Validation Strategies for Comprehensive Aircraft

Validation Strategies for Comprehensive Aircraft Noise Prediction Methods Antonio Filippone* University of Manchester, United Kingdom Lothar Bertsch and Michael Pott-Pollenske German Aerospace Center (DLR), Germany

Abstract This contribution deals with validation and verification issues in aircraft noise prediction. We use two different comprehensive simulation software: PANAM, developed at DLR and FLIGHT, developed at the University of Manchester. The comparison is done on the basis of extensive flight data taken on an Airbus A319-100 operated by Lufthansa. The flight recorder data have been synchronised with noise measurements on the ground, at 25 different microphones. This paper aims to contribute to the establishment of rational validation standards, as well as realistic accuracy margins on integral noise metrics. The computer codes are briefly described. Results are shown for a variety of microphone positions, for approach/landing, take-off/departure and sideline.

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Introduction

In the beginning there was Silence. Then there was human civilization, and finally came airplanes. The real loud noise started with the introduction of the jet engine. Supersonic commercial flight in the early 1970s was threatened by mass protests. In the 21st century, the supersonic airplane has been abandonded, but the rapid expansion of commercial aviation has prompted ever stricter regulations at airports worldwide. It is now estimated that over 800 major airports have noise restrictions. Although noise levels have been driven down by a combination of technological advances and international regulations, they remain unacceptably high. Thus, the problem has expanded to include land planning, flight scheduling and noise zoning. In a few developed countries we have already run out of space available to expand airports. In some cases, there are legal difficulties in maintaining the current level of traffic. In recent years, also the military have become concerned with aircraft noise, due to a combination of factors. One is the introduction of unmanned vehicles performing covert operations. For these vehicles, silence is an asset. Another reason is the operation of aircraft at military bases, as a routine peace-time activity. Communities around these airfields react in the same way as in the vicinity of commercial airports. *

AIAA Senior Member. School of Mechanical, Aerospace & Civil Engineering. George Begg Building, P.O. Box 88. E-mail: [email protected] DLR, Institute of Aerodynamics and Flow Technology; LilienthalPlatz 7, Braunschweig, 38108, Germany.

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The problem of aircraft noise has been expanding even when aircraft have become relatively quieter. Research in this field was prompted by these noise problems as far back as the 1970s. The research has progressed, albeit slowly, due to the seemingly complex systems that are the source of acoustic emission. The problem of validation has been addressed by the civil aviation in the UK. Noise maps are provided for the purpose of land planning, verification of agreed noise limits, flight quotas, local communities lobbies, government departments and for a variety of other reasons. In one of these initiatives it was demonstrated how the use of INM and ANCON provide vastly different answers on noise maps. The Silent Aircraft Initiative (SAI), a partnership between MIT and Cambridge University 1 , developed methods for the noise minimisation of a new aircraft concept (a blended wing-body with aft-mounted engines). Since these applications address conceptual design (i.e. non existing airplanes), it is difficult to make an assessement regarding their accuracy and consistency. A number of other programs has been developed in more recent years, including PANAM, developed at DLR and FLIGHT, developed at the University of Manchester. The scope of this contribution is to make an assessment of two methods (PANAM and FLIGHT), on the basis of measured data taken on an existing airplane (the Airbus A319-100). Furthermore, we aim to establish a first set of recommendations for aircraft noise code validation to be used in the future. A good deal of the validation difficulties can be overcome if a flight data recorder (FDR) is synchronised with noise measurements on the ground. Modern FDR store several hundred flight parameters, and are the most comprehensive source of raw data for comparison with flight simulation data.

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Comprehensive Aircraft Programs

The purpose of comprehensive codes is to model various aspects of the aircraft, its main subsystems and their mutual integration, in order to offer a realistic simulation tool of the aircraft without the need of too much detail on each system, which would be unavailable anyway. This strategy has been used with considerable success in the field of rotorcraft aeromechanics, but it has lagged behind in the field of commercial aircraft. One of the key shortcomings centers around the issue of validation and verification. This is partly due to the lack of reliable data, and thence on a lack of validation standards. The reference data could be published by a number of organizations, including manufacturers, airline operators and service companies. However, there are commercial rights that prevent this to happen. A number of aircraft noise simulation methods have been developed over the years, including NASA’s code ANOPP 2;3;4;5 and its successor ANOPP2 6 . The FOOTPR Footprint/Radius Code was developed by NASA Glenn Research Center in the early 1980s 7 . Then there is the Integrated Noise Model (INM) developed by the US Federal Aviation Administration since 1978 8 and ANCON (Aircraft Noise Contour Model) developed by the UK Civil Aviation Authority since 1992 9 . These are models that are developed specifically for airport operations and planning and offer no physical insight into the nature of aircraft noise. In this paper, we shall be concerned with validation issues of aircraft noise. The problem is compounded by an additional difficulty: the synchronization of FDR data with acoustic measurements taken on the ground. Existing overall aircraft noise prediction tools can be separated into two groups according to their field of application 10 . These two groups are scientific and best practice tools. Scientific

