EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH

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1 EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH Yuma Fuushma* *Insttute of Flud Scence, Tohou Unversty Abstract In ths study, an effcent jet nose predcton procedure s constructed. The proposed stochastc nose generaton and radaton (SNGR) model conssts of the Buldg-Cube Method solvers and a modfed synthetc eddy method (SEM). The present method s appled to two test cases. The frst case s the reconstructon of a wall-bounded turbulent feld computed by drect numercal smulaton (DNS). The second case s the nose predcton of subsonc round jet. The reconstructed spectrum of the wall-bounded flow agrees wth the mean statstcal data computed by DNS. The computatonal cost of the modfed SEM s lower than exstg turbulence generaton methods. The predcton result of jet nose shows good agreement wth experment up to 6, Hz quanttatvely. It s confrmed the proposed SNGR model acheves the hgh computatonal effcency and also hgh predcton accuracy from the results of test cases. 1 Introducton Broadband nose generated from the complcated jet flow s stll ma nose source of arcraft. By usg large eddy smulaton (LES) or drect numercal smulaton (DNS) to compute near-feld turbulent structures, an accurate nose predcton can be made [1]. However these approaches are too tme consumg for dustral and desgn purposes. Therefore, computatonally effcent and accurate methods of modelg turbulence are needed order to study desgns that reduce broadband nose of a jet flow. From ths bacground, varous methods to stochastcally generate a turbulent velocty feld have been proposed. These methods are computatonally effcent and also provde tme dependent turbulent felds whch have prescrbed flow features. These turbulence generaton methods are practcally employed combaton wth Reynolds-averaged Naver-Stoes (RANS) and learzed Euler equatons (LEE) smulatons. Ths combed method s called stochastc nose generaton and radaton (SNGR) model [][3]. In ths study, the SNGR model based on the bloc-structured Cartesan mesh method and synthetc eddy method (SEM) s constructed to realze the effcent jet nose predcton procedure. RANS and LEE computatons are conducted on the framewor of bloc-structured Cartesan mesh method called Buldg-Cube Method (BCM) [4]. BCM has several advantages based on Cartesan mesh over bodyftted structured or unstructured mesh; quc mesh generaton for complcated geometres, easy applcaton of hgh order scheme, hgh effcency calculaton and easy parallelzaton of process. BCM framewor could accelerate the nose predcton processes. SEM [5] s troduced to the procedure of stochastc turbulence generaton to construct faster nose generaton process. In the orgal SNGR model, the generaton of a stochastc turbulent feld s based on the superposton of random Fourer modes, assumg the von Karman-Pao energy spectrum. Ths assumpton s not sutable for homogeneous flows such as a wall-bounded flow. On the other hand, SEM has the capablty to sut any d of flows. Moreover, a turbulence feld s smply represented by the superposton of synthetc 1

2 Yuma Fuushma eddes. Therefore, SEM s smple to mplement and fast to compute le the random Fourer mode method. The objectve of ths research s the constructon of the effcent and accurate broadband nose predcton procedure. To acheve the computatonally effcent process, the smulatons of flow and sound felds are conducted on the BCM framewor. Furthermore, the SEM s employed for the stochastc turbulence generaton. The present method s appled to two test cases. The frst case s the reconstructon of a wall-bounded turbulent feld computed by DNS. The second case s the nose predcton of a subsonc round jet. (a) Computatonal doma and Cube boundary Numercal Method.1 Computatonal Mesh of BCM Computatonal mesh of BCM s generated by followg procedures [4]. Computatonal doma s dvded to aggregaton of square