Auto Taxi System Design for Aircraft

Nedávné studie předpovídají nárůst pasažérů využívajících leteckou dopravu. Tento trend bude vyžadovat zavedení nových leteckých linek, důsledkem čeho bude zhuštěn letový provoz s dopadem hlavně na nápor letišť v metropolitních oblastech. Automatizovaně řízení pojíždení letounu umožní menší rozestupy mezi jednotlivými linkami a zvýšení příletové a odletové kapacity letišť. Tato práce se zabývá návrhem modelu pohybu dopravního letounu po zemi s ohledem na různé provozní podmínky jako např.: stav povrchu vzletové a přistávací dráhy za různého počasí a lišící se provozní parametry letounu (tlak v pneumatikách, zatížení podvozků a pod.). Validace modelu byla založena na sledování poloměru zatáčky pro různe uhly natočení přední podvozkové nohy. Výsledky simulace byly validovany vzhledem k analytickému modelu Ackermanovy geometrie a na specifikační dokument od Boeingu určený pro plánovaní pohybu letounu na letišti. Výsledky prokázaly přesnost modelu a potvrdily jeho možné nasazení pro simulace v reálnem čase.Recent studies focused on the global airline industry predict a continuous growth of passenger numbers, which will stimulate an increased demand for modern sophisticated aircraft capable of precise operations at reduced separation minima. Automation systems, such as AutoTaxi, will allow for decreased ground separation standards and a subsequent increase of throughput at airports in metropolitan areas. This thesis deals with an AutoTaxi control system for a single-aisle passenger aircraft, such as Boeing 737 series, under different operational conditions. The implemented model considers varying runway characteristics due to the atmospheric conditions and different aircraft configurations. Detailed force and momentum equilibria analysis are presented in a form of equations of motion, which is essential in order to achieve high-precision simulation. The validation of the model was based on the turn radii comparison for multiple steering angles. Simulation results were subjected to a comparison with the analytical solution of the Ackerman drive for a tricycle vehicle and with turn radii specified in Airplane Characteristics for Airport Planning issued by Boeing. Obtained results confirm high-precision real-time simulation.

AeroWorks: Visual Identification of Aircraft Flight Regimes

Tato práce se zabývá vizuální detekcí režimů letounu. Obsahuje popis modelu prostorového pohybu letounu a způsoby vizualizace letových parametrů prostřednictvím letových přístrojů.  Práce podává návrh systému pro vizuální detekci režimů letounu. Navrhnutý systém postupně zpracovává každou snímku ve dvou fázích, nejdřív vykoná stabilizaci videa a následně se provede vizuální identifikace hodnot ukazatelů stavových veličin letounu. Stabilizace videa je založená na detekci zájmových bodů a výpočtu optického toku. Snímky jsou transformovány tak aby se co nejvíce překrývali a minimalizoval se tak nežádoucí pohyb kamery. Detekce hodnot, zobrazovaných na letových přístrojích, je založená na Houghově transformaci. V práci je zahrnut popis vytvořené aplikace, která na videozáznamu z pilotní kabiny letounu dokáže rozpoznat hodnoty zobrazené na specifikovaných letových přístrojích.This Bachelor thesis deals with the visual identification of an aircraft flight's regimes. It describes the spatial motion of an airplane along with the visualization of flight parameters and also proposes a system for a flight regime visual identification. The system processes the input video on a frame by frame basis in two steps. Initially, the video is being stabilized and the system subsequently proceeds in identification of flight related quantities describing the current flight state. Video stabilization is based on feature points detection and an optical flow calculation. Video frames are transformed in order to achieve sufficient consecutive frames overlap and thus to minimize the parasitic oscillations of the video acquisition system. Identification of values indicated by flight instruments is based on the Hough line transform approach. The thesis also includes a description of an application that analyzes a video from the cockpit of an aircraft and is able to recognize the instrument values displayed on specified flight instruments.