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tools model the noise emission of individual noise sources. The modeling approach is physicsbased, semi-empirical, and parametric. Therefore, the application is limited to implemented technologies. These tools are applied to indentify low-noise design and operational trends and for initial estimation of new technologies. Scientific tools are usually found in research and academic environments due to their complexity and input requirements. Best practice tools on the other hand are based on measured noise levels. This simplification of the noise source modeling results in low computational requirements. Due to the underlying empirical data base, the application is limited to existing technology but result in higher result accuracy than the scientific tools. The tools are applied to define noise protection zones, optimize land-use planing, and for noise related consulting (decision making support). As a consequence, these tools are usually found in commercial environments. Both tools within this report can be assigned to the scientific overall noise prediction tools.

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PANAM, DLR

In order to indentify promising low-noise technologies at early aircraft design stages, DLR has developed the Parametric Aircraft Noise Analysis Module (PANAM). The tool enables comparative concept studies with respect to overall aircraft noise and performance 11;12;13 . A Balanced Approach as suggested by ICAO has been realized, i.e. noise relevant disciplines and measures can simultaneously be accounted for. PANAM can be applied to investigate modifications to the noise source, land-use planning and traffic routing, operational constraints, and noise abatement flight procedures. Thereby, the tool serves as an integration framework to evaluate selected technologies and provides comprehensible and physics-based solutions. The in-house development allows to fully exploit and incorporate existing and future DLR capabilities in airframe and engine noise modeling, vehicle design, flight procedures, air traffic management, and evaluation of noise effects. Specialists from individual disciplines can use PANAM in order to evaluate their individual technologies on a system level, thus assess the overall impact on vehicle performance and the environment. In general, PANAM is not limited to existing technology due to parametric and semiempirical noise source models. Yet, the parameter space is restrained by the availability of noise source models and their underlying design principles. The prediction methodology is a componential approach, thus major noise sources are simulated with specific models and interactions are neglected. These models reflect basic physical effects and enable to monitor noise related effects along simulated flight operation. Hereby, individual components as well as arbitrary combinations of noise sources can be investigated. The parametric definition of the noise models enables modification of the input data for the noise prediction, e.g. operating condition and geometry of each noise source. Such an approach represents a good compromise between result accuracy and flexibility towards design modification and operational procedures. Aircraft noise is separated into airframe and engine noise contribution. The individual airframe noise components are modeled with DLR in-house approaches 14;15 , i.e. lifting and control surfaces, leading and trailing edge devices, spoilers, and landing gear. Engine noise models are based on available and published solutions 16;17 but have been modified and adapted by DLR engine noise experts 18 . Furthermore, a DLR-inhouse model to account for the noise absorbtion due to acoustic liners has been implemented 19 . Possible noise shielding effects due to aircraft geometry and relative engine location can optionally be accounted for 20 . The required input data is well suitable for conceptual aircraft design. The complexity and quantity of most input parameters can be provided at early vehicle design stages. Yet, certain 3

detailed engine geometry parameters have to be manually provided for the noise prediction, e.g. rotor-stator-spacing. PANAM can be operated in a stand-alone mode or be integrated into larger simulation processes. PANAM has been implemented into two existing simulation environments in order to enable iterative and multidisciplinary analyses with integrated noise prediction capabilities. Automated low-noise aircraft design studies with noise as a new design objective can be performed with PrADO, an aircraft synthesis code by the Technical University of Braunschweig 21 . PANAM can be assembled into individual and task specific process chains within the TIVA system 22 . This distributed and multidisciplinary simulation environment is based on a server-client-architecture in order to interconnect tools from various experts located at different research sites. For example, a dedicated tool chain has been set up among different DLR departments in order to investigate low-noise flight operation 23 .

