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Bao Nguyen Projects
• Aerial GroundVehicle (December 2013)
• Under-Expanded Flow Simulation (December 2013)
• Structure Design with Finite Element Analysis (May 2013)
• The Advection Diffusion of CO in San Bernardino Valley (May 2012)
• Business Jet Design (May 2013)
• Simulation of 2-D heat diffusion (August 2012)
• Tennis Club Database Modeling (May 2011)
GAV Aerodynamic Analysis
Stability & Control
Aerodynamics Characteristics Of Wing Sections
Requirements:
•CL_max clean = 1.47
•CL_max LandingFlaps = 2.42;
•In 118 airfoil sections, the selected: 0012_64a, 1412, 2410, 4412, 4418, 4421
•Using a generic Matlab function to determine the airfoil with margin 0.3. (Codes at notes below)
•Goal to get the highest lift coefficient among the selected: NACA 4412
General lift distribution of the telescoping wing with NACA 4412
• The monoplane equation:
• Lift coefficient:
• Induced drag coefficient:
• Numerical method:
• Parameters:
• Assumptions: thin airfoil theory, 𝒂 𝟎 = 𝟐𝝅, Prandtl lifting line theory
• The lift curve for finite wing of general planform: 𝑎 =
𝑎0
1+(𝑎0/𝜋𝐴𝑅)(1+𝜏)
• Assumptions: Figure 5.20: Induced drag factor δ as a function of taper ratio
C_t(m) b(m) t(m) n aoa_L0 (deg) aoa_Clmax (deg) theta_rad m pts
1.27 10.3 0.002 4 -4 14 π/2 30 30
C_t/C_r ~ 1.01
Fig. 5.18: τ ~ 0.09
Lift curve (/deg): a ~ 0.0865
Numerical method C_L_α (/deg) 0.0859
% error 0.74%
Errors from rounding errors and
estimation from figure 5.20
Lift coefficient and Induced drag coefficient of the wing with
NACA4412
Clmax (clean) C_Lmax (clean) C_Di_max
1.7 1.55 0.0936
(The airplane maximum lift coefficient) < (The airfoil maximum lift coefficients)
Flight Performance
•Cruise
CL: 0.4 to 0.8
L/D: 14.81 to 16.68
•Takeoff:
CL_to: 1.57
L/D: 11.19
•Landing:
CL_ld: 1.5
L/D: 10.63
Stability and Control
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C_m_ac -0.11 S 13.10 m2
Cl_α 0.1 /deg
α_0_L -4 deg b 10.30 m C_m_ac 0
Cl_α 0.125 /deg c_bar 1.27 m i_t -1 deg
X_ac 0.25 c_bar l_f 4.20 m
No twist d_max 2.00 m
i_w 1 deg S_H 2.15 m2
l_t 5.55 m
Tail airfoil sectionRef geometryWing airfoil characteristics
Part b
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Stability and Control Results
Cm_α Cm0
C_mcg_f = 0.013 α + -0.057
C_mcg_w = 0.252 α + -0.092
C_mcg_t = -2.351 α + 0.275
C_mcg = -2.086 α + 0.125
A comparison with a general
aircraft from Robert Nelson’s
textbook
Payload range plot
FAR RequiredTOFL
FAR Climb Gradient Requirement
FAR Required LFL
References
• Anderson, John D. Fundamentals of Aerodynamics. NewYork: McGraw-Hill,
2011. Print.
• Hale, Francis J. "Chapter 7." Introduction to Aircraft Performance, Selection,
and Design. NewYork:Wiley, 1984. N. Print.
