Dynamic data driven applications systems (DDDAS) for multidisciplinary optimisation (MDO)

[ES] Nowadays, the majority of optimisation processes that are followed to obtain new optimum designs involve expensive simulations that are costly and time comsuming. Besides, designs involving aerodynamics are usually highly constrained in terms of infeasible geometries to be avoided so that it is really important to provide the optimisers effective datum or starting points that enable them to reach feasible solutions. This MSc Thesis aims to continue the development of an alternative design methodology applied to a 2D airfoil at a cruise flight condition by combining concepts of Dynamic Data Driven Application Systems (DDDAS) paradigm with Multiobjec- tive Optimisation. For this purpose, a surrogate model based on experimental data has been used to run a multiobjective optimisation and the given optimum designs have been considered as starting points for a direct optimisation, saving number of evaluations in the process. Throughout this work, a technique for retrieving experi- mental airfoil lift and drag coefficients was conducted. Later, a new parametrisation technique using Class-Shape Transformation (CST) was implemented in order to map the considered airfoils into the design space. Then, a response surface model considering Radial Basis Functions (RBF) and Kriging approaches was constructed and the multiobjective optimisation to maximise lift and minimise drag was under- taken using stochastic algorithms, MOTSII and NSGA. Alternatively, a full direct optimisation from datum airfoil and a direct optimisation from optimum surrogate- based optimisation designs were performed with Xfoil and the results were compared. As an outcome, the developed design methodology based on the combination of surrogate-based and direct optimisation was proved to be more effective than a single full direct optimisation to make the whole process faster by saving number of evaluations. In addition, further work guidelines are presented to show potential directions in which to expand and improve this methodology.Patón Pozo, PJ. (2016). Dynamic data driven applications systems (DDDAS) for multidisciplinary optimisation (MDO). Universitat Politècnica de València. http://hdl.handle.net/10251/142210TFG

Applications of Dynamic Mode Decomposition and Sparse Reconstruction in the Data-Driven Dynamic Analysis of Physical Systems

Recent advancements in data collection methods and equipment have resulted in a huge increase in the amount of data collected by observing various types of physical phenomena. Regardless of the amount of data collected, it is well known for many physical systems, the so-called information rank of the collected data is much lower than the rank of the data itself. This usually means the data may be represented sparsely in terms of a properly-chosen basis. This realization has led to methods for storing large amounts of data through compression by sacrificing negligible data quality. More importantly, with the advent of compressed sensing techniques, using an appropriate representation basis and sampling technique, it is now possible to sample data far below the Shannon-Nyquist limit thus speeding up data acquisition and also reducing the complexity of data-acquisition hardware. In this research, we explore the application of various modern data analysis techniques such as proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), compressed sensing, and Kalman filter and smoother in the data-driven analysis of dynamic systems with many degrees of freedom. This research has resulted in four novel methods. The first method is developed for denoising and spatial resolution enhancement of 4D-Flow MRI data based on POD and sparse reconstruction. The second method combines DMD and compressed sensing and takes discrete cosine transform (DCT) as the representation basis for dynamic denoising and gappy data reconstruction in 2D. The third method is a fast and parameter-free dynamic denoising method which combines a reduced-order model (ROM), a Kalman filter and smoother, and a DMD-based forward model. The fourth method is developed for reconstructing a 2D incompressible flow field by taking sparse measurements from the Fourier domain. As the reconstruction basis, a custom divergence-free set of basis vectors are derived and implemented

Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field

Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins

Digital Twins (DT) are essentially Dynamic Data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach.Comment: 10 pages, 1 figure, submitted to the 4th International Conference on InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022

On data-driven modelling and terminal sliding mode control of dynamic systems with applications

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis addresses critical issues in system modelling and control with some applications to robotics and automation. The main content is divided into three parts, namely data-driven identification, fast terminal sliding mode control alongside underactuated crane control, and robotic pointing system for thermoelastic stress analysis (TSA). The first part is devoted to system modelling. A dynamic model can be identified from data collected (input and output data from the plant). However, the data obtained is often affected by noise. Hence, such algorithms for modelling the plant should be robust enough to accurately predict the dynamic behaviour of the system in the presence of noisy data. Taking this into account, this thesis focuses on subspace-based identification methods, and proposes an effective algorithm based on the Least-Square Support Vector Regression (LS-SVR). In the proposed algorithm, the system identification is formulated as a regression problem to be solved by applying multi-output LS-SVR. The second part of the thesis deals with the control of underactuated systems which are subjected to uncertainties including nonlinearities, parameter variations, and external disturbances. Among many control methodologies, Sliding Mode Control (SMC) is known for its strong robustness. Conventional SMC usually consists of linear sliding surfaces, which can only guarantee the asymptotic stability of the system, and hence, takes infinite time to reach the equilibrium. Requirements of finite-time stability can be fulfilled by adding the sliding function with a fractional nonlinear term to achieve the Terminal Sliding Mode, and using another attractor can lead to a faster response, called the Fast Terminal Sliding Mode (FTSM). FTSM is theoretically promising but it has limited application in real-time systems. This thesis is devoted to bridging this practical gap by developing a FTSM controller for underactuated mechanical systems. The third part of this thesis presents the applications of the proposed LS-SVR based identification algorithm and FTSM control scheme. Here, theoretical developments are implemented on a laboratorial gantry crane and an optical pointing system, respectively. Performance of both LS-SVR identification and FTSM control is verified through extensive simulation and experimental results. Notably, the work for this thesis has been applied to the RobotEye, an industrial pointing system of Ocular Robotics Pty. Ltd., which consists of a mirror integrated with other sensors such as laser sensors and vision cameras for robotic navigation or structural health monitoring with TSA

Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

DYNAMIC MODE DECOMPOSITION APPROACH FOR ESTIMATING THE SHAPE OF A CABLE

This study investigates the dynamic behavior of a flexible cable with heterogeneous stiffness using a data-driven approach. The study aims to develop accurate models describing intricate structures with rigid or flexible components. To achieve this, reflective markers were attached to the cable at equal spacing, and the motion was manually excited and captured using an 8-camera setup and OptiTrack\u27s Motive software. The cable displacement data at the marker locations were used as initial conditions for various Dynamic Mode Decomposition (DMD) models. The performance of the data- driven cable model is compared against the performance of the DMD modeling approach, fitting the dynamics of single- and multi-degree of freedom systems with added white noise. In this work, authors have considered using time delays and Wavelets-based DMD. The study found that the Wavelet-based DMD (WDMD) model was the most accurate method for reconstructing the response of the cable in the test cases. The researchers suggest that this data-driven approach can be applied to predict the dynamic behavior of non-linear systems, with potential applications in civil engineering, aerospace, and robotics. Overall, this study presents a promising approach to developing accurate models of complex structures with rigid or flexible components. The findings of this study can be valuable for designing structures that can withstand dynamic loads and vibrations