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41	Stanovení hydrodynamického zatížení přelévané mostovky s využitím numerických simulací / Determination of hydrodynamic load acting on overflowed bridge deck using numerical simulations Stoklas, Jan Unknown Date (has links) The subjekt of this diploma thesis is a quantification of hydrodynamic load of the overflow bridge deck using numerical simulations. The simulation was performe for eight different discharges, which correspond to different degrees of inundation of the bridge deck using different turbulent models in Flow-3D software. The result of the calculation are the values of the hydrodynamic load– the horizontal force, the vertical force and the moment acting on the bridge deck. Furthermore, drag coefficient, lift coefficient and moment coefficient were quantified. Finally, the results of turbulent models were compared with each other and with result of physical experimental testing.
42	Drooped Strings and Dressed Mesons: Implications of Gauge-Gravity Duality for the Properties of Heavy-Light Mesons at Finite Temperature Moomaw, Peter 22 December 2009 (has links) No description available. Particle Physics Physics AdS/CFT holography String Theory quark-gluon plasma mesons drag coefficient heavy-light mesons Gauge-Gravity duality meson spectrum Drooped string Drooping string Dressed meson kinematics, finite temperature
43	Aerodynamic Analysis of Conventional and Spherical Tires Pakala, Akshay Kumar January 2020 (has links) No description available. Aerospace Engineering Applied Mathematics Educational Software Engineering Experiments Fluid Dynamics Mathematics Mechanical Engineering Theoretical Mathematics conventional tires Turbulent flow k-epsilon model k-epsilon realizable model k-transition turbulence model drag force drag coefficient lift coefficient aerodynamics
44	Geometric Uncertainty Analysis of Aerodynamic Shapes Using Multifidelity Monte Carlo Estimation Triston Andrew Kosloske (15353533) 27 April 2023 (has links) <p>Uncertainty analysis is of great use both for calculating outputs that are more akin to real<br> flight, and for optimization to more robust shapes. However, implementation of uncertainty<br> has been a longstanding challenge in the field of aerodynamics due to the computational cost<br> of simulations. Geometric uncertainty in particular is often left unexplored in favor of uncer-<br> tainties in freestream parameters, turbulence models, or computational error. Therefore, this<br> work proposes a method of geometric uncertainty analysis for aerodynamic shapes that miti-<br> gates the barriers to its feasible computation. The process takes a two- or three-dimensional<br> shape and utilizes a combination of multifidelity meshes and Gaussian process regression<br> (GPR) surrogates in a multifidelity Monte Carlo (MFMC) algorithm. Multifidelity meshes<br> allow for finer sampling with a given budget, making the surrogates more accurate. GPR<br> surrogates are made practical to use by parameterizing major factors in geometric uncer-<br> tainty with only four variables in 2-D and five in 3-D. In both cases, two parameters control<br> the heights of steps that occur on the top and bottom of airfoils where leading and trailing<br> edge devices are attached. Two more parameters control the height and length of waves<br> that can occur in an ideally smooth shape during manufacturing. A fifth parameter controls<br> the depth of span-wise skin buckling waves along a 3-D wing. Parameters are defined to<br> be uniformly distributed with a maximum size of 0.4 mm and 0.15 mm for steps and waves<br> to remain within common manufacturing tolerances. The analysis chain is demonstrated<br> with two test cases. The first, the RAE2822 airfoil, uses transonic freestream parameters<br> set by the ADODG Benchmark Case 2. The results show a mean drag of nearly 10 counts<br> above the deterministic case with fixed lift, and a 2 count increase for a fixed angle of attack<br> version of the case. Each case also has small variations in lift and angle of attack of about<br> 0.5 counts and 0.08◦, respectively. Variances for each of the three tracked outputs show that<br> more variability is possible, and even likely. The ONERA M6 transonic wing, popular due<br> to the extensive experimental data available for computational validation, is the second test<br> case. Variation is found to be less substantial here, with a mean drag increase of 0.5 counts,<br> and a mean lift increase of 0.1 counts. Furthermore, the MFMC algorithm enables accurate<br> results with only a few hours of wall time in addition to GPR training. </p> Stochastic analysis and modelling geometric uncertainty drag coefficient predictions Gaussian process regression method multifidelity model monte carlo approach to variability
45	Development of Sensors and Microcontrollers for Underwater Robots Jebelli, Ali January 2014 (has links) Nowadays, small autonomous underwater robots are strongly preferred for remote exploration of unknown and unstructured environments. Such robots allow the exploration and monitoring of underwater environments where a long term underwater presence is required to cover a large area. Furthermore, reducing the robot size, embedding electrical board inside and reducing cost are some of the challenges designers of autonomous underwater robots are facing. As a key device for reliable operation-decision process of autonomous underwater robots, a relatively fast and cost effective controller based on Fuzzy logic and proportional-integral-derivative method is proposed in this thesis. It efficiently models nonlinear system behaviors largely present in robot operation and for which mathematical models are difficult to obtain. To evaluate its response, the fault finding test approach was applied and the response of each task of the robot depicted under different operating conditions. The robot performance while combining all control programs and including sensors was also investigated while the number of program codes and inputs were increased. AUV Autonomous Underwater Vehicles ADC Analog to Digital Converter Cd Drag coefficient of the object DOF Degree of Freedom KT Thrust coefficient Fd Force of drag g Acceleration of gravity h Height of the fluid above the object KQ Torque coefficient mb Buoyant mass NN Neural network P Proportional controller PI Proportional-integral controller PID ρ Density of the fluid Q Torque of the propeller ROV Remotely Operated Vehicles RPM Speed of the motor or propeller SDO Serial data output SMC Sliding mode control Measured thrust T° Temperature v AV Advance speed of the propeller

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