DLR-AS aircraft noise modeling activities

Transcription

DLR-AS aircraft noise modeling
activities
Kolloquium, Institut für Aerodynamik und Strömungstechnik,
11.01.2012, DLR Braunschweig
L. Bertsch
AS-TF
Dr. U. Isermann
AS-HE
presentation outline
part 1: scientific noise models, L. Bertsch
modeling the noise emission
physics based, semi-empirical and parametric
application: limited to implemented technologies, initial estimation, trend
identification (low-noise vehicle design, new flight procedures, real-time
prediction)
DLR activities: Prediction tools PANAM and SIMUL
part 2: best practice noise models, U. Isermann
based on measured noise levels  higher result accuracy
simplified source noise modeling
application: limited to existing technology, noise protection zones,
land-use planning, consulting (airport expansions)
DLR activities: AzB+
summary and Outlook
Folie 2
scientific vs. best practice noise models
classification of overall noise prediction tools
best practice:
1) fully empirical
2) higher accuracy
3) existing technology
4) based on measured noise levels
AzB (CADNA, IMMI, SP)
INM (FAA),
FLULA (EMPA)
AzB+ (DLR)
scientific:
1) semi-empirical, physics-based
2) lower accuracy, initial assessment (trends)
3) new designs, new technology
4) based on modeled noise emission
 possibility of level-time-analysis
VCNS (NLR),
SOPRANO (com.),
Carmen (Onera),
AnOPP 1+2 (NASA),
FLIGHT (Uni Manchester),
SIMUL, PANAM (DLR)
Folie 3
best
practice
scientific vs. best practice noise models
tool
1) modifications to
the noise source
AzB
limited (modification
of existing vehicle
database)
(noise)
SIMUL
scientific
(noise)
PANAM
(noise, local
emissions, LTO
cycle emissions)
examplary
applications
application
2) land-use
planning and traffic
routing
yes, multiple flyover
events, scenarios
(limited to existing
technology)
3) operational
constraints
yes
4) noise
abatement flight
procedures
very limited
(standart and
simplified
procedures)
yes (limited to partial
sound source model)
limited (small
available a/c
database)
limited (small available
a/c database)
yes, 3D flight
procedures
yes, parametric
approach (limited to
modeled technology)
limited (conceptual
studies)
limited (conceptual
studies)
yes, 3D flight
procedures
scientific: new a/c,
engines, technology
scientific: airport
scenarios, capacity
vs. noise
scientific & best
practice: flight
schedule, airport fleet
mix
scientific: arbitrary
low-noise
procedures,
configurational
changes
best practice: noise
protection areas,
consulting
scientific: new
technology (night op.)
Folie 4
DLR-AS aircraft noise modeling activities
Part 1: Scientific noise models
Lothar Bertsch, AS-TF
1. Motivation
2. Tool & Methods
© 2009 by Spiegel.de
(tool assignments, noise
source modeling, IO)
3. Comparison with
flyover noise data
4. Application
(low-noise design,
community noise impact,
airport scenario,
flight test preparation)
Folie 6
1. Motivation
Institute AS:
indentify low-noise technologies at early aircraft design stages
enable comparative concept studies with respect to overall aircraft noise
(a/c design & flight operation)
comprehensible and physics-based methodology
reasons for in-house tool development:
fully exploit and incorporate existing DLR in-house capabilities
(airframe and engine noise, aircraft design, flight procedures, noise effects)
provide common evaluation platform on a system level
assessment of overall impact for selected technologies
(from various specialized institutes and departments)
DLR-wide accessible process
source code access: update recent findings, avoid „black box“ solution
Folie 7
2. Tool & Methods
Parametric Aircraft Noise Analysis Module (PANAM)