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FLIGHT, University of Manchester

The program FLIGHT was developed with the aim of creating a reliable software framework for the current generation of commercial aircraft powered by gas turbine engines. Both turbofan and turboprop aircraft can be modelled 24;25;26 . The simulated aircraft include transport and cargo airplanes, as well as business jets. Unlike other computer models, this program does not address any issue of conceptual design. It uses the framework to generate reliable models of the aircraft systems by using a composite method that relies on a large data base, which consists of: 1.) geometry control points, and corresponding rules to reconstruct the airplane; 2.) aerodynamic derivatives of the airplane; 3.) airplane design limitation data (from the type certificate); 4.) engine design limitation data (from the type certificate); 5.) engine flight envelopes (developed out of the loop). For propeller aircraft, there are additional data: 6.) propeller geometry and limitation (from the type certificate); 7.) propeller flight envelope (developed out of the loop). Additionally, there are the following sets of data: 8.) APU data and operational envelopes (some of which are self-generated); 9.) Direct Operating Costs data base (optional). Therefore, a typical airplane model consists of a large amount of data, which it would not be possible to gather for a conceptual design. At the analysis level, the code includes modules for geometry, aerodynamics, propulsion, airframe-engine integration, flight mechanics, trajectory optimisation, thermo-structural performance, static stability, parametric analysis and aircraft noise. The latter module is the subject of the present study. The aircraft noise is modelled on the basis of the method of components, with some consideration for interference factors. The noise module consists of routines at four levels: 1.) noise sources, split into airframe (non propulsive) and propulsion; 2.) noise interference; 3.) noise propagation; 4.) signal analysis. Aircraft noise validation is done at several levels, as illustrated in Fig. 1. First, we must secure a rational aircraft and engine model that is fully validated. Unless the key airplane parameters, as well as engine parameters, are not fully specified, then there cannot be an acceptable basis for noise validation. Once these verification steps are followed through, the flight mechanics integration must be verified. This operation implies that we are able to generate flight trajectories that are realistics. However, if we have access to flight data (as in the present case), this step is by-passed. Finally, we get to the noise validation process, which in itself may consists of several levels of investigation, from the component level to the integration and the flight trajectory (top level). In this paper we only deal with the latter example.

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Flyover noise campaign

Flyover noise measurements were conducted in the year 2006 at the Baltic-Airport in Parchim, Germany. The measurements were part of the German national funded research project L¨ armarme An- und Abflugverfahren under the leadership of Lufthansa German Airlines. In order to characterize the noise footprint of departing and approaching aircraft a total number of 25 microphones was installed at positions directly under the flight path as well as on sideline positions. For the tool comparison, representative observer locations have been preselected, see Figs. 2(a). For take-off and departure the maximum distance of the microphones relative to the runway threshold was 9 nm while for approaches a distance of up to 18 nm was covered with microphones. Parchim Campaign 2006: Observer locations relative to runway threshold

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are monitored by one central station. Every station is equipped with a GPS receiver. The GPS time signal is used to synchronize all acoustic measurement systems as well as the onboard flight path recording of the aircraft. Furthermore the integrated GPS receiver is used to document the station’s measurements location. In order to provide meaningful acoustic data the pressure time histories were digitized with sampling rate of 48 kHz thus providing a 1/3-octave band sound pressure level data for frequencies up to 20 kHz. Acoustic measurements of aircraft noise can be heavily influenced by meteorological conditions as e.g. wind speed and wind direction. Therefore the flyover noise measurements were conducted for meteorological conditions close to those defined for aircraft noise certification. The flights selected for this tool comparison are 2 approach and 2 departure procedures. The recorded flight data along the selected procedures is depicted in Figs. 3(a) to 4(b). Altitude, flight speed (TAS), engine N1, and the configurational setting are depicted as the most dominating operational parameters with respect to noise generation. Fig. 3(a) and 3(b) show the approach procedures and Fig. 4(a) and 4(b) depict the departure flight procedure. The flight data is provided to both noise prediction tools as an input for the computations. Flight rec002

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Results and Discussion

Figures 5 and 6 show the predicted noise levels by PANAM. The data shown include the separate contribution of the airframe and the engine. Figure 7 shows a comparison between measurements and the predictions obtained with the FLIGHT code at four different trajectories. The data shown include the total OASPL and the relative contribution of the airframe.

Acknowledgements This research was partially supported by an EU Clean Sky Grant (SGO-03-002, R-111664).

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