area named Cube as shown Fg. 1(a). Each Cube s then dvded by equ-spaced Cartesan mesh as shown Fg. 1(b). Cells located outsde the wall boundary are defed as flud cells. On the other hand, cells located sde the wall boundary are defed as wall cells. In the method, all Cubes have the same number of cells so that the computatonal effort of all Cubes s bascally equvalent parallel computaton and excellent parallel effcency s acheved. Each Cube has three overlap cells as shown by hatched cells Fg. 1(b) for data exchange. When mesh s locally refed, selected Cube for refement s dvded to eght Cubes, and each Cube s sub-dvded by prescrbed cells. After the refement, the sze of Cube s smoothed so that the sze of adjacent Cubes s restrcted to the same or double/half sze. (b) Computatonal cells a Cube (15 15 cells, 3 overlap cells) Fgure 1. Computatonal mesh of BCM two-dmenson. Stochastc Nose Generaton and Radaton Model The SNGR model s used as an aeroacoustc analyss method. The SNGR model can smulate the turbulent nose wth lower computatonal cost comparson wth LES. Fgure shows the flowchart of the SNGR model. Frst, RANS smulaton wth a turbulence model provdes a tme-averaged flow feld. Second, turbulent velocty fluctuatons are generated by SEM usg the flow formaton obtaed from the RANS smulaton. Thrd, LEE wth unsteady source terms computed from turbulent velocty fluctuatons s solved. SNGR model Reynolds-Averaged Naver-Stoes (RANS) Compute the steady flow feld Acoustc feld analyss Synthetc Eddy Method (SEM) Generate the random velocty fluctuaton Learzed Euler Equatons (LEE) Compute the propagaton of generated fluctuaton Fgure. Flowchart of aeroacoustc analyss based on SNGR approach

3 EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH.3 Reynolds-Averaged Naver-Stoes Solver The governg equatons of computatonal flud dynamcs solver are the compressble Naver-Stoes equatons, whch are dscretzed usg the cell-centered fte volume method. The smple low-dsspatve AUSM (SLAU) [6] scheme s mplemented to compute the vscd flux. The spatal order s frst order. For tme tegraton, the lower-upper symmetrc Gauss-Sedel (LU-SGS) mplct method s employed. Chen s standard -ε turbulence model s used to close the equatons [7]. Detals of compressble BCM solver are descrbed Refs. [8] - [1]..4 Synthetc Eddy Method The SEM s based on a superposton of a synthetc velocty sgnal whch can be wrtten as a sum of a fte number of eddes convectg wth constant velocty (Fg. 3). Ths method s orgally proposed to generate stantaneous velocty fluctuatons at the flow boundares for LES or DNS applcatons. The advantages of SEM are easess of mplementaton, lower computatonal cost comparson wth other methods, capablty to sut any mesh and any d of flows. The velocty fluctuatons are generated by Eq. (1). mean and unt varance. The shape functon of synthetc eddy f s a lear tent functon ths computaton. In the orgal SEM, each eddy convects wth a constant velocty and tme-dependency s troduced by the convecton speed of eddes. However, ths does not sut the SNGR model because the tme scale s the same all spatal drectons. In the present method, dependent turbulent velocty feld s generated at each tme step wth Eq. (1) and a tme-dependency s troduced by flterg usg the Eq. () each drecton [11]. u a u b ( v v ) n n1 n n1 SEM SEM SEM SEM a b a exp 1/ ( F s ), (1 ) / () where Fs s the samplg frequency. The tme scale τ = (fτ) /ε s calculated by the rato of turbulent etc energy to dsspaton rato ε. (fτ) s a turng parameter for adjustg the tme scale response to prescrbed computatonal or expermental data. 1 v A f N N n SEM j j xx j 1 ( ) 1 x1 x 1 1 x x 1 x3 x 3 f ( x x ) V j B f f f R11 Aj R1 / A11 R A1 R31 / A11 ( R3 A1 A31 ) / A R33 A31 A 3 (1) where x, x are the locatons of mesh and the eddes. The εj are respectve tenstes of eddes and Aj s the Cholesy decomposton of the prescrbed Reynolds stress tensor. VB s the volume of eddy box. σ s the turbulent