componential approach (simulate major noise sources, neglect interactions)
parametric & semi-empirical approach
reflect basic physics
model noise related effects along simulated flight
enable parameter variations (component design, vehicle layout)
main assignments (conceptual design stage):
Institute AS
design of new low-noise vehicles
noise abatement flight procedures (3D): conventional & radical
flight test preparation (mic location selection, optimize flight
procedure, feasibility study)
community noise annoyance (airports, airspace routing)
Partners,
cooperations
effect of individual technologies (retrofit)
Folie 8
2. Tool & Methods
airframe noise source modeling:
simulate major noise sources
(see Fig.)
parametric & semi-empirical models
models derived at DLR-AS from
acoustic flyover data &
component/full-scale
wind tunnel tests*
Tailplane
Flap side-edge
TE devices
Wing
LE devices
Fusela
ge
s
iler
o
p
S
Nacelle
Landing gear
Fig.: airframe noise sources
fully automated geometry segmentation
into acoustic relevant components
(see Fig.)
allows for unconventional vehicles
Fig.: automated geometry segmentation
*) M. Pott-Pollenske, W. Dobrzynski, H. Buchholz, S. Guerin et al.: Airframe Noise Characteristics from
Flyover Measurements and Predictions, AIAA-2006-2567
Folie 9
2. Tool & Methods
engine noise modeling (DLR AT-TA):
simulate major noise sources (see Fig.)
Fan inlet
Jet
Fan exhaust
parametric & semi-empirical models
Turbine & core
Fig.: engine noise sources
models from the literature:
1) Heidmann fan noise model*: adapted to modern high-bypass engines
2) Stone jet noise model**
new acoustic liner damping model***
Fig.: liner
installation
(AT-TA***)
inlet and bypass duct
broadband, tones (center freq.),
buzz-saw
turbine & core noise sources
available, not yet implemented
turboprop and CROR
engine noise under investigation
Fig.: CROR
design (PrADO)
*) M.F. Heidmann: Fan and compressor source noise, NASA Technical Report TM-X-71763, 1979
**) J.R. Stone, D.E. Groesbeck, C.L. Zola: Jet noise prediction, AIAA Journal, 21(1) (1983), pp. 336-342
***) A. Moreau, S. Guerin, S. Busse: Liner Acoustics, NAG/DAGA 2009
Folie 10
2. Tool & Methods
*) M. Lummer, AIAA 2008-3050
engine noise shielding effects:
interface to DLR-AS ray-tracing tool SHADOW*
Input: aircraft geometry, engine location
Output: shielding factors on reference sphere
shielding factors are applied to (forward) fan noise source
automated evaluation process: aircraft design  shielding
factors  overall aircraft noise  aircraft design
Figs.: take-off noise emission directivity - impact of shielding
effects
Folie 11
2. Tool & Methods
input data requirements (according to noise source models):
1.
aircraft design parameters
2.
engine design and performance map
3.
3D flight trajectory (operating conditions, configurational setting)
4.
observer location (height, ground resistivity to air, pop. density)
data complexity suitable for conceptual aircraft design stage
input can be generated by
(1) conceptual aircraft design codes,
(2) dedicated expert tools,
tool interfaces / framework
integration
(3) or be provided by the user
stand-alone operation
Folie 12
2. Tool & Methods
output data:
single and multiple flyover events
individual observers or arrays
max. level and integrated noise levels
(e.g. EPNL, LDEN)
noise level time history: SPL(t)
spectral shape (emission & impact)
community noise annoyance metrics
(e.g. DLR aircraft noise induced awakenings, EU function:
annoyed people)
gaseous engine emissions; local and global
(e.g. CO, HC, NOx, Soot, SO)
WGS data for geographical visualization
Folie 13
3. Comparison with flyover noise data
flyover noise measurements:
