Emulation of a dynamic broadcasting network with adaptive radiated power in a real scenario

Broadcasting networks are an efficient means for delivering media content to a high density of users, because their operational cost is almost independent of the size of their audience for a given coverage area. However, when the propagation conditions are better than the worst-case design, the energy efficiency is suboptimal. In this paper, we present the results of a trial to emulate the performance of a dynamic broadcasting network with adaptive radiated power in a real broadcasting scenario. We assess the radiated power of the broadcasting network in a Cuban environment by means of a monitoring device. The power consumption of the dynamic broadcasting network with adaptive radiated power is assessed and compared with traditional broadcasting for different implementation margins. To emulate the performance of the dynamic broadcasting network with adaptive radiated power, we consider a commercial Digital Terrestrial Multimedia Broadcast (DTMB) transmitter in Havana, Cuba. Testbed hardware is designed and developed to measure the fading with a commercial receiver and emulate the signal reception under adaptive power conditions. The dynamic broadcasting network performance is assessed following the general guidelines and techniques for the evaluation of digital terrestrial television broadcasting systems recommended in the ITU-R BT. 2035-2 report

IoT-based management platform for real-time spectrum and energy optimization of broadcasting networks

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra-High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT-based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB-IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time

An automated methodology for optimisation with respect to vessel manoeuvring

Over the past two decades the marine industry has been facing ever more stringent and radical environmental aims. These are not only been defined by the International Maritime Organisation (IMO), but also by individual countries defining limitations to greenhouse gases emitted by vessels. To combat this the industry has turned towards the use of more complex fluid analysis tools, both model scale tests and computational simulations. This analysis has not only focused on hull design, but also on hull roughness, hull propeller rudder interaction and the marine environment. The focus of this PhD research is to develop methodologies that can be utilised within the industry to optimise vessel performance. With this research optimisation aimed towards improving vessel manoeuvring, with focus away from the traditional nondimensional methodologies. To do so, this research aims to lean heavily on the utilisation of Reynolds Averaged Navier Stokes (RANS) method within Computational Fluid Dynamics (CFD). Towing tests have been considered the primary means of evaluating designs, not only for resistance but also for vessel motions. This includes the analysis forces and motions from both waves and manoeuvring tests. These tests however can be time consuming and financially costly. Therefore, the industry has begun to utilise CFD analysis at the early design stage as a low-cost and fast alternative. Not only this, but in recent years CFD has begun to achieve a level of accuracy matching towing tank tests. Due to these factors this research has a focus on the use of such computational means to improve vessel performance, with extensive validation against multiple towing tank tests. The research has a focus on developing and understanding that can be used to quickly evaluate a potential ship design’s manoeuvring characteristics. The methodology for simulating a captive harmonic test is presented, which has been validated against towing tank data conducted for the SIMMAN 2014 conference. This methodology is used in conjunction with a fully parametric hull form, developed within this research, to create and evolve equations used for ranking the hull forms manoeuvring performance. These unique equations are used in two optimisations cycles, one on the NPL hull and a further one on a custom hull to improve the vessels performance and efficiency. The optimum NPL hull forms are evaluated through a virtual turning circle manoeuvring simulation in CFD to quantify the improvements made through optimisation. This research developed a novel methodology for ranking manoeuvring characteristics that significantly reduced the overall optimisation time, as well as producing manoeuvring gains over 20% when evaluated in a simulated turning circle manoeuvre. In addition, the research has also presented best practice approaches for developing such a scheme and how to create a parametric setup that enables quick and accurate CFD simulations for complex manoeuvring simulations. This has been extensively validated against benchmark studies of the DTMB hull form from the SIMMAN 14 towing tank data.Over the past two decades the marine industry has been facing ever more stringent and radical environmental aims. These are not only been defined by the International Maritime Organisation (IMO), but also by individual countries defining limitations to greenhouse gases emitted by vessels. To combat this the industry has turned towards the use of more complex fluid analysis tools, both model scale tests and computational simulations. This analysis has not only focused on hull design, but also on hull roughness, hull propeller rudder interaction and the marine environment. The focus of this PhD research is to develop methodologies that can be utilised within the industry to optimise vessel performance. With this research optimisation aimed towards improving vessel manoeuvring, with focus away from the traditional nondimensional methodologies. To do so, this research aims to lean heavily on the utilisation of Reynolds Averaged Navier Stokes (RANS) method within Computational Fluid Dynamics (CFD). Towing tests have been considered the primary means of evaluating designs, not only for resistance but also for vessel motions. This includes the analysis forces and motions from both waves and manoeuvring tests. These tests however can be time consuming and financially costly. Therefore, the industry has begun to utilise CFD analysis at the early design stage as a low-cost and fast alternative. Not only this, but in recent years CFD has begun to achieve a level of accuracy matching towing tank tests. Due to these factors this research has a focus on the use of such computational means to improve vessel performance, with extensive validation against multiple towing tank tests. The research has a focus on developing and understanding that can be used to quickly evaluate a potential ship design’s manoeuvring characteristics. The methodology for simulating a captive harmonic test is presented, which has been validated against towing tank data conducted for the SIMMAN 2014 conference. This methodology is used in conjunction with a fully parametric hull form, developed within this research, to create and evolve equations used for ranking the hull forms manoeuvring performance. These unique equations are used in two optimisations cycles, one on the NPL hull and a further one on a custom hull to improve the vessels performance and efficiency. The optimum NPL hull forms are evaluated through a virtual turning circle manoeuvring simulation in CFD to quantify the improvements made through optimisation. This research developed a novel methodology for ranking manoeuvring characteristics that significantly reduced the overall optimisation time, as well as producing manoeuvring gains over 20% when evaluated in a simulated turning circle manoeuvre. In addition, the research has also presented best practice approaches for developing such a scheme and how to create a parametric setup that enables quick and accurate CFD simulations for complex manoeuvring simulations. This has been extensively validated against benchmark studies of the DTMB hull form from the SIMMAN 14 towing tank data

Administration of municipal bus transport with specific reference to the Durban City Council.