Application of deep and reinforcement learning to boundary control problems

The boundary control problem is a non-convex optimization and control problem in many scientific domains, including fluid mechanics, structural engineering, and heat transfer optimization. The aim is to find the optimal values for the domain boundaries such that the enclosed domain adhering to the governing equations attains the desired state values. Traditionally, non-linear optimization methods, such as the Interior-Point method (IPM), are used to solve such problems. This project explores the possibilities of using deep learning and reinforcement learning to solve boundary control problems. We adhere to the framework of iterative optimization strategies, employing a spatial neural network to construct well-informed initial guesses, and a spatio-temporal neural network learns the iterative optimization algorithm using policy gradients. Synthetic data, generated from the problems formulated in the literature, is used for training, testing and validation. The numerical experiments indicate that the proposed method can rival the speed and accuracy of existing solvers. In our preliminary results, the network attains costs lower than IPOPT, a state-of-the-art non-linear IPM, in 51\% cases. The overall number of floating point operations in the proposed method is similar to that of IPOPT. Additionally, the informed initial guess method and the learned momentum-like behaviour in the optimizer method are incorporated to avoid convergence to local minima

AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study

Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where electricity prices become more volatile and less predictable using classical approaches. Locational Marginal Pricing (LMP) pricing mechanism is used in many modern power markets, where the traditional approach utilizes optimal power flow (OPF) solvers. However, for large electricity grids this process becomes prohibitively time-consuming and computationally intensive. Machine learning solutions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. The study evaluates the performance of popular machine learning and deep learning models in predicting LMP on multiple electricity grids. The accuracy and robustness of these models in predicting LMP is assessed considering multiple scenarios. The results show that machine learning models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6\% error rate, highlighting the potential of machine learning models in LMP prediction for large-scale power models with the help of hardware solutions like multi-core CPUs and GPUs in modern HPC clusters

High-performance interior point methods: application to power grid problems

A software library for the solution of large-scale structured nonconvex optimization problems is presented in this work, with the purpose of accelerating the solution on single- core, multicore, or massively parallel high-performance distributed memory computing infrastructures. A large class of industrial and engineering problems possesses a particular structure, motivating the development of structure exploiting interior point methods. Interior point methods are among the most popular techniques for large-scale nonlinear optimization and their efficiency has attracted a lot of attention in recent years. Since the overall performance of interior point methods relies heavily on scalable sparse linear algebra solvers, this work thoroughly analyzes cutting-edge research based on the sparse linear algebra and structure exploiting methods presented over recent years, and further advances the performance by inspecting the structure of the underlying linear systems, resulting in an additional computational time and memory savings. The primal-dual interior point framework is applied for the solution of optimal power flow problems, a class of optimization problems attracting increasing attention in power system research, operations, and planning. Optimal power flow involves large-scale nonconvex optimization problems with a number of variables and constraints ranging up to hundreds of millions depending on the grid resolution and specific problem formulation. The robustness and reliability of interior point methods is investigated for different optimal power flow formulations for a wide range of realistic power grid networks. Furthermore, the object-oriented parallel and distributed scalable solver is implemented and applied to large-scale problems solved on a daily basis for the secure transmission and distribution of electricity in modern power grids. Similarly, an efficient algorithm is investigated for optimal power flow spanning long time horizons. Using computational studies from security constrained and multiperiod optimal power flow problems, the robustness and scalability of the structure exploiting approach is demonstrated

Comparison of Non-Gated vs. ECG-gated CT Angiography of Fontan Circulation in Patients with Implanted Stents in Pulmonary Branches

Background: Motion artifacts may degrade CT examination of Fontan pathway and hinder accurate diagnosis of in-stent restenosis. Purpose: We retrospectively compared ECG-gated multi-detector computed tomography (CT) with non-ECG-gated CT in order to demonstrate whether or not one of the methods should be preferred. Method: The study included 13 patients with surgically reconstructed Fontan pathway. A total of 16 CT examinations were performed between February 2010 and November 2015.The incidence of motion artifacts in Fontan pathway and pulmonary branches were analysed subjectively by two readers. The effective dose for each examination was calculated. Results: Just in one non-gated CT examination was evidence of motion artifact in distal part of left pulmonary artery. The mean normalized effective radiation dose was 2.33 mSv (±0.62) for the non-ECG-gated scans and 4.55 mSv (±0.85) for the ECG-gated scans (p ≤ 0.05). Conclusion: Non-gated CT angiography with single phase reconstruction significantly reduces radiation dose without loss of image quality compared with ECG-gated CT angiography