length scale. The poston of the eddes x before the frst tme step are dependent from each other and taen from a unform dstrbuton over the box of eddes B and εj are dependent random varables taen from any dstrbuton wth zero Fgure 3. Schematc of SEM.5 Learzed Euler Equatons Solver Equatons (3) and (4) represent the threedmensonal LEE. The LEE s a wave equaton wth advecton and source terms, and thus s suffcent to compute the realstc sound propagaton. In the computaton of LEE, the mean flow feld Q and sound source S are troduced as put data. Then, the tme evoluton of fluctuaton component Q s computed. The governg equatons are nondmensonalzed by the mean flow densty, sonc speed and reference length. 3

4 Yuma Fuushma Q ' E F G H S t x x x 1 3 ' u ' u Q' v ' Q v w' w p ' p u ' ' u v ' ' v w' ' w p ' vu ' wu ' uu ' p ' wv ' E F vv ' G uv ' p ' ww ' uw' vw' u p ' pu ' v p ' pv ' w p ' pw' u 1 p u ' v ' p ' x1 x1 x 1 v 1 p H v ' v ' p ' x x x w 1 p w' v ' p ' x3 x3 x3 ( 1) p ' v v ' p S S S S S 1 1 S S 3 3 u u u u S n n n n SEM j SEM SEM j SEM, S xj xj (3) (4) The spatal dervatve s calculated by fourth-order dsperson relaton preservg (DRP) scheme of seven-pot stencls [1]. In addton, a fourth-order spatal flterg s appled each teraton to elmate the nonphyscal oscllatons generated at the Cube boundary. Lagrange terpolaton s employed for data exchange at the Cube boundary [13]. Tme tegraton s performed by sx-stage fourth-order low dsspaton and dsperson Runge-Kutta (LDDRK) scheme [14]. The outgog wave s damped by buffer zone boundary condton [15]. The sound source of Eq. (4) proposed by Bogey and Bally s employed [16]. Detals of BCM LEE solver are descrbed Refs. [17] - [19]. 3 Reconstructon of Wall-Bounded Turbulent Feld The reconstructon of a wall-bounded turbulent feld computed by DNS s conducted as the valdaton of the present stochastc turbulence generaton method []. The mean statstcal data of DNS s avalable from the European research communty on flow, turbulence and combuston classc collecton database [1]. The case of Reynolds number Re = 1,41 s chosen. Data conssts of mean veloctes and turbulent propertes at a number of vertcal locatons. These are shown Fgs. 4 and 5. Energy spectra at y + = 1 are reported the database. The turbulent feld s generated by four methods, and the turbulent etc energy spectra of generated turbulent felds are compared wth those of DNS result. In the random Fourer method, the turbulent feld s generated by the superposton of the Fourer seres []. The ampltude of each wave number s computed from the von Karman-Pao energy spectrum. In the dgtal flter method, the turbulent feld s generated by the applcaton of Gaussan flter to the whte nose [3]. In the orgal SEM, turbulent feld s generated by Eq. (1). In the modfed SEM process, the nondmensonal convecton velocty s 16.39, R11 = 3.68, R = 1.19, R33 = 1.89 and R1 = from Fgs. 4 and 5. The resultant turng parameters are (fτ)1 = , (fτ) = and (fτ)3 = To compare the computatonal tme of each turbulence generaton method, the turbulent feld s generated 5, tmes on 1 1 meshes all methods. Fgure 6 shows the computed turbulent etc energy spectra correspondg to the u velocty. Blac le s the spectrum obtaed from the DNS conducted by Spalart. The results of dgtal flter method and present method seem to agree the DNS data very well, wth some dscrepances at the lower and hgher wavenumber. On the other hand, the result of random Fourer overestmates lower and hgher wavenumbers. The result of orgal SEM has over- and underestmaton almost all wavenumbers. These results dcate that the present method has the capablty to generate 4

