Development and Simulation of a Variable Rate Controller for an Air search and Air Sampling Unmanned Aerial System

This report describes the development and simulation of a variable rate controller for a 6-degree of freedom nonlinear model. The variable rate simulation model represents an off the shelf autopilot. Flight experiment involves risks and can be expensive. Therefore a dynamic model to understand the performance characteristics of the UAS in mission simulation before actual flight test or to obtain parameters needed for the flight is important. The control and guidance is implemented in Simulink. The report tests the use of the model for air search and air sampling path planning. A GUI in which a set of mission scenarios, in which two experts (mission expert, i.e. air sampling or air search and an UAV expert) interact, is presented showing the benefits of the method.

Design and Simulation of a Solar Powered UAV for Remote Sensing Tasks Technical Report

This research deals with the development of a Solar-Powered UAV designed for remote sensing, in particular to the development of the autopilot sub-system and path planning. The design of the Solar-Powered UAS followed a systems engineering methodology, by first defining system architecture, and selecting each subsystem. Validation tests and integration of autopilot is performed, in order to evaluate the performances of each subsystem and to obtain a global operational system for data collection missions. The flight tests planning and simulation results are also explored in order to verify the mission capabilities using an autopilot on a UAS. The important aspect of this research is to develop a Solar-Powered UAS for the purpose of data collection and video monitoring, especially data and images from the ground; transmit to the GS (Ground Station), segment the collected data, and afterwards analyze it with a Matlab code.

Multi-Disciplinary Design and Simulation of a Fuel Cell Engine for a UAV

This report describes a methodology for the design and coupling of a proton exchange membrane (PEM) Fuel Cell to an Unmanned Aerial Vehicle (UAV). The report summarizes existing work in the field, the type of UAV and the mission requirements, design the fuel cell system, simulation environment, and compares endurance and range to when the aircraft is fitted with a conventional internal combustion engine (ICE).

Traffic safety at road–rail level crossings using a driving simulator and traffic simulation

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic microsimulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers’ behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

Efficient simulation of unsaturated flow in heterogeneous soils using an exponential integrator

- Provided a practical variable-stepsize implementation of the exponential Euler method (EEM). - Introduced a new second-order variant of the scheme that enables the local error to be estimated at the cost of a single additional function evaluation. - New EEM implementation outperformed sophisticated implementations of the backward differentiation formulae (BDF) of order 2 and was competitive with BDF of order 5 for moderate to high tolerances.

Mathematical modelling and numerical simulation of phosphine flow during grain fumigation in leaky cylindrical silos

The phosphine distribution in a cylindrical silo containing grain is predicted. A three-dimensional mathematical model, which accounts for multicomponent gas phase transport and the sorption of phosphine into the grain kernel is developed. In addition, a simple model is presented to describe the death of insects within the grain as a function of their exposure to phosphine gas. The proposed model is solved using the commercially available computational fluid dynamics (CFD) software, FLUENT, together with our own C code to customize the solver in order to incorporate the models for sorption and insect extinction. Two types of fumigation delivery are studied, namely, fan- forced from the base of the silo and tablet from the top of the silo. An analysis of the predicted phosphine distribution shows that during fan forced fumigation, the position of the leaky area is very important to the development of the gas flow field and the phosphine distribution in the silo. If the leak is in the lower section of the silo, insects that exist near the top of the silo may not be eradicated. However, the position of a leak does not affect phosphine distribution during tablet fumigation. For such fumigation in a typical silo configuration, phosphine concentrations remain low near the base of the silo. Furthermore, we find that half-life pressure test readings are not an indicator of phosphine distribution during tablet fumigation.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

Molecular dynamics simulation of the phosphorylation-induced conformational changes of a tau peptide fragment

Aggregation of the microtubule associated protein tau (MAPT) within neurons of the brain is the leading cause of tauopathies such as Alzheimer's disease. MAPT is a phospho-protein that is selectively phosphorylated by a number of kinases in vivo to perform its biological function. However, it may become pathogenically hyperphosphorylated, causing aggregation into paired helical filaments and neurofibrillary tangles. The phosphorylation induced conformational change on a peptide of MAPT (htau225−250) was investigated by performing molecular dynamics simulations with different phosphorylation patterns of the peptide (pThr231 and/or pSer235) in different simulation conditions to determine the effect of ionic strength and phosphate charge. All phosphorylation patterns were found to disrupt a nascent terminal β-sheet pattern (226VAVVR230 and 244QTAPVP249), replacing it with a range of structures. The double pThr231/pSer235 phosphorylation pattern at experimental ionic strength resulted in the best agreement with NMR structural characterization, with the observation of a transient α-helix (239AKSRLQT245). PPII helical conformations were only found sporadically throughout the simulations. Proteins 2014; 82:1907–1923. © 2014 Wiley Periodicals, Inc.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Simulation and modelling tools for quantitative safety assessments of unmanned aircraft systems and operations

This paper presents two simple simulation and modelling tools designed to aid in the safety assessment required for unmanned aircraft operations within unsegregated airspace. First, a fast pair-wise encounter generator is derived to simulate the See and Avoid environment. The utility of the encounter generator is demonstrated through the development of a hybrid database and a statistical performance evaluation of an autonomous See and Avoid decision and control strategy. Second, an unmanned aircraft mission generator is derived to help visualise the impact of multiple persistent unmanned operations on existing air traffic. The utility of the mission generator is demonstrated through an example analysis of a mixed airspace environment using real traffic data in Australia. These simulation and modelling approaches constitute a useful and extensible set of analysis tools, that can be leveraged to help explore some of the more fundamental and challenging problems facing civilian unmanned aircraft system integration.

Lateral impact derailment mechanisms, simulation and analysis

Lateral collisions between heavy road vehicles and passenger trains at level crossings and the associated derailments are serious safety issues. This paper presents a detailed investigation of the dynamic responses and derailment mechanisms of trains under lateral impact using a multi-body dynamics simulation method. Formulation of a three-dimensional dynamic model of a passenger train running on a ballasted track subject to lateral impact caused by a road truck is presented. This model is shown to predict derailment due to wheel climb and car body overturning mechanisms through numerical examples. Sensitivities of the truck speed and mass, wheel/rail friction and the train suspension to the lateral stability and derailment of the train are reported. It is shown that improvements to the design of train suspensions, including secondary and inter-vehicle lateral dampers have higher potential to mitigate the severity of the collision-induced derailments.

Simulation design in information systems research: example of studying it value cocreation with NK model

As an emerging research method that has showed promising potential in several research disciplines, simulation received relatively few attention in information systems research. This paper illustrates a framework for employing simulation to study IT value cocreation. Although previous studies identified factors driving IT value cocreation, its underlying process remains unclear. Simulation can address this limitation through exploring such underlying process with computational experiments. The simulation framework in this paper is based on an extended NK model. Agent-based modeling is employed as the theoretical basis for the NK model extensions.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.