• Schaufele,The Elements ofAircraft Preliminary Design, Aries Publications,
2000
Under-Expanded Flow
Simulation
Bao Nguyen (Created program, process, and technical approach)
Teammates: Mike Krausert, Jesse Perez, RonaldAbrigo
Problem Statement
Analyze the diamond-shaped pattern seen in an
Under-expanded exhaust flow:
Conditions under which phenomena occurs
Define pattern geometry
Determine local flow properties at selected points of interest
Swiss Propulsion Laboratory
www.spl.ch
Purpose
Analysis of Diamond-Shaped Pattern in Rocket Nozzle Exhaust Flow
Geometry of characteristic mesh
Wall point location (jet boundary)
Internal point location
Slopes of expansion / compression waves
Flow properties at grid points
Local mach number and flow direction
Local temperature and pressure
Swiss Propulsion Laboratory
www.spl.ch
Purpose (continued)
Analysis of Diamond-Shaped Pattern in Rocket Nozzle Exhaust Flow
Assumptions:
Exit flow Me = Mach 3
Underexpanded flow condition: PB < Pe,6
Expansion wave consisting of 3 mach waves
Isentropic flow relations
Swiss Propulsion Laboratory
www.spl.ch
Approach
Results
Rocket Nozzle Design Input Parameters After the rocket nozzle design, we test it.
T (N) 1.20E+07 Pa (Pa) 2.65E+04 Pb ≡ P2 (psi) 5
Altitude (km) 10 Pa ≡ Pe 2.65E+04 Pe,6 ≡ P1 (psi) 10
Po (Pa) 3.45E+06 Me 3.89 Underexpanded flow
To (K) 2800 Te (K) 696.62 Me,6 ≡ M1 2.5
γ 1.4 Ue (m/s) 2055.83 Po/P1 17.09
R (J/kg.K) 287.05 m_dot (kg/s) 5837.06 Po/P2 34.17
A* ≡ At (m2) 0.57 M2 2.95
Ae (m2) 5.54 ν(M2) (deg) 48.82
ν(M1) (deg) 39.12
θ (deg) 9.70
Results (continued)
References
• Anderson, John D. Fundamentals of Aerodynamics. NewYork: McGraw-Hill,
2011. Print.
• Zikanov, Oleg. Essential Computational Fluid Dynamics. Hoboken, NJ:Wiley,
2010. Print.
Structural DesignWith
Finite Element Analysis
The Process
Step1
• Set up the model: 4 groups of stringers & stiffeners, and 5 groups of panels
• The thicknesses of all the panels: 0.002 ≤ t ≤ 0.15 in
• The cross sectional area of the stiffeners and stringers: 0.01 ≤ A ≤ 2 sq.in
Step2
• Activate FEADLAB
• Feed the program the random numbers for area and thickness within the specific ranges
• Store the data in a matrix
Step3
• Filter the data by using the criteria required for this project: margin of safety (MS) ≥ 0.5, the maximum
displacement allowable δall ≤ 1.2 in
• Guarantee: Max (MS) ≤ k*Min (MS). Constant k is empirically picked from the observation of the margin
of safety data, i.e.200.
Step4
• The data retrieved will be used to select the lightest weight, which we manually adjust to meet the
requirements.
Results
A1 A2 A3 A4 t1 t2 t3 t4 t5
0.05 0.017 0.034 0.04 0.018 0.017 0.08 0.026 0.0087
•The data retrieved will be used to select the
lightest weight, which we manually adjust to
meet the requirements.
• Results:
•With the maximum deflection of
0.95038 in, the angle of twist:
where 10 inches is the length of the
stiffener 16.The twist angle comes
out to be:
There are 8 longitudinal stringers, 8 transverse stiffeners,
4 upper and lower panels, 4 side walls or panels, and 2
transverse panels.
Forces (lbf) applied at nodes:
Node 3 (-z): 2000
Node 5 (+z): 500
Node 5 (+y): 1000
Node 6 (-z): 1000
Node 6 (+y): 2000
Node 8 (-z): 1500
Node 9 (-y): 750
Node 11 (-z): 1000
References
• Kim, Nam H., and BhavaniV. Sankar. Introduction to Finite Element Analysis
and Design. NewYork: JohnWiley & Sons, 2009. Print.
