Application of non-linear system identification approaches to modelling, analysis, and control of fluid flows.

Flow control has become a topic of great importance for several applications, ranging from commercial aircraft, to intercontinental pipes and skyscrapers. In these applications, and many more, the interaction with a fluid flow can have a significant influence on the performance of the system. In many cases the fluids encountered are turbulent and detrimental to the latter. Several attempts have been made to solve this problem. However, due to the non-linearity and infinite dimensionality of fluid flows and their governing equations, a complete understanding of turbulent behaviour and a feasible control approach has not been obtained. In this thesis, model reduction approaches that exploit non-linear system identification are applied using data obtained from numerical simulations of turbulent three-dimensional channel flow, and two-dimensional flow over the backward facing step. A multiple-input multiple-output model, consisting of 27 sub-structures, is obtained for the fluctuations of the velocity components of the channel flow. A single-input single-output model for fluctuations of the pressure coefficient, and two multiple-input single-output models for fluctuations of the velocity magnitude are obtained in flow over the BFS. A non-linear model predictive control strategy is designed using identified one- and multi-step ahead predictors, with the inclusion of integral action for robustness. The proposed control approach incorporates a non-linear model without the need for expensive non-linear optimizations. Finally, a frequency domain analysis of unmanipulated turbulent flow is perfumed using five systems. Higher order generalized frequency response functions (GFRF) are computed to study the non-linear energy transfer phenomena. A more detailed investigation is performed using the output FRF (OFRF), which can elucidate the contribution of the n-th order frequency response to the output frequency response

Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

During the continuous evolution of one organism's ancestry, its genes accumulate extensive experiences and knowledge, enabling newborn descendants to rapidly adapt to their specific environments. Motivated by this observation, we propose a novel machine learning paradigm Learngene to enable learning models to incorporate three key characteristics of genes. (i) Accumulating: the knowledge is accumulated during the continuous learning of an ancestry model. (ii) Condensing: the extensive accumulated knowledge is condensed into a much more compact information piece, i.e., learngene. (iii) Inheriting: the condensed learngene is inherited to make it easier for descendant models to adapt to new environments. Since accumulating has been studied in well-established paradigms like large-scale pre-training and lifelong learning, we focus on condensing and inheriting, which induces three key issues and we provide the preliminary solutions to these issues in this paper: (i) Learngene Form: the learngene is set to a few integral layers that can preserve significance. (ii) Learngene Condensing: we identify which layers among the ancestry model have the most similarity as one pseudo descendant model. (iii) Learngene Inheriting: to construct distinct descendant models for the specific downstream tasks, we stack some randomly initialized layers to the learngene layers. Extensive experiments across various settings, including using different network architectures like Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) on different datasets, are carried out to confirm four advantages of Learngene: it makes the descendant models 1) converge more quickly, 2) exhibit less sensitivity to hyperparameters, 3) perform better, and 4) require fewer training samples to converge

Development of an EMG-based Muscle Health Model for Elbow Trauma Patients

Musculoskeletal (MSK) conditions are a leading cause of pain and disability worldwide. Rehabilitation is critical for recovery from these conditions and for the prevention of long-term disability. Robot-assisted therapy has been demonstrated to provide improvements to stroke rehabilitation in terms of efficiency and patient adherence. However, there are no wearable robot-assisted solutions for patients with MSK injuries. One of the limiting factors is the lack of appropriate models that allow the use of biosignals as an interface input. Furthermore, there are no models to discern the health of MSK patients as they progress through their therapy. This thesis describes the design, data collection, analysis, and validation of a novel muscle health model for elbow trauma patients. Surface electromyography (sEMG) data sets were collected from the injured arms of elbow trauma patients performing 10 upper-limb motions. The data were assessed and compared to sEMG data collected from the patients\u27 contralateral healthy limbs. A statistical analysis was conducted to identify trends relating the sEMG signals to muscle health. sEMG-based classification models for muscle health were developed. Relevant sEMG features were identified and combined into feature sets for the classification models. The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Classification models based on individual motions achieved cross-validation accuracies of 48.2--79.6%. Following feature selection and optimization of the models, cross-validation accuracies of up to 82.1% were achieved. This work suggests that there is a potential for implementing an EMG-based model of muscle health in a rehabilitative elbow brace to assess patients recovering from MSK elbow trauma. However, more research is necessary to improve the accuracy and the specificity of the classification models

N+3 Small Commercial Efficient and Quiet Transportation for Year 2030-2035

This study develops a future scenario that enables convenient point-to-point commercial air travel via a large network of community airports and a new class of small airliners. A network demand and capacity study identifies current and future air travel demands and the capacity of this new network to satisfy these demands. A current technology small commercial airliner is defined to meet the needs of the new network, as a baseline for evaluating the improvement brought about by advanced technologies. Impact of this new mode of travel on the infrastructure and surrounding communities of the small airports in this new N+3 network are also evaluated. Year 2030-2035 small commercial airliner technologies are identified and a trade study conducted to evaluate and select those with the greatest potential for enhancing future air travel and the study metrics. The selected advanced air vehicle concept is assessed against the baseline aircraft, and an advanced, but conventional aircraft, and the study metrics. The key technologies of the selected advanced air vehicle are identified, their impact quantified, and risk assessments and roadmaps defined

A smoothed finite element approach for computational fluid dynamics: applications to incompressible flows and fluid–structure interaction

In this paper the cell-based smoothed finite element method (CS-FEM) is introduced into two mainstream aspects of computational fluid dynamics: incompressible flows and fluid–structure interaction (FSI). The emphasis is placed on the fluid gradient smoothing which simply requires equal numbers of Gaussian points and smoothing cells in each four-node quadrilateral element. The second-order, smoothed characteristic-based split scheme in conjunction with a pressure stabilization is then presented to settle the incompressible Navier–Stokes equations. As for FSI, CS-FEM is applied to the geometrically nonlinear solid as usual. Following an efficient mesh deformation strategy, block-Gauss–Seidel procedure is adopted to couple all individual fields under the arbitrary Lagriangian–Eulerian description. The proposed solvers are carefully validated against the previously published data for several benchmarks, revealing visible improvements in computed results