5 EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH the turbulent feld based on the prescrbed ansotropc Reynolds stress tensor and spacetme correlaton. Table 1 shows the computatonal tme to generate the turbulent feld of Fg. 6. The dgtal flter method s most tme consumg. On the other hand, the orgal SEM method has the lowest computatonal cost. The present method shows lower computatonal cost than the random Fourer method although Eq. () s added to the orgal SEM process. From these results, t s confrmed that modfed SEM s sutable for the generaton of velocty fluctuatons SNGR model. E() 1.E+3 1.E+ 1.E+1 1.E+ 1.E-1 DNS Random Fourer Dgtal Flter Orgal SEM Present 1.E- 1.E- 1.E-1 1.E+ Fgure 6. Turbulent etc energy spectra at y + = 1 correspondg to the u velocty Table 1. Computatonal tme Method Computatonal tme [sec.] Random Fourer 17.3 Dgtal Flter 4.1 Orgal SEM 8. Present 11.9 Fgure 4. Mean velocty profle [17] Fgure 5. Reynolds stress profle [17] 4 Nose Predcton of Subsonc Round Jet The present SNGR model s appled to the nose predcton of a subsonc round jet [4][5]. The nose generated from a jet flow s computed and compared wth experment and wth other s computatonal result. The jet Mach number Mjet =.7 and the dameter of the nozzle D = 8 mm. The computaton s conducted wth the bacground flow of M =.1 because flow to quescent ar s dffcult to acheve the reasonable result. Reynolds number based on the dameter of the nozzle s Re = 5,61. Turbulent etc energy and the dsspaton rate of RANS computaton are used to generate the velocty fluctuaton. The flow feld s used as the bacground flow of LEE computaton. Velocty profle of a round jet, turbulent free-stream boundary condton of turbulence etc energy and dsspaton rato are computed by followg equatons [6]. 5

6 Yuma Fuushma 1 R r u( r) M jet tanh a r R a.3 v ( r) w ( r) 1.5I u I.1 1 t C (5) where I s the turbulence tensty. r s the radus from jet centerle. R s jet radus. Cμ =.9 s the model coeffcent of -ε turbulence model. μt/μ s the eddy vscosty rato. μt s the turbulent vscosty. μ s the molecular dynamc vscosty. μt/μ s set to 1 ths computaton. Densty and pressure are the same as far-feld values. The computatonal regon s one-quarter of jet doma due to flow symmetrcty (Fg. 7). Computatonal doma s 4D 5D 5D, number of Cubes are 386, total number of cells are 1.6 mllon and mmum cell sze s D. Fgures 8 and 9 show the u velocty and turbulent etc energy dstrbuton of the cross-secton surface. Jet flow spreads a radal drecton toward the downstream. Turbulent etc energy creases at the shear flow regon. z x z y (a) From +x drecton (b) From y drecton Fgure 7. Computatonal doma of RANS computaton Fgure 8. u velocty dstrbuton Fgure 9. Turbulent etc energy dstrbuton The velocty fluctuaton s generated by Eqs. (1) and () usg the result of RANS computaton. The number of eddes s N = 5 and the eddy shape f s modeled by lear tent functon. The eddy box s set x = [-D, - 175D], y = [-3.15D, 3.15D], z = [-3.15D, 3.15D]. The nondmensonal convecton velocty s.7. The turbulent length scale s computed by followg equaton usg local turbulent etc energy, the dsspaton rate and cell sze max C, x (6) where Δx s the local cell sze. Dagonal components of Reynolds stress tensor are computed by /3 and the other components are set zero. The turng parameter (fτ) s set all drectons. The generated velocty fluctuaton s used for the sound source represented Eq. (4). The LEE computaton s conducted wthout assumg flow symmetrcty around jet axs. (Fg. 1) Computatonal doma s 4D 5D 5D, the number of Cubes are 1,544, total number of cells are 5.6 mllon and 6

7 EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH mmum cell sze s D. Fgure 11 shows the pressure dstrbuton generated from SEM procedure wth turbulent etc energy contours of RANS computaton. The sound source s the shear flow. The SEM successfully smulates the sound source of a jet flow. The generated nose propagates to far-feld. Fgure 1 shows the defton of samplg pots. Fgure 13 shows the power spectral densty (PSD) of the r = 1D radus. The PSD at 45, 6, 7 and 8 deg are compared wth experment and Laftte s computatonal results [5]. The computaton of Laftte s conducted based on SNGR model. However, the orgal turbulence