The Advection Diffusion of
CO in San BernardinoValley
Objectives
• Investigate the concentration of carbon monoxide diffused
in San Bernardino valley
• Learn how to program a simplified form of the equation
After simplifying the above equation:
A is the advection coefficient and K is the diffusivity coefficient
The process
• Collect data of wind speed and CO
concentration on 04/07/2012 f rom
3 AM to 10 PM
• Calculate the standard deviation of
CO concentration, and estimate
the diffusivity K(t):
• Optimize the calculation with the
initial condition f(x) = sin(pi*x).
The process (continued)
• function CN = CN_Mat(maxN,c1,c2,c3): Calculate the matrix with coefficients c1, c2, c3 with
parameters k(subinterval of time), h(subinterval of space) and K(diffusivity constant) by using
Crank-Nicolson method
• function AInvB = Crank_Nicolson_Const(k, h, D, maxN): Feed the function above c1, c2, c3, and
calculate A*inv(B) from the returned matrixes A, B
• function InitCondArr = InitCond(maxN,h): Calculate the initial condition data.
• function W = LW_Mat(k, h, A, maxN): Calculate the matrix with parameters k(subinterval of
time), h(subinterval of space), A(windspeed), and maxN(the number of space subintervals) by
using Lax-Wendroff method.
• function v = MatMulti(mat1, mat2, maxN): Process vector multiplication and return a vector.
• functionTimeDependent1(mat, k, h, maxN, maxM): Calculate the concentration of CO with the
assumption the wind speed and the diffusivity constant are unchanged during each subinterval
of time.
• function MainProj(): Control the program.
Results
References
• Hanna, Steven R., GaryA. Briggs, and Rayford P. Hosker. Handbook on Atmospheric Diffusion: Prepared for
theOffice of Health and Environmental Research, Office of Energy Research, U.S. Department of Energy.
[Oak Ridge,TN]:Technical Information Center, U.S. Dept. of Energy, 1982. Print.
• Cengel,YunusA., and MichaelA. Boles. "1." Thermodynamics:An EngineeringApproach.Singapore: McGraw-
Hill, 2011. Print.
• Fox, RobertW. "4." Introduction to Fluid Mechanics, by R.w. Fox. Print.
• Web. 10 May. 2012. <http://weathercurrents.com/sanbernardino/ArchiveDay.do?day=07Apr2012>.
• Web. 10 May 2012.
http://www3.aqmd.gov/webappl/aqdetail/AirQualityParameterData.aspx?Stationid=36197&AreaNumber=
34&res=1680>.
Business Jet Design
C_L vs α plot for clean, takeoff and landing flap
settings
C_Lα = 0.09
CL_max clean = 1.45;
CL_max flapup = 1.95;
CL_maxTOFlaps = 2.33;
CL_max LandingFlaps = 2.75;
Component zero lift drag buildup
C_Dp = Σ(D/q)_i/S_ref = 0.0238
Cf = 0.55/(log10RN)^2.58
Cf is a function of surface finish, and this expression is only
valid for a typical metal aircraft skin condition, as stated in the
figure.
Takeoff and landing flap setting
ΔCD flaps 15 0.007
ΔCD flaps 25 0.017
ΔCD flaps 50 0.081
ΔC_Dslat 0.006
Clean configuration drag polar : e_lowspeed =
e_cruise(0.90) = 0.68
Takeoff configuration:
*Leading edge devices extended:
*Trailing edge flaps set for takeoff
*Gear retracted
*Speed = 1.2Vstall
The total drag coefficient for this
condition may be written:
C_D vs. Mach for increasing C_L
*Our plane cruises at Mach 0.8.The airplane
will be best performed at CL = 0.2
*It has been found that for the lift coefficients
of interest for cruise, the shape of the
compressibility drag rise curve vs Mach
number may be generalized for all lift
coefficients as a function of Mdiv as shown in
fig. 12-10.
