Water Delivery Quality and Automatic Control Modes in Irrigation Canals. A Case Study.

Regarding canal management modernization, water savings and water delivery quality, the study presents two automatic canal control approaches of the PI (Proportional and Integral) type: the distant and the local downstream control modes. The two PI controllers are defined, tuned and tested using an hydraulic unsteady flow simulation model, particularly suitable for canal control studies. The PI control parameters are tuned using optimization tools. The simulations are done for a Portuguese prototype canal and the PI controllers are analyzed and compared considering a demand-oriented-canal operation. The paper presents and analyzes the two control modes answers for five different offtake types – gate controlled weir, gate controlled orifice, weir with or without adjustable height and automatic flow adjustable offtake. The simulation results are compared using water volumes performance indicators (considering the demanded, supplied and the effectives water volumes) and a time indicator, defined taking into account the time during which the demand discharges are effective discharges. Regarding water savings, the simulation results for the five offtake types prove that the local downstream control gives the best results (no water operational losses) and that the distant downstream control presents worse results in connection with the automatic flow adjustable offtakes. Considering the water volumes and time performance indicators, the best results are obtained for the automatic flow adjustable offtakes and the worse for the gate controlled orifices, followed by the weir with adjustable height.

SCADA configuration and control modes implementation on an experimental water supply canal

In the last years, digital controllers became a very interesting alternative (low costs and higher accuracy) to the analogue or to hydrodynamic traditional controllers in water supply canal automation, in order to match water supply to water demands. This kind of hydraulic systems needs particular research for control applications because they are big scale systems, open and characterized by big delays and great inertia. This paper presents several digital control modes tested in an experimental canal that will be used as a research platform on the automatic canal control domain. The canal operation and their control modes selection are supervised by a SCADA system developed and configured for this particular canal.

Avaliação do efeito do corte horizontal da copa, com máquina de podar de discos, em pomares de pera “Rocha

Current pear pruning making use of pneumatic shears still is a very labour intensive operation. The Proder project “Avaliação da poda mecânica em pomares de pera” was designed to contribute to solutions that would reduce the present dependence in labour and therefore to promote a reduction in pruning costs. This paper shows the results of a trial made to evaluate the influence of mechanical topping in manual pruning complement field work and pear yield. Topping was performed using a Reynolds 6DT 3.0m cutting bar with six hydraulic-driven circular disc-saws mounted in the three point tractor linkage system. The field trial was performed in a commercial orchard with 20 years, planted in an array of 4m x 2m with tree lines oriented in North-South direction. Trees were trained as the central leader system. In this trial, in a randomised complete block design with four replications, two treatments are being compared leading to 8 plots with one line of 14 trees per plot. The treatments tests were: T1 - manual pruning performed by workers using pneumatic shears, in each year; T2 - Topping the canopy parallel to the ground, using a discs-saw pruning machine mounted in a front loader of an agricultural tractor, followed by manual pruning complement performed by workers with pneumatic shears. Tree height and width was measured, before and after pruning. Work was timed and pear yields evaluated. Mechanical topping seems to be effective in the control of tree height, which can contribute to increase 14% of work rates on manual pruning complement. No significant differences in pear yield were found between treatments.

Efficacy and economic returns from various insecticide combinations applied for tobacco budworm control in the Brazos River Valley.

A field efficacy evaluation revealed significant differences in efficacy among a few of the numerous insecticides or combinations of insecticides applied for Heliothis spp. control. An increasing proportion of made up this field population during the test period. Partial budgeting revealed that the net returns from applying any treatment were directly proportional to the resulting yield obtained from that treatment.