generaton method s employed [4]. Frequency s normalzed by jet velocty Vjet = 44.8 m/s and reference length D =.8 m. PSD s also normalzed by Strouhal number, St. St = 1 corresponds to 3,6 Hz. The mmum cell sze of LEE computaton s m. Ths cell sze can resolve the sound wave up to 5,44 Hz usg 1 cells. Ths frequency s the grd cut-off frequency of ths computaton. In Fg. 13, PSD s rapdly damped over ths frequency. The mmum cell sze of Laftte s computaton s m and the grd cut-off frequency of ths cell sze s 8,5 Hz. The PSD of present method shows good agreement wth experment at all degree qualtatvely. Hghest PSD near St =.4 and negatve slope near St = 1 are shown. However, the result at 45 deg s slghtly overestmated compared wth experment and Laftte s computatonal results. The samplg pot of 45 deg s closest to sound source the samplg pots, and PSD s overestmated. Ths result can be mproved by the modfcaton of turbulent model because the standard -ε turbulence model tends to estmate the pea of turbulent etc energy downstream compared wth experment the jet flow computaton [7]. The turbulent etc energy dstrbuton changes accordg to the turbulent model. z x z y (a) From +x drecton (b) From y drecton Fgure 1. Computatonal doma of LEE computaton Fgure 11. Pressure dstrbuton wth turbulent etc energy contours Jet ext y θ Samplg pots at r=1d Fgure 1. The defton of samplg pots x 7

8 PSD[dB/St] Yuma Fuushma PSD[dB/St] 1 db Present (45deg) Laftte (1) Experment St (a) 45deg Present (6deg) Laftte (1) Experment PSD[dB/St] PSD[dB/St] 1 db.1 1 St 1 (b) 6deg Present (7deg) Laftte (1) Experment 1 db.1 1 St 1 (c) 7deg Present (8deg) Laftte (1) Experment 1 db 5 Concluson In ths research, an effcent jet nose predcton procedure s constructed. The present SNGR model s employed to predct the turbulent nose generated from jet flow. RANS and LEE smulatons are conducted on the BCM framewor. The modfed SEM s troduced to generate the velocty fluctuaton. The modfed SEM s appled to the reconstructon of a wall-bounded turbulent feld. Four turbulence generaton procedures cludg the modfed SEM are valdated. The dgtal flter method and the modfed SEM method provde better results compared wth other turbulent generaton methods. Moreover, the computatonal tme of the modfed SEM s much lower than the dgtal flter method. These results show the superorty of modfed SEM. The broadband nose generated from a round jet s predcted and compared wth expermental and Laftte s computatonal results. The predcted PSD shows good agreement wth experment at four angles of r = 1D radus. Hghest PSD near St =.4 and negatve slope at St = 1 are shown. However, the result at 45 deg s slghtly overestmated compared wth experment and Laftte s computatonal result. Ths result can be mproved by the modfcaton of a turbulence model. From the results of the test cases, t s confrmed the present SNGR model based on the BCM framewor and the modfed SEM s effectve to jet nose predcton terms of computatonal cost and predcton accuracy. RANS and LEE computatons are based on the BCM framewor and easly extended to large scale computaton. The SEM realzes lower computatonal cost than exstg methods, and can be mplemented wthout sacrfcg parallel effcency of the LEE computaton. The resultant SNGR model acheves the hgh computatonal effcency and also hgh predcton accuracy..1 1 St 1 (d) 8deg Fgure 13. PSD dstrbuton at r = 1D radus References [1] Palath U., et al., Large Eddy Smulaton for Jets from Chevron and Dual Flow Nozzles, 17 th AIAA/CEAS Aeroacoustcs Conference, AIAA Paper , Jun