L/D vs. CL at cruise condition for different
Mach values
From the data generating cruise drag
map, by interpolation at each Mach
number, C_D is derived.
Maneuvering & Gust Envelope
Choice of materials
n = 3.46
Graphite-epoxy
ρ = 0.056 lb/in^3
σ_ult = 110 ksi to 180 ksi
References
• Schaufele,The Elements ofAircraft Preliminary Design, Aries Publications,
2000
Simulation of 2-D Heat
Diffusion
The Process
• Simulate the transient heat transfer:
• Time step must satisfy a stability criterion:
• Use spmd to run two functions in two labs separately.
• Results: (starting from first row to second row and from left to right)
References
• Incropera, Frank P. Fundamentals of Heat and MassTransfer / Frank P.
Incropera ... Hoboken, NJ: JohnWiley, 2007. Print.
Tennis Club Database
Modeling
The Process
• Use Oracle SQL to transform the logical entity
relationship diagram (ERD) into a physical data
model.
• Connect the database to ASP. Net
References
• Oracle Database 11g: SQL Fundamentals I,Volume I, II (Student Guide)

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Aerospace Engineering projects in aerodynamics, finite element analysis, math modelling, and database modelling

  • 1. Bao Nguyen Projects • Aerial GroundVehicle (December 2013) • Under-Expanded Flow Simulation (December 2013) • Structure Design with Finite Element Analysis (May 2013) • The Advection Diffusion of CO in San Bernardino Valley (May 2012) • Business Jet Design (May 2013) • Simulation of 2-D heat diffusion (August 2012) • Tennis Club Database Modeling (May 2011)
  • 3. Aerodynamics Characteristics Of Wing Sections Requirements: •CL_max clean = 1.47 •CL_max LandingFlaps = 2.42; •In 118 airfoil sections, the selected: 0012_64a, 1412, 2410, 4412, 4418, 4421 •Using a generic Matlab function to determine the airfoil with margin 0.3. (Codes at notes below) •Goal to get the highest lift coefficient among the selected: NACA 4412
  • 4. General lift distribution of the telescoping wing with NACA 4412 • The monoplane equation: • Lift coefficient: • Induced drag coefficient: • Numerical method: • Parameters: • Assumptions: thin airfoil theory, 𝒂 𝟎 = 𝟐𝝅, Prandtl lifting line theory • The lift curve for finite wing of general planform: 𝑎 = 𝑎0 1+(𝑎0/𝜋𝐴𝑅)(1+𝜏) • Assumptions: Figure 5.20: Induced drag factor δ as a function of taper ratio C_t(m) b(m) t(m) n aoa_L0 (deg) aoa_Clmax (deg) theta_rad m pts 1.27 10.3 0.002 4 -4 14 π/2 30 30 C_t/C_r ~ 1.01 Fig. 5.18: τ ~ 0.09 Lift curve (/deg): a ~ 0.0865 Numerical method C_L_α (/deg) 0.0859 % error 0.74% Errors from rounding errors and estimation from figure 5.20
  • 5. Lift coefficient and Induced drag coefficient of the wing with NACA4412 Clmax (clean) C_Lmax (clean) C_Di_max 1.7 1.55 0.0936 (The airplane maximum lift coefficient) < (The airfoil maximum lift coefficients)
  • 6. Flight Performance •Cruise CL: 0.4 to 0.8 L/D: 14.81 to 16.68 •Takeoff: CL_to: 1.57 L/D: 11.19 •Landing: CL_ld: 1.5 L/D: 10.63
  • 7. Stability and Control )1()( )( 0000 0         d d CVC c x c x CC iiCVCCC CCC tfw tfw cg LHm accg Lm twLHmmm mmm                  d d C C V C C c x c x wL tL H wL fmacNP 1 C_m_ac -0.11 S 13.10 m2 Cl_α 0.1 /deg α_0_L -4 deg b 10.30 m C_m_ac 0 Cl_α 0.125 /deg c_bar 1.27 m i_t -1 deg X_ac 0.25 c_bar l_f 4.20 m No twist d_max 2.00 m i_w 1 deg S_H 2.15 m2 l_t 5.55 m Tail airfoil sectionRef geometryWing airfoil characteristics Part b     f wf l x ffm xiw cS kk C 0 0 212 )( 5.360       f f l x u fm xw cS C 0 2 5.36 1    Nelson