Analysis of slip distribution of large earthquakes oriented to tsunamigenesis characterisation

The present thesis focuses on the on-fault slip distribution of large earthquakes in the framework of tsunami hazard assessment and tsunami warning improvement. It is widely known that ruptures on seismic faults are strongly heterogeneous. In the case of tsunamigenic earthquakes, the slip heterogeneity strongly influences the spatial distribution of the largest tsunami effects along the nearest coastlines. Unfortunately, after an earthquake occurs, the so-called finite-fault models (FFM) describing the coseismic on-fault slip pattern becomes available over time scales that are incompatible with early tsunami warning purposes, especially in the near field. Our work aims to characterize the slip heterogeneity in a fast, but still suitable way. Using finite-fault models to build a starting dataset of seismic events, the characteristics of the fault planes are studied with respect to the magnitude. The patterns of the slip distribution on the rupture plane, analysed with a cluster identification algorithm, reveal a preferential single-asperity representation that can be approximated by a two-dimensional Gaussian slip distribution (2D GD). The goodness of the 2D GD model is compared to other distributions used in literature and its ability to represent the slip heterogeneity in the form of the main asperity is proven. The magnitude dependence of the 2D GD parameters is investigated and turns out to be of primary importance from an early warning perspective. The Gaussian model is applied to the 16 September 2015 Illapel, Chile, earthquake and used to compute early tsunami predictions that are satisfactorily compared with the available observations. The fast computation of the 2D GD and its suitability in representing the slip complexity of the seismic source make it a useful tool for the tsunami early warning assessments, especially for what concerns the near field.

Insights on epidemiology and control of Marek’s disease through traditional and novel molecular tools

Marek's disease (MD) is a contagious, lymphoproliferative and neuropathic disease of poultry caused by a ubiquitous lymphotropic and oncogenic virus, Gallid alphaherpesvirus 2 (GaHV-2). MD has been reported in all poultry-rearing countries and is among the viral diseases with the highest economic impact in the poultry industry worldwide, including Italy. MD has been also recognized as one of the leading causes of mortality in backyard poultry. The present doctoral thesis aimed at exploring Marek's disease virus molecular epidemiology in Italian commercial and backyard chicken flocks and, for the first time, in commercial turkeys affected by clinical MD. Molecular biology techniques targeting the full-length meq gene, the major GaHV-2 oncogene, were used to detect and characterize the circulating GaHV-2 strains searching for genetic markers of virulence. A final study focused on the development of rapid, sensitive, and species-specific loop-mediated isothermal amplification assays coupled with a lateral flow device readout for the detection of conventional and recombinant HVT-based vaccines is included in the thesis. HVT vaccines, currently used to protect chickens from MD, are referred to as "leaky", as they do not impede the infection, replication, and shedding of field GaHV-2: vaccinal and field viruses can coexist in the vaccinated host and molecular tests able to discriminate between GaHV-2 and HVT are required. These new simple, fast, and accurate tests for the monitoring of MD vaccination success in the field could be greatly beneficial for field veterinarians, small laboratories, and more broadly for resource-limited settings.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

An optimal control tool for trajectory generation of autonomous drones

In the recent years, autonomous aerial vehicles gained large popularity in a variety of applications in the field of automation. To accomplish various and challenging tasks the capability of generating trajectories has assumed a key role. As higher performances are sought, traditional, flatness-based trajectory generation schemes present their limitations. In these approaches the highly nonlinear dynamics of the quadrotor is, indeed, neglected. Therefore, strategies based on optimal control principles turn out to be beneficial, since in the trajectory generation process they allow the control unit to best exploit the actual dynamics, and enable the drone to perform quite aggressive maneuvers. This dissertation is then concerned with the development of an optimal control technique to generate trajectories for autonomous drones. The algorithm adopted to this end is a second-order iterative method working directly in continuous-time, which, under proper initialization, guarantees quadratic convergence to a locally optimal trajectory. At each iteration a quadratic approximation of the cost functional is minimized and a decreasing direction is then obtained as a linear-affine control law, after solving a differential Riccati equation. The algorithm has been implemented and its effectiveness has been tested on the vectored-thrust dynamical model of a quadrotor in a realistic simulative setup.