validation of PANAM & interaction (!) with other tools
A/C design, flight mechanics, engine design, & acoustics
data available from 3 dedicated fly-over noise campaigns
1) A319 campaign 2006: 9 departures & 9 approaches, 25 ground
observer locations
 overall good agreement of prediction and measurements (trends
and levels)
2) ATTAS campaign 2009: 7 approaches (steep and helical flight
procedures), 12 ground observer locations
 good agreement of level differences & confirmation of initially
predicted noise dislocation effects
3) B737 campaing 2010: 5 approaches, 2 ground observer locations
 promising results, further analysis necessary (engine deck, N1)
overall aircraft noise prediction: adequate for decision making support
Folie 14
3. Comparison
A319 departure procedure: Parchim 2006
prediction of ground noise impact
Folie 15
4. Application: low-noise design
advanced airframe design: theoretical overall noise reduction potential?
comparative noise evaluation: ref. aircraft = A319 type
© DLR
design modifications: 1) leading edge design
2) landing gear
Selected flight procedure: CDA
 airframe noise dominance (=leading edge devices & gear)
Folie 16
4. Application: community noise annoyance
geographical noise mapping (e.g. google earth)
time integrated or maximum levels
simple and quick approach
detailed evaluation: community noise metrics
e.g.: aircraft noise induced awakenings
requires population density  usually: generic distribution
generic
+ CORINE
land cover
data
Folie 17
4. Application: airport & airspace scenario
evaluation of multiple flyover events / flight schedules
output: time integrated and max. noise levels, EU annoyance function, and local
gaseous emissions
interface to fast
time airspace
simulation (DLR FL):
FAA
capacity vs. noise
new a/c & procedures
Folie 18
4. Application: flight test preparation & feasibility study
1) feasibility evaluation
2) mic locations wrt prediction
Activities with DLR RM, e.g.:
radical operational solution*
(Helical Noise Abatement Procedure)
High initial approach altitude
Spiraling final descent in close
proximity to runway threshold
Expected noise dislocation effects:
1. Noise impact concentration in area around the spiraling descent
Multiple flyover events per approaching aircraft
Descent area: ideally a low-populated region e.g. industrial zone
2.
Significant (!) reduction along entire preceding flight path
*) C.Hange, D.Eckenrod: Assessment of a C-17 Flight Test of an ESTOL Transport Landing Approach for
Operational Viability, Pilot Perceptions and Workload, and Passenger Ride Acceptance, AIAA-2007-1398
4. Application: flight test preparation & feasibility study
Noise measurements:
Measurements confirm
predicted / expected
trends
Noise concentration and
dislocation effects along
HeNAP
Significant noise
reduction at Mic5 & 6
Curved flight: noise effects
Recorded noise level diff. wrt reference approach (ILS, flight 1)
e
Nois
l
tentia
o
p
ction
redu
5. Summary: scientific noise models
PANAM: DLR-AS scientific noise prediction tool
main focus on low-noise aircraft and engine design (unconventional
vehicle concepts), low-noise components & technologies
application outside of AS: procedures, level-time-analysis …
limitation: design principles according to noise source models (!)
input data: complexity and requirement adequate for conceptual
design
comparison with experimental data:
feasible overall aircraft noise prediction capabilites
 PANAM ranked as well suitable to
support decision making (comparative analysis)
Folie 21
BACKUP SLIDES
Simulation environments
1) PrADO, aircraft design synthesis code, TU Braunschweig
iterative multidisciplinary design analysis