  • 8. Stability and Control Results Cm_α Cm0 C_mcg_f = 0.013 α + -0.057 C_mcg_w = 0.252 α + -0.092 C_mcg_t = -2.351 α + 0.275 C_mcg = -2.086 α + 0.125 A comparison with a general aircraft from Robert Nelson’s textbook
  • 11. FAR Climb Gradient Requirement
  • 13. References • Anderson, John D. Fundamentals of Aerodynamics. NewYork: McGraw-Hill, 2011. Print. • Hale, Francis J. "Chapter 7." Introduction to Aircraft Performance, Selection, and Design. NewYork:Wiley, 1984. N. Print. • Schaufele,The Elements ofAircraft Preliminary Design, Aries Publications, 2000
  • 14. Under-Expanded Flow Simulation Bao Nguyen (Created program, process, and technical approach) Teammates: Mike Krausert, Jesse Perez, RonaldAbrigo
  • 15. Problem Statement Analyze the diamond-shaped pattern seen in an Under-expanded exhaust flow: Conditions under which phenomena occurs Define pattern geometry Determine local flow properties at selected points of interest Swiss Propulsion Laboratory www.spl.ch
  • 16. Purpose Analysis of Diamond-Shaped Pattern in Rocket Nozzle Exhaust Flow Geometry of characteristic mesh Wall point location (jet boundary) Internal point location Slopes of expansion / compression waves Flow properties at grid points Local mach number and flow direction Local temperature and pressure Swiss Propulsion Laboratory www.spl.ch
  • 17. Purpose (continued) Analysis of Diamond-Shaped Pattern in Rocket Nozzle Exhaust Flow Assumptions: Exit flow Me = Mach 3 Underexpanded flow condition: PB < Pe,6 Expansion wave consisting of 3 mach waves Isentropic flow relations Swiss Propulsion Laboratory www.spl.ch
  • 19. Results Rocket Nozzle Design Input Parameters After the rocket nozzle design, we test it. T (N) 1.20E+07 Pa (Pa) 2.65E+04 Pb ≡ P2 (psi) 5 Altitude (km) 10 Pa ≡ Pe 2.65E+04 Pe,6 ≡ P1 (psi) 10 Po (Pa) 3.45E+06 Me 3.89 Underexpanded flow To (K) 2800 Te (K) 696.62 Me,6 ≡ M1 2.5 γ 1.4 Ue (m/s) 2055.83 Po/P1 17.09 R (J/kg.K) 287.05 m_dot (kg/s) 5837.06 Po/P2 34.17 A* ≡ At (m2) 0.57 M2 2.95 Ae (m2) 5.54 ν(M2) (deg) 48.82 ν(M1) (deg) 39.12 θ (deg) 9.70
  • 21. References • Anderson, John D. Fundamentals of Aerodynamics. NewYork: McGraw-Hill, 2011. Print. • Zikanov, Oleg. Essential Computational Fluid Dynamics. Hoboken, NJ:Wiley, 2010. Print.
  • 23. The Process Step1 • Set up the model: 4 groups of stringers & stiffeners, and 5 groups of panels • The thicknesses of all the panels: 0.002 ≤ t ≤ 0.15 in • The cross sectional area of the stiffeners and stringers: 0.01 ≤ A ≤ 2 sq.in Step2 • Activate FEADLAB • Feed the program the random numbers for area and thickness within the specific ranges • Store the data in a matrix Step3 • Filter the data by using the criteria required for this project: margin of safety (MS) ≥ 0.5, the maximum displacement allowable δall ≤ 1.2 in • Guarantee: Max (MS) ≤ k*Min (MS). Constant k is empirically picked from the observation of the margin of safety data, i.e.200. Step4 • The data retrieved will be used to select the lightest weight, which we manually adjust to meet the requirements.