Modeling, simulation, and control of a formation of multirotor aircraft for cooperative transportation of suspended loads

The work presented in this thesis aims to contribute to innovation in the Urban Air Mobility and Delivery sector and represents a solid starting point for air logistics and its future scenarios. The dissertation focuses on modeling, simulation, and control of a formation of multirotor aircraft for cooperative load transportation, with particular attention to environmental sustainability. First, a simulation and test environment is developed to assess technologies for suspended load stabilization. Starting from the mathematical model of two identical multirotors, formation-flight-keeping and collision-avoidance algorithms are analyzed. This approach guarantees both the safety of the vehicles within the formation and that of the payload, which may be made of people in the very near future. Afterwards, a mathematical model for the suspended load is implemented, as well as an active controller for its stabilization. The key focus of this part is represented by both analysis and control of payload oscillatory motion, by thoroughly investigating load kinetic energy decay. At this point, several test cases were introduced, in order to understand which strategy is the most effective and safe in terms of future applications in the field of air logistics.

Simulation and Implementation of a PMSM Sensorless Control Algorithm for Submarine Applications

In this thesis, the study and the simulation of two advanced sensorless speed control techniques for a surface PMSM are presented. The aim is to implement a sensorless control algorithm for a submarine auxiliary propulsion system. This experimental activity is the result of a project collaboration with L3Harris Calzoni, a leader company in A&D systems for naval handling in military field. A Simulink model of the whole electric drive has been developed. Due to the satisfactory results of the simulations, the sensorless control system has been implemented in C code for STM32 environment. Finally, several tests on a real brushless machine have been carried out while the motor was connected to a mechanical load to simulate the real scenario of the final application. All the experimental results have been recorded through a graphical interface software developed at Calzoni.

Design and Implementation of Single phase 230V to Three Phase Inverter Based on the Embedded System STM-32 Applied to motor control

In the field of Power Electronics, several types of motor control systems have been developed using STM microcontroller and power boards. In both industrial power applications and domestic appliances, power electronic inverters are widely used. Inverters are used to control the torque, speed, and position of the rotor in AC motor drives. An inverter delivers constant-voltage and constant-frequency power in uninterruptible power sources. Because inverter power supplies have a high-power consumption and low transfer efficiency rate, a three-phase sine wave AC power supply was created using the embedded system STM32, which has low power consumption and efficient speed. It has the capacity of output frequency of 50 Hz and the RMS of line voltage. STM32 embedded based Inverter is a power supply that integrates, reduced, and optimized the power electronics application that require hardware system, software, and application solution, including power architecture, techniques, and tools, approaches capable of performance on devices and equipment. Power inverters are currently used and implemented in green energy power system with low energy system such as sensors or microcontroller to perform the operating function of motors and pumps. STM based power inverter is efficient, less cost and reliable. My thesis work was based on STM motor drives and control system which can be implemented in a gas analyser for operating the pumps and motors. It has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes and improved structural reliability. The present research was designed to use STM Inverter board on low power MCU such as NUCLEO with some practical examples such as Blinking LED, and PWM. Then we have implemented a three phase Inverter model with Steval-IPM08B board, which converter single phase 230V AC input to three phase 380 V AC output, the output will be useful for operating the induction motor.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.

Hypothalamic Inflammation And The Central Nervous System Control Of Energy Homeostasis.

The control of energy homeostasis relies on robust neuronal circuits that regulate food intake and energy expenditure. Although the physiology of these circuits is well understood, the molecular and cellular response of this program to chronic diseases is still largely unclear. Hypothalamic inflammation has emerged as a major driver of energy homeostasis dysfunction in both obesity and anorexia. Importantly, this inflammation disrupts the action of metabolic signals promoting anabolism or supporting catabolism. In this review, we address the evidence that favors hypothalamic inflammation as a factor that resets energy homeostasis in pathological states.