Gianfranco Morani

Virtual Air Data System Architecture for Space Reentry Applications

air data are virtual measurements based on the combination of inertial measurements with weather forecast data coming from a meteorological model. This model can be considered as a virtual sensor that provides information on wind velocities, air temperature, and air pressure. The combination of inertial measurements with weather forecast data has been carried out using a sensor fusion algorithm (specifically, an extended Kalman filter) allowing the estimation of the true-air-speed components. This kind of air data system architecture allows most of the typical limitations of conventional air data probes to be overcome and does not require any specific experimental or computational fluid dynamics calibration campaign or any thermal protection system when dealing with reentry applications.Its effectiveness hasbeendemonstrated with twopractical applications,the firstbasedonthepostflight analysis of the first Dropped Transonic Flight Test mission (carried out by the Italian Aerospace Research Center) and the second based on a simulated case of the second Dropped Transonic Flight Test planned for the next years.

Method for Prediction and Optimization of a Stratospheric Balloon Ascent Trajectory

DOI: 10.2514/1.39469 In this paper, we propose a methodology for the prediction and optimization of the ascent trajectory of a stratospheric balloon to target a specified three-dimensional area. The methodology relies mainly on the Analysis Code for High-Altitude Balloons, a simulation tool for the prediction of flight trajectory and thermal behavior of high-altitude,zero-pressureballoons,andonastatisticalanalysisforestimatingthetrajectorypredictionerrors.The paperalsodescribesthealgorithmsusedforballoonparameteroptimizationtoobtaina flighttrajectorythatreaches apredefinedtargetareawithoutanyballastdroporgasventingcontrol.Theproposedmethodologywassuccessfully used during the first Dropped Transonic Flight Test of the Flying Test Bed 1 demonstrator accomplished on 24February2007bytheItalianAerospaceResearchCenter.Thereportedpostflightanalysisofallthetestcampaign demonstratesthattheproposedmethodologyfortrajectorypredictionandoptimizationguaranteesverysatisfactory and reliable results for both the selection of the best day to perform such a mission and the definition of the correct balloon parameters to be used for targeting a predefined three-dimensional area.

On-line guidance with trajectory constraints for terminal area energy management of re-entry vehicles

In this article, an on-line guidance strategy for terminal area energy management phase of a re-entry flight is proposed. In order to fulfil the objectives of this flight phase, the algorithm continuously performs long- and short-term on-line trajectory generation, also accounting for the most relevant vehicle and trajectory constraints. Long-term guidance computes a reference trajectory which minimizes the distance from the final target. Short-term guidance generates the control commands by computing a trajectory that minimizes the displacement from the reference one, thus compensating for the errors due to the environmental disturbances and to the uncertainties of the vehicle model. In the proposed guidance strategy, the main vehicle performance constraints are appropriately accounted for, thus guaranteeing adaptivity in the failure situations where the manoeuvring capabilities are reduced. The proposed guidance strategy has been developed in the framework of the unmanned space vehicle program of the Italian Aerospace Research Centre for the execution of Dropped Transonic Flight Test second mission. In this article, the algorithm performances and robustness to the vehicle initial state have been assessed through a preliminary Monte Carlo analysis. Furthermore, several simulations with bank angle and angle of attack limitations have shown the effectiveness of the algorithm in the presence of reduced manoeuvring capabilities.

Design Analysis of the High-Speed Experimental Flight Test Vehicle HEXAFLY-International

Achieving airbreathing hypersonic flight is an ongoing challenge with the potential to cut air travel time and provide cheaper access to space. Waveriders are potential candidates for achieving hypersonic cruise or acceleration flight within the atmosphere. Current research tends to focus on key issues like thermal loading, aero-elasticity and aerothermodynamics at hypersonic speeds. Design problems in each of these areas must be solved if a hypersonic waverider design is to be viable. In this frame the HEXAFLY-INT project aims at the test in free-flight conditions of an innovative gliding vehicle with several breakthrough technologies on-board to be launched along a suborbital trajectory. Its preliminary conceptual design has been carried out by means of a number of numerical tools suitable to design vehicles flying in hypersonic conditions. The main results of the design analysis carried out during the preliminary phase of the study, such as vehicle aerodynamics and aerothermodynamics, re-entry trajectories, structures and mechanisms, and on the overall system, as well, are presented in this work.

On-line trajectory generation for autonomous unmanned vehicles in the presence of no-fly zones

In this article, an algorithm for three-dimensional path generation and tracking for unmanned air vehicles in the presence of no-fly zones is proposed. The algorithm is based on a local optimization procedure aimed to find the shortest path between the waypoints in compliance with all path constraints. Vehicle structural and envelope limitations are accounted for by simple geometric constraints such as minimum curvature radius and flight path angle limitations, while no-fly zones are defined as cylindrical objects with infinite altitude. The algorithm is simple and it has a limited computational burden, at most quadratic with the number of zones to avoid. This makes the algorithm very suitable for real-time applications even in case of a high number of forbidden zones. Algorithm effectiveness has been demonstrated by means of numerical simulations in scenarios including the presence of no-fly zones not known before flight (for instance, in the case of sudden changes of weather conditions and/or detection of new fixed obstacles).