  • 24. Results A1 A2 A3 A4 t1 t2 t3 t4 t5 0.05 0.017 0.034 0.04 0.018 0.017 0.08 0.026 0.0087 •The data retrieved will be used to select the lightest weight, which we manually adjust to meet the requirements. • Results: •With the maximum deflection of 0.95038 in, the angle of twist: where 10 inches is the length of the stiffener 16.The twist angle comes out to be: There are 8 longitudinal stringers, 8 transverse stiffeners, 4 upper and lower panels, 4 side walls or panels, and 2 transverse panels. Forces (lbf) applied at nodes: Node 3 (-z): 2000 Node 5 (+z): 500 Node 5 (+y): 1000 Node 6 (-z): 1000 Node 6 (+y): 2000 Node 8 (-z): 1500 Node 9 (-y): 750 Node 11 (-z): 1000
  • 25. References • Kim, Nam H., and BhavaniV. Sankar. Introduction to Finite Element Analysis and Design. NewYork: JohnWiley & Sons, 2009. Print.
  • 26. The Advection Diffusion of CO in San BernardinoValley
  • 27. Objectives • Investigate the concentration of carbon monoxide diffused in San Bernardino valley • Learn how to program a simplified form of the equation After simplifying the above equation: A is the advection coefficient and K is the diffusivity coefficient
  • 28. The process • Collect data of wind speed and CO concentration on 04/07/2012 f rom 3 AM to 10 PM • Calculate the standard deviation of CO concentration, and estimate the diffusivity K(t): • Optimize the calculation with the initial condition f(x) = sin(pi*x).
  • 29. The process (continued) • function CN = CN_Mat(maxN,c1,c2,c3): Calculate the matrix with coefficients c1, c2, c3 with parameters k(subinterval of time), h(subinterval of space) and K(diffusivity constant) by using Crank-Nicolson method • function AInvB = Crank_Nicolson_Const(k, h, D, maxN): Feed the function above c1, c2, c3, and calculate A*inv(B) from the returned matrixes A, B • function InitCondArr = InitCond(maxN,h): Calculate the initial condition data. • function W = LW_Mat(k, h, A, maxN): Calculate the matrix with parameters k(subinterval of time), h(subinterval of space), A(windspeed), and maxN(the number of space subintervals) by using Lax-Wendroff method. • function v = MatMulti(mat1, mat2, maxN): Process vector multiplication and return a vector. • functionTimeDependent1(mat, k, h, maxN, maxM): Calculate the concentration of CO with the assumption the wind speed and the diffusivity constant are unchanged during each subinterval of time. • function MainProj(): Control the program.
  • 31. References • Hanna, Steven R., GaryA. Briggs, and Rayford P. Hosker. Handbook on Atmospheric Diffusion: Prepared for theOffice of Health and Environmental Research, Office of Energy Research, U.S. Department of Energy. [Oak Ridge,TN]:Technical Information Center, U.S. Dept. of Energy, 1982. Print. • Cengel,YunusA., and MichaelA. Boles. "1." Thermodynamics:An EngineeringApproach.Singapore: McGraw- Hill, 2011. Print. • Fox, RobertW. "4." Introduction to Fluid Mechanics, by R.w. Fox. Print. • Web. 10 May. 2012. <http://weathercurrents.com/sanbernardino/ArchiveDay.do?day=07Apr2012>. • Web. 10 May 2012. http://www3.aqmd.gov/webappl/aqdetail/AirQualityParameterData.aspx?Stationid=36197&AreaNumber= 34&res=1680>.
