (MDO) in ModelCenterTM for cantilever wing, strut-braced wing, and truss-braced wing designs and optimized for minimum takeoff gross weight at 7730 NM range and minimum fuel weight for 7730 and 4000 NM range at the following cruise altitudes: 25,000; 30,000; and 35,000 ft. For the longer range, both objective functions exhibit a large penalty in fuel weight and takeoff gross weight due to the increased drag from the fixed fuselage when reducing cruise altitude. For the shorter range, there was a slight increase in takeoff gross weight even though there was a large increase in fuel weight for decreased cruise altitudes. Thus, the benefits of reducing cruise altitude were offset by increased fuel weight. Either a two-jury truss-braced wing or telescopic strut could be studied to reduce the fuel penalty. / Master of Science

A significant improvement can be achieved in the performance of transonic transport aircraft using Multidisciplinary Design Optimization (MDO) by implementing truss-braced wing concepts in combination with other advanced technologies and novel design innovations. A considerable reduction in drag can be obtained by using a high aspect ratio wing with thin airfoil sections and tip-mounted engines. However, such wing structures could suffer from a significant weight penalty. Thus, the use of an external strut or a truss bracing is promising for weight reduction. Due to the unconventional nature of the proposed concept, commonly available wing weight equations for transport aircraft will not be sufficiently accurate. Hence, a bending material weight calculation procedure was developed to take into account the influence of the strut upon the wing weight, and this was coupled to the Flight Optimization System (FLOPS) for total wing weight estimation. The wing bending material weight for single-strut configurations is estimated by modeling the wing structure as an idealized double-plate model using a piecewise linear load method. Two maneuver load conditions 2.5g and -1.0g factor of safety of 1.5 and a 2.0g taxi bump are considered as the critical load conditions to determine the wing bending material weight. From preliminary analyses, the buckling of the strut under the -1.0g load condition proved to be the critical structural challenge. To address this issue, an innovative design strategy introduces a telescoping sleeve mechanism to allow the strut to be inactive during negative g maneuvers and active during positive g maneuvers. Also, more wing weight reduction is obtained by optimizing the strut force, a strut offset length, and the wing-strut junction location. The best configuration shows a 9.2% savings in takeoff gross weight, an 18.2% savings in wing weight and a 15.4% savings in fuel weight compared to a cantilever wing counterpart. / Master of Science