Análisis de sistemas eléctricos en microsatélites. Desarrollo de módulos para una Concurrent Design Facility (CFD)para el desarrollo y análisis de misiones espaciales = Analysis of microsatellites power subsystems (battery testing). Development and programming of a new Propulsion Module for a space mission Concurrent Design Facility (CDF)

El objetivo de este proyecto es recoger y explicar el conjunto de tareas realizadas durante el proceso de colaboración llevado a cabo en el Instituto de Microgravedad “Ignacio Da Riva” durante el curso académico 2014/2015, las cuales han conformado las prácticas externas cursadas en la titulación de Grado en Ingeniería Aeroespacial, y el Trabajo de Fin de Grado de la misma titulación. En este documento se pretende,además, poner de manifiesto la rigurosidad con la que se trabaja en el ámbito espacial y la importancia de los protocolos y procedimientos para asegurar un resultado adecuado en los trabajos realizados. Una parte importante del proyecto detalla los procesos de monitorización y mantenimiento de la batería del satélite universitario UPMSat-2 cuyo lanzamiento está previsto para el año próximo y el cual será el segundo satélite del mundo en incorporar una batería de Ión-Litio.

Análisis de sistemas eléctricos en microsatélites. Desarrollo de módulos para una Concurrent Design Facility (CDF) para desarrollo y análisis de misiones espaciales = Analysis of microsatellites power subsystems (battery testing). Development and programming of a new Orbit Design Module for a space mission Concurrent Design Facility (CDF)

El presente proyecto se ha realizado durante las prácticas curriculares que han tenido lugar en el Instituto Universitario de Microgravedad “Ignacio Da Riva” (IDR/UPM). A lo largo de estas prácticas se han llevado a cabo trabajos en diversos campos, todos relacionados con el UPMSat-2, desde el subsistema de potencia (placas solares y baterías) hasta el desarrollo de módulos para la Sala de Diseño Concurrente (Concurrent Design Facility, CDF). En la realización de las mismas se ha trabajado en equipo, junto con otros dos alumnos. El objetivo del proyecto es recopilar las tareas realizadas, proporcionando el desarrollo teórico necesario para llevar a cabo todas ellas. Al ser un trabajo con varias partes claramente diferenciadas, se ha optado por comenzar con unas páginas dedicadas a las misiones espaciales. A continuación el trabajo se adentra en el subsistema de potencia de un satélite, particularizando para el UPMSat-2. Finalmente, se proporciona la teoría necesaria para el desarrollo del módulo de misión de la CDF del IDR/UPM, software que se ha desarrollado y tiene reservado un espacio al final, en el cual se describe el programa y se realizan comparaciones de los resultados que proporciona frente a casos reales.

Análisis en sistemas eléctricos en microsatélites. Desarrollo de módulos para una Concurrent Design Facility (CDF) para desarrollo y análisis de misiones espaciales = Analysis of microsatellites power subsystems (battery testing). Development and programming of a new Orbit Design Module for a space mission Concurrent Design Facility (CDF)

El objetivo de este trabajo de fin de grado es la exposición de los resultados y conclusiones, fruto de las tareas desarrolladas durante las practicas curriculares en el Instituto Universitario de Microgravedad “Ignacio Da Riva” (IDR/UPM) el presente curso académico. La estructura del trabajo se compone de dos bloques diferenciados entre sí: el seguimiento de una batería y el desarrollo de un módulo para una CDF.

Mission Bay Harbor, San Diego County, California, design for wave and surge protection : coastal model investigation /

Includes abstract.

Trajectory design of a multiple flyby mission to asteroids

In designing the trajectory for a multiple flyby mission to asteroids the choice of the targets is the most challenging problem. This dissertation faces this problem in the framework of the recently issued medium-size mission call (M5) from ESA: CASTAway. Starting from the preliminary work done in [6], this thesis develops a methodology for sequencing the potential targets in a multiple flyby mission. In order to reduce the computational time, the complete database of known small bodies is firstly pruned on the base of heuristic considerations. Using the assumption of small manoeuvres, a chief orbit concept could be used. Thus, two heuristic thresholds are defined in order to exclude non-promising targets given a chief orbit. The sequencing process takes chief orbit and promising targets as inputs and gives a set of candidate sequences. The results of such a process are analysed in the CASTAway framework and the best feasible sequence studied in details.

UAS mission path planning system (MPPS) using hybrid-game coupled to multi-objective optimiser

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.