  • 33. C_L vs α plot for clean, takeoff and landing flap settings C_Lα = 0.09 CL_max clean = 1.45; CL_max flapup = 1.95; CL_maxTOFlaps = 2.33; CL_max LandingFlaps = 2.75;
  • 34. Component zero lift drag buildup C_Dp = Σ(D/q)_i/S_ref = 0.0238 Cf = 0.55/(log10RN)^2.58 Cf is a function of surface finish, and this expression is only valid for a typical metal aircraft skin condition, as stated in the figure.
  • 35. Takeoff and landing flap setting ΔCD flaps 15 0.007 ΔCD flaps 25 0.017 ΔCD flaps 50 0.081 ΔC_Dslat 0.006 Clean configuration drag polar : e_lowspeed = e_cruise(0.90) = 0.68 Takeoff configuration: *Leading edge devices extended: *Trailing edge flaps set for takeoff *Gear retracted *Speed = 1.2Vstall The total drag coefficient for this condition may be written:
  • 36. C_D vs. Mach for increasing C_L *Our plane cruises at Mach 0.8.The airplane will be best performed at CL = 0.2 *It has been found that for the lift coefficients of interest for cruise, the shape of the compressibility drag rise curve vs Mach number may be generalized for all lift coefficients as a function of Mdiv as shown in fig. 12-10.
  • 37. L/D vs. CL at cruise condition for different Mach values From the data generating cruise drag map, by interpolation at each Mach number, C_D is derived.
  • 38. Maneuvering & Gust Envelope Choice of materials n = 3.46 Graphite-epoxy ρ = 0.056 lb/in^3 σ_ult = 110 ksi to 180 ksi
  • 39. References • Schaufele,The Elements ofAircraft Preliminary Design, Aries Publications, 2000
  • 40. Simulation of 2-D Heat Diffusion
  • 41. The Process • Simulate the transient heat transfer: • Time step must satisfy a stability criterion: • Use spmd to run two functions in two labs separately. • Results: (starting from first row to second row and from left to right)
  • 42. References • Incropera, Frank P. Fundamentals of Heat and MassTransfer / Frank P. Incropera ... Hoboken, NJ: JohnWiley, 2007. Print.
  • 44. The Process • Use Oracle SQL to transform the logical entity relationship diagram (ERD) into a physical data model. • Connect the database to ASP. Net
  • 45. References • Oracle Database 11g: SQL Fundamentals I,Volume I, II (Student Guide)

Editor's Notes

  • #4: function NACA_Clmax() Clmax = [1 1.8 0.9;2 2.1 1.3;3 1.8 0.8;4 1.8 0.8;5 1.9 1.0;6 2.4 1.6;7 2.3 1.4;8 2.5 1.6;... 9 2.1 1.3;10 2.3 1.5;11 2.5 1.6;12 2.3 1.5;13 2.5 1.7;14 1.7 1.2;15 1.6 1.2;16 1.5 1.1;17 1.5 0.9;... 18 1.3 0.8;19 2.7 1.7;20 2.8 1.6;21 2.7 1.5;22 2.7 1.5;23 2.7 1.4;24 1.8 1.2;25 1.7 1.2;26 1.6 1.1;... 27 1.5 0.9;28 1.4 0.8;29 1.8 0.9;30 1.8 1.2;31 1.9 1.1;32 2.1 1.4;33 2.1 1.5;34 2.2 1.5;35 2.4 1.2;... 36 2.5 1.3;37 2.4 1.1;38 2.6 1.3;39 2.7 1.3;40 2.8 1.4;41 2.7 0.9;42 2.8 0.9;43 2.8 1.0;44 2.9 0.8;... 45 2.7 0.7;46 2.7 0.6;47 2.7 0.7;48 1.8 0.6;49 2 1.0;50 1.8 0.4;51 1.8 0.7;52 1.8 0.7;53 2 0.8;... 54 1.8 0.8;55 1.9 0.8;56 2 0.9;57 2 0.9;58 2.2 0.9;59 2.3 1.0;60 2.4 1.0;61 2.5 1.2;62 2.4 0.9;... 