modular framework: aircraft and engine design, aerodynamics, …
noise as a new design constraint
(certification noise levels, isocontour areas, level-time-histories …)
automated low-noise aircraft design studies
2) TIVA: DLR simulation environment
common system to enable distributed multidisciplinary conceptual
aircraft
server-client-architecture: Phoenix ModelCenter
common data exchange format (CPACS)
framework for the integration of other multidisciplinary tools & methods
 assemble individual process chains
(e.g. engine noise shielding effects)
Folie 23
Future applications & developments
Pilot awareness training (ground
based simulator)
ATM integration
Real-time
prediction:
Google Earth,
weather data?
???
© DLR
© DFS
© DLR RM
Future applications & developments
DLR Tool Suite for Decision Making
Towards Environmental Friendly Aviation
Unterstützung von Entscheidungsträgern im Rahmen von Fragestellungen zu den
Themen Luftfahrt und Umwelt
Automatisierter Prozess (Schritte 1-4) zur Bewertung der
Umweltbeeinflussung
2 Modi: Wissenschaftlich & Zertifizierung
für unterschiedliche Anwendungen (neue vs. existierende Technologien)
Fluglärm, bodennahe Schadstoffe vs. wirtschaftliche Aspekte, langfristig:
Klimawirksamkeit (Projekt CATS)
Keine Black-Box Lösung: Wissenschaftlich fundierter, dokumentierter Prozess
1 – 4 liefern
Ergebnisse:
Grundlage für
Bewertung &
Entscheidung
1) Flugzeugentwurf
- Aerodyn. & Flugmechanik
2) Flugverfahren
3) Lärm und Schadstoffe
4) Auswirkung auf Bevölkerung
Folie 25
Aircraft noise modelling at DLR institute AS
Part 2: Best practice noise modelling
Ullrich Isermann, AS‐HE Göttingen
Institutskolloqium DLR AS-BS > U. Isermann > 11.01.2012
Aircraft noise model classification
Scientific models (already presented by L. Bertsch)
 Pure‐empirical source models (SIMEX2)
 Semi‐empirical source models (SIMUL, PANAM, ANOPP, SOPRANO)
 Physical exact 3D source models (we are dreaming of this ....)
Conventional („classical”) models for practical use
 CPA‐methods (AzB‐1975)  Segmentation methods (AzB‐2008, INM/Doc.29‐derivatives)
 Time‐step simulation (FLULA2)
„Best Practice“
Folie 27
The special characteristics of aircraft noise
 Sound propagation over long distances  large areas influenced by noise
 restrictions in modelling of ground surface properties
 Obstacle‐free sound propagation is standard situation
 simple propagation models adequate for most practical situations
 Estimation of source location not easy
 statistical approaches for complex air traffic scenarios
 effective pre‐processing algorithms needed (e.g. radar data analysis)
 Meteorology affects primarily aircraft location and performance
 acoustical as well as flight mechanical effects must be modelled
Folie 28
Institutskolloqium DLR AS‐BS > U. Isermann > 11.01.2012
The role of propagation modelling
Modelling accuracy
of about ±1 dB
standard situation
air‐to‐ground propagation
Modelling accuracy
of about ±10 dB
ground‐to‐ground propagation
Folie 29
DAGA
2011, Düsseldorf,
Plenarvortrag
U. Isermann
/ Folie 29 von N
Institutskolloqium
DLR AS-BS
> U. Isermann
> 11.01.2012
Source localisation
Zürich airport, departure E16
backbone track
flight corridor boundaries
Source: EMPA 2009
25 km
Variations in: aircraft mass, flight procedure, meteorology
 additional vertical flight path spreading Folie 30
Temperature, pressure and flight performance
10000
Engine thrust (arb. units) 9000
8000
Alitude [ft]
7000
6000
5000
4000
Departure B737–400, 48.5 t
3000
2000
1000
Madrid: 27°C, 580 m above SL Stockholm: 13°C, 15 m above SL 0
0
10000
20000
30000
40000
50000
60000
70000
Distance from brake release [ft]
Folie 31
Some fundamentals ...
d 
L  L ( d o )  20 log    L
 do 
F
L

