A new CFD-Simulink based systems engineering approach applied to the modelling of a hydraulic safety relief valve

A safety relief valve is a simple hydro-mechanic device, needed to avoid overpressure transients inside hydraulic circuits. Such valves are a critical part of the hydraulic system of aircraft; hence their performances must be adapted to a specific nominal pressure level and design requirements. In the following paragraphs the authors will address the issue of designing and validating a safety valve through a hybrid CFD/MATLAB-Simulink® approach. The main constraints are the geometrical dimensions and the need to limit the weight of the device. A significant part of the work consists of gathering all the possible information available in the literature, dealing with the best design practices to achieve the performance objective. Thanks to a robust computational procedure, it should be possible to reduce the amount of “physical” prototypes required to validate the functionality of a safety relief valve. The process presented uses a numerical computational fluid dynamic (CFD) approach, to define the pressure field inside the valve and the forces acting on it; identifying the force distribution inside the valve is paramount to address the performance evaluation of the valve itself. The first step deals with the definition of a computer aided design (CAD) model of the valve. Then the CFD software uses the above-mentioned CAD model to define the domain of the problem. Once obtained the pressure field, it is possible to integrate it through the surface of the valve, thus obtaining the forces acting on the moving part (poppet). After the numerical scheme has been calibrated, some investigations are done to reduce the computational cost: the main aim is to run a complete simulation (meshing and solving) on a standard computer. Some of the positions (i.e. strokes) of the valve have been simulated as static, hence a steady-state calculation has been applied to solve the motion field. Another important result consists of creating a MATLAB-Simulink® model, capable to reach results comparable to the CFD simulation, but in shorter times. While the CFD model can provide high quality results, the MATLAB-Simulink® calculation should be used to create a “first guess” instrument, useful to address the very first valve geometry. The implementation of the Look-Up Tables (LUTs) links the MATLAB-Simulink® model to the CFD simulation, but increases the time required to obtain a solution: on the other hand, this reduces the amount of equation-modeled quantities, delivering a greater precision to the calculations

Proposal of a model based fault identification neural technique for more-electric aircraft flight control EM actuators

There are many different ways to detect incipient failures of electromechanical actuators (EMA) of primary flight command provoked by progressive wear. With the development of a prognostic algorithm it’s possible to identify the precursors of an electromechanical actuator failure, to gain an early alert and so get a proper maintenance and a servomechanism replacement. The present work aims to go beyond prognostic algorithms strictly technology-oriented and based on accurate analysis of the cause and effect relationships because if on one hand they show great effectiveness for some specific applications, instead they mostly fail for different applications and technologies. Through the development of a simulation test bench the authors have demonstrated a robust method to early identify incoming failures and reduce the possibility of false alarms or non-predicted problems. Authors took into account friction, backlash, coil short circuit and rotor static eccentricity failures and defined a model-based fault detection neural technique to assess data gained through Fast Fourier Transform (FFT) analysis of the components under normal stress conditions

Linear Electromechanical Actuators Affected by Mechanical Backlash: a Fault Identification Method Based on Simulated Annealing Algorithm

Several approaches can be employed in prognostics, to detect incipient failures of primary flight command electromechanical actuators (EMA), caused by progressive wear. The development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure is beneficial for the anticipation of the incoming faults: a correct interpretation of the fault degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. The research presented in this paper proposes a fault detection / identification technique, based on approaches derived from optimization methods, able to identify symptoms of EMA degradation before the actual exhibition of the anomalous behavior; in particular, the authors’ work analyses the effects due to progressive backlashes acting on the mechanical transmission and evaluates the effectiveness of the proposed approach to correctly identify these faults. An experimental test bench was developed: results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual fault, minimizing the risk of false alarms or not annunciated failures

Electromechanical actuators affected by multiple failures: a simulated-annealing-based fault identification algorithm

The identification of early evidences on monitored parameters allows preventing incoming faults. Early alerts can avoid rate of the failures and trigger proper out-of-schedule maintenance activities. For this purpose, there are many prognostic approaches. This paper takes into account a primary flight command electromechanical actuator (EMA) with multiple failures originating from progressive wear and proposes a fault detection approach that identifies symptoms of EMA degradation through a simulated annealing (SA) optimization algorithm; in particular, the present work analyses the functioning of this prognostic tool in three different fault configurations and it focuses on the consequences of multiple failures. For this purpose, we developed a test bench and obtained experimental data necessary to validate the results originated from the model. Such comparison demonstrates that this method is affordable and able to detect failures before they occur, thus reducing the occurrence of false alarms or unexpected failures. © 2016, North Atlantic University Union. All rights reserved

Flaps Failure and Aircraft Controllability: Developments in Asymmetry Monitoring Techniques