UAS Mission Path Planning System, (MPPS) using hybrid-game coupled to multi-objective optimizer

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

A hardware-based multi-disciplinary design optimization method for aeronautical applications

There are many applications in aeronautics where there exist strong couplings between disciplines. One practical example is within the context of Unmanned Aerial Vehicle(UAV) automation where there exists strong coupling between operation constraints, aerodynamics, vehicle dynamics, mission and path planning. UAV path planning can be done either online or offline. The current state of path planning optimisation online UAVs with high performance computation is not at the same level as its ground-based offline optimizer's counterpart, this is mainly due to the volume, power and weight limitations on the UAV; some small UAVs do not have the computational power needed for some optimisation and path planning task. In this paper, we describe an optimisation method which can be applied to Multi-disciplinary Design Optimisation problems and UAV path planning problems. Hardware-based design optimisation techniques are used. The power and physical limitations of UAV, which may not be a problem in PC-based solutions, can be approached by utilizing a Field Programmable Gate Array (FPGA) as an algorithm accelerator. The inevitable latency produced by the iterative process of an Evolutionary Algorithm (EA) is concealed by exploiting the parallelism component within the dataflow paradigm of the EA on an FPGA architecture. Results compare software PC-based solutions and the hardware-based solutions for benchmark mathematical problems as well as a simple real world engineering problem. Results also indicate the practicality of the method which can be used for more complex single and multi objective coupled problems in aeronautical applications.

Design, simulation and analysis of a parallel hybrid electric propulsion system for unmanned aerial vehicles

Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This paper presents the development of a parallel Hybrid Electric Propulsion System (HEPS) on a small fixed-wing UAV incorporating an Ideal Operating Line (IOL) control strategy. A simulation model of an UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine were determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

Design, modelling and measurement of hybrid powerplant for unmanned aerial vehicles (UAVs)

The success or effectiveness for any aircraft design is a function of many trade-offs. Over the last 100 years of aircraft design these trade-offs have been optimized and dominant aircraft design philosophies have emerged. Pilotless aircraft (or uninhabited airborne systems, UAS) present new challenges in the optimization of their configuration. Recent developments in battery and motor technology have seen an upsurge in the utility and performance of electric powered aircraft. Thus, the opportunity to explore hybrid-electric aircraft powerplant configurations is compelling. This thesis considers the design of such a configuration from an overall propulsive, and energy efficiency perspective. A prototype system was constructed using a representative small UAS internal combustion engine (10cc methanol two-stroke) and a 600W brushless Direct current (BLDC) motor. These components were chosen to be representative of those that would be found on typical small UAS. The system was tested on a dynamometer in a wind-tunnel and the results show an improvement in overall propulsive efficiency of 17% when compared to a non-hybrid powerplant. In this case, the improvement results from the utilization of a larger propeller that the hybrid solution allows, which shows that general efficiency improvements are possible using hybrid configurations for aircraft propulsion. Additionally this approach provides new improvements in operational and mission flexibility (such as the provision of self-starting) which are outlined in the thesis. Specifically, the opportunity to use the windmilling propeller for energy regeneration was explored. It was found (in the prototype configuration) that significant power (60W) is recoverable in a steep dive, and although the efficiency of regeneration is low, the capability can allow several options for improved mission viability. The thesis concludes with the general statement that a hybrid powerplant improves the overall mission effectiveness and propulsive efficiency of small UAS.

The SCRAMSPACE I scramjet flight design and construction

The design activities of the development of the SCRAMSPACE I scramjet-powered free-flight experiment are described in this paper. The objectives of this flight are first described together with the definition of the primary, secondary and tertiary experiments. The Scramjet configuration studied is first discussed together with the rocket motor system selected for this flight. The different flight sequences are then explained, highlighting the SCRAMSPACE I free-flyer separation and re-orientation procedures. A design trade-off study is then described considering vehicle stability, packaging, thermo-structural analysis and trajectory, discussing the alignment of the predicted performance with the mission scientific requirements. The global system architecture and instrumentation of the vehicle are then explained. The conclusions of this design phase are that a vehicle design has been produced which is able to meet the mission scientific goals and the procurement & construction of the vehicle are ongoing.

A framework for testing robust autonomy of UAS during design and certification

As the number of Uninhabited Airborne Systems (UAS) proliferates in civil applications, industry is increasingly putting pressure on regulation authorities to provide a path for certification and allow UAS integration into regulated airspace. The success of this integration depends on developments in improved UAS reliability and safety, regulations for certification, and technologies for operational performance and safety assessment. This paper focusses on the last topic and describes a framework for quantifying robust autonomy of UAS, which quantifies the system's ability to either continue operating in the presence of faults or safely shut down. Two figures of merit are used to evaluate vehicle performance relative to mission requirements and the consequences of autonomous decision making in motion control and guidance systems. These figures of merit are interpreted within a probabilistic framework, which extends previous work in the literature. The valuation of the figures of merit can be done using stochastic simulation scenarios during both vehicle development and certification stages with different degrees of integration of hardware-in-the-loop simulation technology. The objective of the proposed framework is to aid in decision making about the suitability of a vehicle with respect to safety and reliability relative to mission requirements.