Israel Lopez

Intelligent Flight Trajectory Generation to Maximize Safe Outcome Probability after a Distress Event

A flight trajectory generation method called the distressed-aircraft-recovery technique for maximum safe-outcome probability (DART_MSOP), based on integration of three new algorithms, is developed that maximizes safe-outcome probability after a distress event by incorporating an abort airport together with a model of current aircraft dynamics. Several abort-probability models are studied under various constraints. The first new algorithm, a statistical-based initial-turn-determination algorithm, is developed to advise pilots to a reachable best landing site immediately after the distress event and before using the second new algorithm, a high-fidelity flight trajectory generation algorithm. A third new algorithm determines the flight maneuver for guidance of a perpetual-turning-attitude aircraft to fly the trajectory generated by the second algorithm. The third algorithm is only used if the aircraft has stuck controls or a similar malfunction that generates a nonzero amount of bank angle and causes the aircraft to turn only in one direction. As a three-dimensional high-fidelity algorithm, the second algorithm considers the probability of an abort to increase overall survivability by minimizing expected flight-path length as it shapes the trajectory. The performance of this new intelligent flight trajectory determination method DART_MSOP is evaluated using a case study based on a hypothetical in-flight distressed transport aircraft in northern California. Numerical simulations include variable failure rates to simulate different in-flight distress conditions, and multiple fixes along the path to accommodate realistic trajectories. DART_MSOP intelligent flight trajectory determination method should increase aviation safety if these algorithms are implemented in aircraft avionics systems.

Vibration Mitigation Using Passive Active Tunable (PAT) System: Experimental Aspects

The objective of this paper is to test and model a single-degree-of-freedom vibration isolation system with a magnetorheological (MR) foam damper under harmonic and random excitations. The results of this research are valuable for understanding the characteristics of the MR foam damper and include the experimental design and results of vibration mitigations for frequency ranges up to 2000 Hz. Transmissibility and acceleration hysteresis experiments of the MR foam damper system with different levels of input current are discussed. A simple damper design that eliminates many of the constraints normally associated with fluid filled devices is presented. Constitutive equations of the Bouc-Wen model are used to validate and characterize the MR foam damper. The motion characteristics of the MR foam damper are studied. Experimental results reveal that the mechanical behavior of the MR foam damper is nonlinear and that the field-dependent behavior of MR foam damper is associated with the applied frequency and acceleration amplitude. Experiments demonstrate MR foam damper works well in controlling vibrations and can be controlled and tuned for specific applications.

A Probabilistic Algorithm in Landing Site Selection of a Distressed Aircraft Including Effects of Failure Rates

An algorithm is developed that generates statistically optimal flight trajectory to a best landing site after occurrence of an in-flight distress condition using an abort probability model. The approach developed increases overall survivability by minimizing the expected flight path distance, given the abort probability model. An airport grouping strategy that clumps the airports logically prior to path derivation is also developed. The performance of this newly developed probabilistic trajectory algorithm is evaluated using numerical simulations that include variable failure rates to simulate different in-flight distress conditions, and multiple turns to accommodate realistic trajectories. The results show that it is possible by using this algorithm to increase aircraft survivability.Copyright

System Identification and Damage Assessment of Deteriorating Hysteretic Structures

Assessment of impact damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques are capable of detecting damage, location and/or severity; however, none of the available techniques provide an approach that can take into account structural deterioration in the parameter estimation, and results classificatio. Structures exhibit certain level of hysteretic behavior due to for example hydraulic systems of control surfaces, freeplay of structural joints, and/or accumulated structural damage. In order to capture the uncertainty in estimating system parameters due to structural deterioration, our paper presents a modified exponential forgetting and resetting algorithm (MEFRA) for adaptive system identification to assess impact damage of time-varying hysteretic structures using vibration measurements. In our research, modal and time series parameters are to be identified using measured structural response from an undamaged system and then from a damaged system. Once the model structure is known, the problem of system identification is reduced to an adaptive parameter estimation and tracking. Individual and combined binary classification techniques are utilized to search for the most probable class of event by comparing the relative probabilities for impact damages. The effectiveness and robustness of our damage assessment approach is evaluated via computational simulations and next an overall comparison of the classifiers is given.

Effects of Dimensional Reduction Techniques on Structural Damage Assessment Under Uncertainty

In this paper, we present a study of dimensional reduction techniques for structural damage assessment of time-varying structures under uncertainty. Discrete tracking of the frequency response and the mode shape curvature index method is employed to perform damage assessment. Assessment of spontaneous damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques incorporate the usage of system identification and classification techniques for detecting damage, location, and/or severity; however, much work is needed in the area of dimensional reduction in order to compress the ever-increasing data and facilitate decision-making in damage assessment classification. A comparison of dimensional reduction techniques is presented and evaluated in terms of separating damaged from undamaged data sets under two types of uncertainty, structural deterioration and environmental uncertainties. The use of a recursive principal component analysis for detecting and tracking structural deterioration and spontaneous damage is evaluated via computational simulations. The results of this study reveal that dimensional reduction techniques can greatly enhance structural damage assessment under uncertainties. This paper compares multiple dimensional reduction techniques by identifying their weaknesses and strengths.