The asymmetry limitation between left and right wing flap surfaces is one of the most severe requirements for the design of the actuation and control system. When the position asymmetry exceeds a defined value, it must be detected and limited by appropriate monitoring devices equipped with a suitable software. In the design of a new flap control system the development of the asymmetry monitoring subsystem plays a very important role and, together with the flap system layout selection, it was and still is a debated matter in the industrial field. The currently used monitoring technique is based on the differential position detection between left and right surfaces. Their use generally slightly reduces the asymmetry, but in some cases it may have an unreliable behavior. In order to overcome the shortcomings of the previous models, in this work the authors propose new different monitoring strategies and assess their positive effects on the maximum asymmetry following a torque tube failure; in particular, the asymmetry reduction coming from the employment of the proposed techniques in the considered operative conditions (typical deployment / retraction flap actuations), with respect to a previous type of technique, can be evaluated in the order of 30%

Electromechanical servomechanisms affected by motor static eccentricity: Proposal of fault evaluation algorithm based on spectral analysis techniques

The development of a prognostic algorithm capable of identifying the precursors of incipient failures of primary flight command Electromechanical Actuators (EMA) is beneficial for the anticipation of the incoming failure: a correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. Prognostic, though, is strictly technology-oriented as it is based on accurate analysis of the cause and effect relationships. As a consequence, it is possible that prognostics algorithms that demonstrate great efficacy for certain applications fail in other circumstances, just because the actuator is based on a different technology. In this paper the authors propose an innovative prognostic “model-based” technique able to identify symptoms of an EMA degradation before the actual exhibition of the anomalous behavior. The identification/evaluation of the considered incipient failures is performed analyzing proper critical operational system parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters are correlated with the actual health condition of the considered system by means of failure maps created by a reference monitoring model-based algorithm. According to preliminary testing, the proposed method has proved its worth for different types of EMA progressive failure: in particular, in the present work, it has been applied to the case of an actuator having brushless DC motor affected by a progressive increase of the static eccentricity of the rotor. © 2015 Taylor & Francis Group, London

Numerical methods for the electromagnetic modelling of actuators for primary and secondary flight controls

Prognostics and health management of electromechanical actuators (EMA) must rely on affordable and representative simulation models to be effective in predicting evolution of failures, so to identify them before they occur through the assessment of monitored parameters, leading to on-spot maintenance operations. This paper presents a multi domain model of al EMA, putting special attention on the fidelity of the numerical modelling of the inverter and of the related electromagnetic aspects. Such model permits to simulate the behavior and the types of failure of the electro actuator in a realistic and precise way. The choice of the multi domain simulation is necessary to improve from the simplifying hypotheses that are typically considered in numerical models that are mostly used for prognostic analyses of electro mechanical actuators

Evaluation of the correlation coefficient as a prognostic indicator for electromechanical servomechanism failures

In order to identify incipient failures due to a progressive wear of a primary flight command electromechanical actuator, several approaches could be employed; the choice of the best ones is driven by the efficacy shown in fault detection/identification, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the abovementioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming malfunction and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a fault detection/identification technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior, and to evaluate its potential use as prognostic indicator for the considered progressive faults (i.e. frictions and mechanical backlash acting on transmission, stator coil short circuit, rotor static eccentricity). To this purpose, an innovative model based fault detection technique has been developed merging several information achieved by means of Fast Fourier Transform (FFT) analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the performance of the proposed technique, an appropriate simulation test environment was developed: the results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures. © 2015, Prognostics and Health Management Society. All Rights Reserved

Model based fault detection neural technique for electromechanical servomechanisms

Incipient failures of electromechanical actuators (EMA) of primary flight command, provoked by progressive wear, can be identified with the employment of several different approaches. A strong asset is expected by the development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure. If the degradation pattern is well understood, it is possible to trig an early alert, leading to proper maintenance and servomechanism replacement. Prognostic, though, is as it is. As such algorithms are strictly technology-oriented and based on accurate analysis of the cause and effect relationships, they may show great effectiveness for some specific applications, while mostly failing for different applications and technologies. This work proposes an approach with a demonstrated benefit from a prognostics point of view. Friction, backlash, coil short circuit and rotor static eccentricity failures are considered. A model-based fault detection neural technique is defined and used for the assessment of the data obtained through Fast Fourier Transform (FFT) analysis of the components under normal stress conditions. A simulation test bench has been developed for the purpose, demonstrating that the method is robust and is able to early identify incoming failures, reducing the possibility of false alarms or non-predicted problems

Simulated Annealing Algorithm applied as a Fault Identification Method for Electromechanical Actuators affected by Multiple Failures

The approaches used in prognostics can be varied, all with the aim to recognize incipient failures. The considered case is one primary flight command electromechanical actuator (EMA), with multiple failures originating from progressive wear. The capability to anticipate incoming faults is a consequence of the possibility to identify early clues on monitored parameters. Early alerts can avoid the occurrence of the failure, triggering opportune out-of-schedule maintenance activities. This paper proposes a fault detection approach, based on the simulated annealing optimization algorithm, capable to identify symptoms of EMA degradation before the behavior becomes anomalous. In particular, the work puts a focus on the effects of multiple failures, analyzing the performance of this prognostic tool in three different fault configurations. The results originated from the model are validated through experimental data obtained from a test bench developed for the purpose; such comparison shows that the method is robust and has a high degree of confidence in the ability to identify faults before they occur, minimizing the possibility of false alarms or unexpected failures