D
W
Folie 32
Rules for aircraft noise model design
Scientific models
 Make the model as „physical“ as possible
 Try to avoid empirical approaches
 Give the priority to accuracy / completeness
 Efficiency and calculation speed is of secondary importance Conventional („classical”) models for practical use
 Make the model as accurate as necessary and as efficient as possible
 Fit it to the particular field of application
 Keep the different model uncertainties in mind
 Don’t waste your time to increase accuracy where it makes no sense
 Make it precise, transparent and reliable
 Provide documentation and introduce quality management
Folie 33
Noise model and calculation scenario
Noise model
Aircraft data
Scenario
Task
acoustic data
performance data
„Noise engine“
Scenario data
(physical model)
airport data
air traffic data
All components must be harmonised !
Immission data
Folie 34
Database granularity – aircraft grouping
AzB‐2008
INM 7.0b
Jet aircraft groups
(Annex 16, Chap.3)
No. of corresponding
jet aircraft types
No. of engines
No. of engines
MTOM
MTOM
2
3
2
4
3
 50 t
S5.1
 50 t
20
 120 t
BPR > 3  S5.2
BPR  3  S5.3
 120 t
15
9
 300 t
 500 t
> 500 t
A340  S6.3
sonst  S6.2
 300 t
S7
 500 t
S6.1
∅
S8
> 500 t
4
2
8
11
6
∅
2
+ substitution rules
Folie 35
Why aircraft grouping ?
Reasons
 Exact aircraft mix unknown (e.g. for forecast situations  AzB)
 Aircraft database does not cover all relevant aircraft types
 Minimisation cost and effort (data acquisition, calculation time)
How to do it ?
 Define grouping parameters (engine type/number, MTOM, noise certificate ....)
 Use the principle of „acoustic equivalency“
 Describe „noise significant aircraft“ as exact as possible
 Combine less noise significant aircraft to suitable groups
Folie 36
Acoustic equivalency
Departure profiles of AzB group S6.1 aircraft (calculated with INM)
3500
A310
(150 t)
A300
(170 t)
A330
(212 t)
B767
(191 t)
B777
(289 t)
3000
Altitude [m]
2500
2000
1500
1000
Standard deviation of SEL group average (in dB)
500
0
0
1.4
1.4
1.3
1.2
1.2
1.6
5
10
15
20
25
30
35
40
45
Distance from brake release [km]
Folie 37
Modelling approaches – from CPA to segmentation
CPA‐approach
Noise Level
NPD‐Data
Parameter: Power
Distance
Closest Point of Approach
Observer
Folie 38
Modelling approaches – from CPA to segmentation
Noise Level
NPD‐Data
CPA approach
Segmentation approach Parameter: Power
Distance
Total exposure E =  Ei
E4
Closest Point of Approach
E1
E2
E3
Observer
Folie 39
DAGA 2011, Düsseldorf, Plenarvortrag U. Isermann / Folie 39 von N Institutskolloqium
DLR AS-BS > U. Isermann > 11.01.2012
The ECAC Doc.29 segmentation approach (implemented in INM)
Segment
‐