GN&C Technology Innovations for TAEM: USV DTFT2 Mission Results

This paper presents an overview of key GN&C technology innovations for Terminal Area Energy Management that have been flight demonstrated in CIRA’s Unmanned Space Vehicle Dropped Transonic Flight Test 2, successfully performed in April 2010. During the flight the vehicle dropped from an altitude of about 25 km, accelerated up to Mach 1.2 and then decelerated to less than Mach 0.2 at 5 km of altitude for final recovery using a very low cost parachute. In this paper, CIRA GN&C technology innovation roadmap for TAEM phase of flight is firstly described. Then an overview of innovations on guidance, navigation and control algorithms introduced in DTFT2 mission is given. Finally, a comparison between expected and actual flight test results is presented together with a discussion of the lessons learned.

Effective Approach to Characterization of Prediction Errors for Balloon Ascent Trajectories

In order to comply with the objectives of a balloon mission, it is usually necessary to predict, monitor and track the flight trajectory. Within the framework of the Unmanned Space Vehicles program, the Italian Aerospace Research Center has developed several methodologies and tools useful to evaluate balloon mission feasibility, predict balloon flight trajectory and assess trajectory prediction errors. These methodologies are based on weather forecast data for trajectory computation obtained using the Integrated Forecast System model by the European Center for Medium Range Weather Forecast and on the use of a proprietary simulation software for the prediction of flight trajectory and thermal behavior of high altitude zero-pressure balloons. In this paper, we propose an effective approach for the characterization of trajectory prediction error based on: statistical characterization of the forecast error of the Integrated Forecast System atmospheric data (winds, temperature and pressure), statistical characterization of the error on gas mass due to inflation procedures and analytical error propagation of these sources of uncertainties on the balloon’s velocity vector. The proposed approach allows to estimate the actual trajectory dispersion once the predicted trajectory is computed using forecast data. The methodology proposed in this paper is an improvement of the trajectory prediction methodology that has been successfully used during the first Dropped Transonic Flight Test mission accomplished by Italian Aerospace Research Center.

A MONTE CARLO BASED ANALYSIS FOR USV FTB1 DTFT MISSION VALIDATION

This paper describes the validation of the CIRA FTB_1 experimental re-entry vehicle in performing the first Dropped Transonic Flight Test, by using a Monte Carlo based analysis. Aims of the presented validation process were both to assess the capability of the FTB1 augmented vehicle to fulfill all the mission requir ements and to optimize the nominal mission parameters for maximizing the mission execution success rate. Some peculiar issues have been addressed in this study. Specifically the pape r reports a criterion for estimating the minimum number of simulation runs needed for obtaining reliable analysis results. Moreover, a structured analytical model for taking into account aerodynamic uncertainties is proposed and an algorithm for flight instability detection have been developed in order to verify GNC robustness. Finally, a dedicated automatic code generation environment have been used in order to reduce both development effort and total computational time of Monte Carlo analysis to acceptable values for parameter o ptimizing purposes.

Advanced GN&C Technologies for TAEM: Flight Test Results of the Italian Unmanned Space Vehicle

This paper describes the guidance, navigation and control challenges posed by the Unmanned Space Vehicles Program. Within the framework of this program the Italian Aerospace Research Center has conceived several advanced GN&C technologies useful in the Terminal Area Energy Management phase of a re-entry flight pattern. These technologies were flight tested during the first two dropped transonic flight tests (DTFT1 and DTFT2) of the program. More specifically, this paper will present the design of the adaptive guidance algorithms developed to accomplish the mission objectives of the DTFT2 flight test. Flight results will be shown in order to state the performance of the guidance strategy putting in evidence, where possible, its most promising aspects for future TAEM applications.

New Algorithm for Probabilistic Robustness Analysis in Parameter Space

In this paper a new algorithm for robustness analysis of uncertain parametric systems is proposed. The algorithm adopts a probabilistic approach to find a multidimensional region in the uncertainty parameter space where a system satisfies a given property. In particular it finds a (suboptimal) maximum volume hyper-rectangle in which the given system’s property is satisfied with a pre-assigned confidence. The algorithm has been applied for the robustness analysis of the Italian Aerospace Research Centre’s unmanned space vehicle demonstrator’s maneuverability. The use of the algorithm during the design phase of the project lowered the effort spent in aerodynamic wind tunnel testing on specific coefficients that did not require the reduction of the uncertainty ranges. Moreover the algorithm has been used for estimating the flying test bed’s initial state displacement compatible with a safe mission execution to support online launch decisions. Effectiveness of the proposed method in terms of computational efficiency and reliability has been demonstrated by comparing the results with a deterministic method that finds the actual region in the uncertainty space where the system properties are verified. The proposed algorithm allows the computational burden of robustness analysis to be drastically reduced, particularly when the number of uncertain parameters is greater than three.