9 EFFICIENT JET NOISE PREDICTION USING SYNTHETIC EDDY METHOD AND BLOCK-STRUCTURED CARTESIAN MESH [] Bally, C., Juve, D., A Stochastc Approach to Compute Subsonc Nose Usg Learzed Euler s Equatons, AIAA Paper , [3] Bechara, W., et al., Stochastc Approach to Nose Modelg for Free Turbulent Flows, AIAA Journal, Vol. 3, No. 3, pp , [4] Ishda, T., et al., Effcent and Robust Cartesan Mesh Generaton for Buldg-Cube Method, Journal of Computatonal Scence and Technology, Vol., No. 4, pp , 8. [5] Jarr, N., et al., A Synthetc-Eddy-Method for Generatg Inflow Condtons for Large-Eddy Smulatons, Internatonal Journal of Heat and Flud Flow, Vol. 7, No. 4, pp , 6. [6] Shma, E., Ktamura, K., On New Smple Low- Dsspaton Scheme of AUSM-Famly for All Speeds, 47 th AIAA Aerospace Scences Meetg, AIAA Paper 9-136, Jan. 9. [7] Georgads, N. J., et al., Evaluaton of Modfed Two-Equaton Turbulence Models for Jet Flow Predctons, 44 th AIAA Aerospace Scences Meetg, AIAA Paper 6-49, Jan. 6. [8] Naahash, K., Km, L., Hgh-Densty Mesh Flow Computatons by Buldg-Cube Method, Computatonal Flud Dynamcs 4, pp , 6. [9] Naahash, K., Buldg-Cube Method for Flow Problems wth Broadband Characterstc Length, Computatonal Flud Dynamcs, pp , 3. [1] Noshmoto, S., et al., RANS Smulaton around Arfols Usg Buldg-Cube Method, 48 th AIAA Aerospace Scences Meetg, AIAA Paper 1-71, Jan. 1. [11] Jones, R. F., et al., Stochastcally generated Turbulence for Wall bounded Flows, ANZIAM Journal Vol. 51, pp , 1. [1] Tam, C. K. W. Recent advances computatonal aeroacoustcs, Flud Dynamcs Research, Vol. 38, No. 9, pp , 6. [13] Ishda, T., et al., A Hgh-Resoluton Method for Flow Smulatons on Bloc-Structured Cartesan Meshes, The Sxth Internatonal Conference on Computatonal Flud Dynamcs, 1. [14] Berland, J., et al., Low-dsspaton and lowdsperson fourth-order Runge-Kutta algorthm, Computers and Fluds, Vol. 35, No. 1, pp , 6. [15] Rchards, S. K., et al., The Evaluaton of Non- Reflectg Boundary Condtons for Duct Acoustc Computaton, Journal of Sound and Vbraton, Vol. 7, No. 3, pp , 4. [16] Bogey, C., et al., Computaton of Flow Nose Usg Source Terms Learzed Euler s Equatons, AIAA Journal Vol. 4, No., pp ,. [17] Fuushma Y., et al., Code Development of Learzed Euler Equaton on Bloc-Structured Cartesan Mesh for Complcated Geometres, 5 th AIAA Aerospace Scences Meetg, AIAA Paper 1-83, Jan. 1. [18] Fuushma Y., et al., CFD-CAA Coupled Computaton of Fan Nose Propagaton from Enge Nacelle Based on Cartesan Mesh Method, 19 th AIAA/CEAS Aeroacoustcs Conference, AIAA Paper 13-, May. 13. [19] Fuushma Y., et al. The Numercal Analyss of Forward Fan Nose Sheldg Effect on the Over-the- Wg Nacelle Confguraton, 5 nd Aerospace Scences Meetg, AIAA Paper 14-7, Jan. 14. [] [1] Spalart, P. R., Drect Smulaton of a Turbulent Boundary Layer up to Re(θ)=1,41, Journal of Flud Mechancs, Vol. 187, No. 1, pp , [] Lush, P., Measurements of Subsonc Jet Nose and Comparson wth Theory, Journal of Flud Mechancs, Vol. 46, No. 3, pp , [3] Velouds, I., et al., Novel mplementaton and Assessment of a Dgtal Flter Based Approach for the Generaton of LES Inlet Condtons, Flow, Turbulence and Combuston, Vol. 74, No. 1, pp. 1-4, 7. [4] Laftte, A., et al., A study based on the sweepg hypothess to generate stochastc turbulence, 17 th AIAA/CEAS Aeroacoustcs Conference, AIAA Paper , Jun. 11. [5] Laftte, A., et al., Predcton of subsonc jet nose relyg on a sweepg based turbulence generaton process, 18 th AIAA/CEAS Aeroacoustcs Conference, AIAA Paper 1-149, Jun. 1. [6] Faghan, E., et al., Numercal Investgaton of Turbulent Free Jet Flows Issug from Rectangular Nozzles: the Influence of small Aspect Rato, Archve of Appled Mechancs, Vol. 8, No. 7, pp , 1. Acnowledgements The computaton ths research was conducted by AltxUV of Insttute of Flud Scence, Tohou Unversty. Copyrght Statement The authors confrm that they, and/or ther company or organzaton, hold copyrght on all of the orgal materal cluded ths paper. The authors also confrm that they have obtaed permsson, from the copyrght holder of any thrd party materal cluded ths paper, to publsh t as part of ther paper. The authors confrm that they gve permsson, or have obtaed permsson from the copyrght holder of ths paper, for the publcaton and dstrbuton of ths paper as part of the ICAS 14 proceedgs or as dvdual off-prts from the proceedgs. 9