Flight path
Eseg
E
d
Eseg = F ∙ E
Eseg
Segment contribution to exposure
E
Exposure from infinite flight path (from NPD data)
F
„Energy Fraction Factor“
Directivity model: Observer
p2 ~ sin2/d2 ~ d4 („4th‐power‐90°‐dipole“)
 F can be estimated analytically
Merging of emission and propagation – „practical“ approach !
Folie 40
The approach used by AzB‐2008
LWA

Source model based on spectral directivity
(stored in AzB database)
+
Sound propagation model
130 dB
Emission and propagation are treated separately  „physical“ approach !
140 dB
150 dB
Folie 41
Advanced source modelling
NPD
Folie 42
The limiting cases of source modelling
Analytical (physical model + aircraft/engine design and operating parameters)
Separate modelling of:

Engine (jet, fan, turbine, combustion)

Airframe (airframe, gear, wing/high lift devices)

Interaction (wakes, shielding ...) The optimal approach, but not yet realisable (if at all ...)
Empirical (comprehensive measurements)
Spectral directivity as function of:

Engine power

Aircraft speed

Configuration (Flaps, Slats, Spoiler)
Expensive and disregarding physics – „brute force method“
Folie 43
SIMUL partial sound source model
Modelling of the „characteristic“ source mechanisms (based on velocity dependency) Fan noise:
P(V) = P(V0) ∙fF(V)
P(0)  0
Jet noise P(V) = P(V0) ∙ fJ(V, VJet)
P(0)  0
Airframe noise:
P(V) = P(V0) ∙fA(V/V0) P(0) = 0
Simplifying compromise not disregarding physical mechanisms Folie 44
Example: modelling of retrofit measures using SIMUL
A 319 – change of maximum sound level under approach flight path
Noise reducing measures:
 2 dB
 3 dB
Jet : Fan:
Gear :  2.5 dB HLD : up to  2 dB
deployment of
gear and HLD
engines idle
0
LA,max [dB]
engines ‐1
airframe
‐2
total noise reduction
‐3
50
45
40
35
30
25
20
15
10
5
0
Distance from landing threshold [km]
Folie 45
Flight path modelling
Source: Walter Moers, „Der Bonker“
Folie 46
Fixed point flight profiles – up to now standard for noise modelling
Characterisation
 Only 3 parameters needed
 Easy to realise
Example: AzB departure profile Aircraft group S5.1 Distance from
brake release
Speed
 Not flexible
 Aircraft mass and
flight procedure fixed
 Flight mechanical effects
cannot be modelled
Engine power parameter
Power correction
Altitude
Folie 47
Procedural profiles – a must for advanced noise modelling
F
L
Characterisation

 Prescription of flight procedure steps
close to reality
 Accounts for influences of mass, routing and meteorology on flight path
D
W
sin  
Departure B737–400
Climb
F
a D cos 
  
m  g g L cos 
L = n ∙ W Stockholm:
Madrid:

Z
 complex
 requires aircraft performance database
W
Turn
V2
tan  
R  g  cos 
Folie 48
Example: modelling of approach procedures
A320 flight performance data for different approach procedures
Flap setting
Engine power [%N1]
60
4
 Gear deployment
3
2
40
1
20
0
Altitude [km] True airspeed [m/s]
2
140
1
100
60
0
0
10
20
30
40
50
0
10
20
30
40
50
Distance to touchdown [km]
Folie 49
Some profiles of application
‐ Noise legislation
‐ Land use planning
Comparative scenarios („what‐if‐studies“)
„Classical“ applications  Best‐practice‐models
Noise abatement
flight procedures
Source noise reduction
Applications with advanced requirements Folie 50
Classical applications
Airport planning, legal noise protection
Doc.29
AzB
Doc.29
AzB
Doc.29
AzB
 Noise to be forecasted
 Aircraft grouping suitable for traffic forecast EC Environmental Noise Directive (END)
 Harmonised calculation model for EU  Accounting for local conditions at different memberstates (meteorology, flight procedures, aircraft mix ...)
Aircraft phase‐out or replacement  Type‐specific databases required Folie 51
Advanced applications
Noise abatement departure procedures
(PANAM/SIMUL)
Doc.29
AzB
Doc.29
AzB
Doc.29
AzB
 Type‐specific database required
 Availability of procedural profiles  Acoustic data parametrised by engine performance
Noise abatement approach procedures
(PANAM/SIMUL)
 ... as for departure procedures
 Additional capability to model airframe noise
Source noise reduction measures
(PANAM/SIMUL)
 Partial sound source model
Folie 52
How to deal with advanced applications for complex scenarios ? Conventional models
fail ...
Doc.29
AzB
(PANAM/SIMUL)
... databases for scientific models are limited.
Folie 53
DIN 45689 – „Determination of aircraft noise exposure at airports“