Thesis (M.Admin.)-University of Durban-Westville, 1991.No abstract available

Proceedings of the Sixth Hydraulics Conference

https://ir.uiowa.edu/uisie/1036/thumbnail.jp

Advanced data analysis for traction force microscopy and data-driven discovery of physical equations

The plummeting cost of collecting and storing data and the increasingly available computational power in the last decade have led to the emergence of new data analysis approaches in various scientific fields. Frequently, the new statistical methodology is employed for analyzing data involving incomplete or unknown information. In this thesis, new statistical approaches are developed for improving the accuracy of traction force microscopy (TFM) and data-driven discovery of physical equations. TFM is a versatile method for the reconstruction of a spatial image of the traction forces exerted by cells on elastic gel substrates. The traction force field is calculated from a linear mechanical model connecting the measured substrate displacements with the sought-for cell-generated stresses in real or Fourier space, which is an inverse and ill-posed problem. This inverse problem is commonly solved making use of regularization methods. Here, we systematically test the performance of new regularization methods and Bayesian inference for quantifying the parameter uncertainty in TFM. We compare two classical schemes, L1- and L2-regularization with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. We find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. We further combine the Bayesian L2 regularization with the computational speed of Fast Fourier Transform algorithms to develop a fully automated method for noise reduction and robust, standardized traction-force reconstruction that we call Bayesian Fourier transform traction cytometry (BFTTC). This method is made freely available as a software package with graphical user-interface for intuitive usage. Using synthetic data and experimental data, we show that these Bayesian methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Next, we employ our methodology developed for the solution of inverse problems for automated, data-driven discovery of ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic differential equations (SDEs). To find the equations governing a measured time-dependent process, we construct dictionaries of non-linear candidate equations. These candidate equations are evaluated using the measured data. With this approach, one can construct a likelihood function for the candidate equations. Optimization yields a linear, inverse problem which is to be solved under a sparsity constraint. We combine Bayesian compressive sensing using Laplace priors with automated thresholding to develop a new approach, namely automatic threshold sparse Bayesian learning (ATSBL). ATSBL is a robust method to identify ODEs, PDEs, and SDEs involving Gaussian noise, which is also referred to as type I noise. We extensively test the method with synthetic datasets describing physical processes. For SDEs, we combine data-driven inference using ATSBL with a novel entropy-based heuristic for discarding data points with high uncertainty. Finally, we develop an automatic iterative sampling optimization technique akin to Umbrella sampling. Therewith, we demonstrate that data-driven inference of SDEs can be substantially improved through feedback during the inference process if the stochastic process under investigation can be manipulated either experimentally or in simulations

Elk River Chain of Lakes Watershed Management Plan

The Elk River Chain of Lakes (ERCOL) watershed is located in northwestern Michigan in the Lower Peninsula. It is the largest sub-watershed of the Grand Traverse Bay watershed and covers over 500 square miles of land, has over 60 square miles of open water, and 200 miles of shoreline. The lakes and streams found in this watershed are some of the most pristine inland waterbodies in the entire country and provide a multitude of recreational and economic benefits for both full time residents and tourist. Despite continual efforts to protect the watershed, emerging issues such as land development pressures, invasive species, failing septic systems, and barriers to hydrologic connectivity threaten to impair these waters and degrade their ecological and economic treasures. The SNRE team developed a comprehensive watershed management plan under the guidance of Tip of the Mitt Watershed Council and in conjunction with local lake associations and the ERCOL Watershed Plan Implementation Team (ERCOL-WPIT). The team’s efforts included: conducting road stream crossing and streambank erosion surveys across the watershed, leading town hall meetings, performing a priority parcel analysis, and generating spatial analysis reference sets and maps. Ultimately, the ERCOL Watershed Protection Plan will be submitted for approval by the Michigan Department of Environmental Quality (DEQ) and the US Environmental Protection Agency (EPA).The lessons learned on restoration and protection can be carried over to similar geographies throughout the Great Lakes region, to cumulatively protect and enhance Great Lakes’ water quality and ecosystems.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117589/1/2016-04-18_ERCOL_Final.pd

Stability and Seakeeping of Marine Vessels

This book presents the papers accepted into the Special Issue “Stability and Seakeeping of Marine Vessels” and includes nine contributions to this Special Issue published in 2020. The overall aim of the collection is to improve knowledge about the most relevant and recent topics in ship stability and seakeeping. Specifically, the articles cover a wide range of topics and reflect the recent scientific efforts in the 2nd generation intact stability criteria evaluation and modelling of the ship dynamics assessment in intact or damaged conditions. These topics were investigated mainly through direct assessments performed both via numerical methods and tools, and experimental approaches. The book is addressed to individuals from universities, research organizations, industry, government agencies and certifying authorities, as well as designers, operators and owners who contribute to improved knowledge about “stability and seakeeping”