Aggregating Imprecise Information in Distressed Aircraft Path Planning

This paper investigates the uncertainty information management in emergency path planning via probability and imprecise probability methods. When in-flight failures or damage occur resulting in a distress condition, rapid and precise decision-making under imprecise information is required in order to regain and maintain control of the aircraft. In order to fly the pre-planned aircraft trajectory and complete safe landing, the uncertainties in system dynamics of the damaged aircraft need to be estimated and incorporated at the level of motion re-planning. The damaged aircraft is simulated via a simplified kinematic model. The different sources and perspectives of uncertainties in the damage assessment process and post-distress trajectory re-planning are presented. The objective of the trajectory re-planning is to arrive at a target position while maximizing the safety of the aircraft given uncertain conditions. Comparative analysis between Bayesian and Dempster-Shafer evidence theory is presented for analyzing imprecise information. Simulations that take into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision are presented for motion planning and landing of an aircraft following a hypothetical distress event.

Intelligent Aircraft Damage Assessment, Trajectory Planning, and Decision-Making under Uncertainty

Situational awareness and learning are necessary to identify and select the optimal set of mutually non-exclusive hypothesis in order to maximize mission performance and adapt system behavior accordingly. This paper presents a hierarchical and decentralized approach for integrated damage assessment and trajectory planning in aircraft with uncertain navigational decision-making. Aircraft navigation can be safely accomplished by properly addressing the following: decision-making, obstacle perception, aircraft state estimation, and aircraft control. When in-flight failures or damage occur, rapid and precise decision-making under imprecise information is required in order to regain and maintain control of the aircraft. To achieve planned aircraft trajectory and complete safe landing, the uncertainties in system dynamics of the damaged aircraft need to be learned and incorporated at the level of motion planning. The damaged aircraft is simulated via a simplified kinematic model. The different sources and perspectives of uncertainties in the damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning and landing is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft given uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.

A novel dimensional reduction approach for structural damage diagnosis using feature similarity

Dimensionality reduction is an essential data preprocessing technique for feature extraction, clustering and data classification in the area of Structural Health Monitoring (SHM). This paper presents a novel data-driven model for feature extraction and its application to damage identification by means of experimental case studies. The method obtains similarity matrix indices for individual dimensional reduction techniques whereby maximum compression of information is obtained and redundancy therein is removed by creating an ensemble of these indices. A systematic comparison of this novel technique to existing linear and nonlinear dimensional reduction methods is given. First case study investigates the aeroacoustic properties of a scaled wing model with penetrating impact damage. In the experimental vibration case study, we use the response of surface mounted accelerometers to detect and quantify damage of an aluminum plate. The dimensional reduction methods will be used to improve the efficiency and effectiveness of damage classifier. In this study, damage identification performances are evaluated using a one-class k-Nearest Neighbor classifier. Classification performance is measured in terms of rate of detection and false alarm via receiver operating characteristic (ROC) curves. The robustness of the damage detection approach to uncertainty in the input data is investigated using probabilistic-based confidence bounds of prediction accuracy. Experimental results show that proposed approach yields significant reduction of false-diagnosis and increasing confidence levels in damage detection.

A Novel Probabilistic Approach in Determination of Landing Site for Distressed Aircraft

A flight path planning strategy based on a novel probabilistic approach is proposed and developed. The main goal is to increase overall survivability of an in-flight distressed aircraft by biasing the flight path closer to possible abort airports so that in the event of an abort, the aircraft has less distance to fly to reach the abort field. Probabilistic methods are used to derive an optimal biased path and to evaluate the efficacy of such a path in improving overall aircraft survivability. Simulation results show that this novel probabilistic approach to flight path planning increases survivability of an in-flight distressed aircraft.

Decision-Making Under Model Uncertainty of Damaged Aircraft Systems

When in-flight failures occur, rapid and precise decision-making under imprecise information is required in order to regain and maintain control of the aircraft. To achieve planned aircraft trajectory and complete landing safely, the uncertainties in vehicle parameters of the damaged aircraft need to be learned and incorporated at the level of motion planning. Uncertainty is a very important concern in recovery of damaged aircraft since it can cause false diagnosis and prognosis that may lead to further performance degradation and mission failure. The mathematical and statistical approaches to analyzing uncertainty are first presented. The damaged aircraft is simulated via a simplified kinematics model. The different sources and perspectives of uncertainties under a damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning to landing site is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft under uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.Copyright