Kickoff October 2011 (3–5 years of development expected)
Folie 54
DIN 45689 database: project MODAL
IV. Aviation Research Programme
WP1: Evaluation of B747‐400
flight experiments WP2: DIN 45689 noise and performance database WP3: Analysis of active noise protection measures
WP1.1
Analysis of single microphone data WP2.1
Analysis of air traffic data WP3.1
Verification of measures implemented at FRA
WP1.2
Analysis of
microphone array data
WP2.2
Performance database
development
WP3.2
Application of measures
at other airports
WP1.3
Technical noise reduction measures
WP2.3
Characteristic
flight procedures
WP1.4
Improvement of SIMUL noise model
WP2.4
Noise database
development
Folie 55
Summary: DLR AS aircraft noise modelling competence
PANAM:
Parametric partial sound source noise model
 detailed modelling up from design stage  focus on source modelling, simple propagation model
 very complex, limited database, integrated in modular framework
SIMUL:
Simplified partial sound source noise model
 advanced propagation model
 complex, limited database, stand‐alone solution
AzB+:
DIN 45684 (advanced best practice)
 designed for complex scenarios, comprehensive database
 advanced source and propagation model AzB/INM: Best practice models (commercial software)
 designed for complex scenarios
 simple source models, comprehensive database
Folie 56
Thanks for your attention
Source: Delta Acoustics, DK
Folie 57
The objective of aircraft noise research
Minimisation of aircraft noise immissions on the ground without serious restrictions on operational safety and airport capacity.
Measures identified by ICAO („Balanced Approach“):
•Reduction of noise emission at the source
•Introduction of effective land use planning measures
•Development of noise abatement flight procedures (NAPs)
•Operation restrictions
Cannot be achieved without use of efficient aircraft noise modelling tools Folie 58
Aircraft noise model classification
„Classical“ models for practical use
 CPA‐methods (AzB‐1975)  Segmentation methods (AzB‐2008, INM/Doc.29‐derivatives)
„Best Practice“
 Zeitschrittverfahren (FLULA2)
DIN 45689
AzB+
Scientific models
 pure empirical source models (SIMEX2)
 semi‐empirical source models (SIMUL, PANAM, ANOPP, SOPRANO)
 physically exact 3D source models (available not before retirement of U.I.)
Folie 59
Windspeed and ‐direction
2500
no wind
5 kn tailwind
2000
Altitude [m]
15 kn headwind
1500
1000
Airbus A320
70 t TOM
500
0
0
5000
10000
15000
20000
25000
Distance from brake release [m]
Folie 60
Wind direction, direction of operation and noise contours
wind
eastbound operations
westbound operations
wind
Folie 61
The aircraft as a point source ????
Noise source distribution (Boeing B747)
High‐lift devices
Gears
65 m
Jet
Fan, turbine, ….
It´s up to now the feasible approach !
70 m
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An example Definition of AzB datasets for noise abatement approach procedures
i.
Define procedureal steps (e.g. from airline information) ii.
Use Doc.29 to generate procedurale Profils
 Speed  Altitude 

Doc.29
iii.
Perform noise calculations for the procedural profiles
from step (ii.) using SIMUL
SIMUL
iv.
Analyse level differences along fligtpath
 Power correction level
AzB

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Derived fix‐point‐profiles of AzB type Altitude [km]
Approach procedure
2.0
1.5
S5.2 (LDLP‐AzB)
LDLP
1.0
SCDA
0.5
SLDLP
4
0.0
150
Sped [m/s]
Power correction [dB]
2
100
0
‐2
50
‐4
‐6
40
30
20
10
0
40
30
20
10
0
0
Distance to touchdown [km]
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The AzB+ modelling environment
AzB+ is a DLR software package developed with following intentions:
Provision of a DIN 45689 prototype to support and facilitate the standardization process Definition and testing of database structures and interface formats
Evaluation of software algorithms for physical models Characteristics of AzB+
Fortran 2003 core program
Generalised interfaces (implementation of AzB, SIMUL, FLULA ... possible) I/O data structures in ASCII to guarantee portability
(Input: CSV, Output: CSV, ESRI, NMGF)
Computation restricted to particular aircraft/flight‐track combinations (basic noise grids)
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DLR-AS aircraft noise modeling activities

Transcription

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