63 2.6 1.0;64 2.7 0.9;65 2.7 1.0;66 2.7 0.8;67 2.8 0.9;68 2.8 0.9;69 2.8 0.7;70 2.7 0.8;71 2.8 0.7;... 72 1.9 0.7;73 2.2 0.8;74 2.4 1.0;75 2.4 0.9;76 2.6 1.2;77 1.8 0.9;78 1.8 1.1;79 1.8 1.0;80 2 1.0;... 81 2.1 1.0;82 2.2 1.2;83 2.1 1.0;84 2.3 1.1;85 2.3 1.0;86 2.4 1.3;87 2.5 1.0;88 2.5 1.1;89 2.6 1.2;... 90 1.6 1.4;91 2.6 1.1;92 2.7 1.1;93 2.7 1.2;94 1.5 1.2;95 2.6 1.3;96 1.5 1.2;97 2.7 1.1;98 2.7 1.2;... 99 2.7 1.2;100 1.4 1.1;101 1.8 0.8;102 1.8 1.1;103 1.8 1.0;104 2 1.2;105 2 1.0;106 2.1 0.9;107 2.2 1.0;... 108 2.4 1.0;109 2.5 1.1;110 2.6 1.2;111 2.6 1.1;112 2.7 1.1;113 2.6 1.3;114 2.5 1.0;115 2.5 1.2;116 1.5 1.0;... 117 2.6 1.1;118 2.7 1.2]; selc = zeros(length(Clmax),1); GAV_Clmax_landing = 2.42; GAV_Clmax_clean = 1.47; for n = 1:118 if (Clmax(n,2) >= (GAV_Clmax_landing))&&(Clmax(n,2) <= (GAV_Clmax_landing+0.3))&&... (Clmax(n,3) >= (GAV_Clmax_clean))&&(Clmax(n,3) <= (GAV_Clmax_clean+0.3)) selc(n)=Clmax(n,1); end end %Write all the data to a file. dlmwrite('MAE478_GAV_Airfoil.xls',selc, ';'); warning off MATLAB:xlswrite:AddSheet;
  • #5: %@Ct: c_tip %@Cr: c_root %@b: wing span %@c_bar: mean root wing cord %@t: gap or metal thickness between telescoping sections %@n: number of telescoping sections %@aoa_L0: the zero lift angle of attack, ie., airfoil 4412: aoa_L0 = -4 deg %@aoa_Clmax: the angle of attack at Clmax, ie., airfoil 4412: aoa_Clmax(clean) = 14 deg %@theta_rad: the interval of angle under investigation of a wing, 0<=theta_rad<=pi %@m: the number of A coefficients %@pts: the number of points to draw on a curve of C_D,i and CL vs. aoa %Current %config:Ct=1.27;b=10.3;t=0.002;n=4;aoa_L0=-4;aoa_Clmax=14;theta_rad=pi/2;m=20;pts=20;
  • #6: *Since a small amount of lift is carried on other parts of the airplane, such as the fuselage, nacelles, and horizontal tail, the value of the airplane lift curve slope is slightly higher than the wing lift curve slope. *An analysis of all these parameters is made in the preliminary design phase of the program, with the objective of having the angle of attack of the fuselage reference plane, or floor line the passenger cabin, between 0 and 2 degrees at the cruise condition. *The maximum lift coefficient in the cruise configuration is dependent on two primary parameters, the spanwise variation of local wing section lift coefficients, as the wing approaches the angle of attack for stall, and the wing airfoil section maximum lift coefficients, which are unique values for each airfoil section. *For performance reasons, airfoil sections with high values of maximum lift coefficient are usually selected to achieve the highest value of airplane maximum lift coefficient. *However, as shown in fig.11.2, the airfoil section maximum lift coefficient values are usually varied across the span, so that the wing spanwise lift distribution will reach values of the airfoil maximum lift coefficients over the inboard portion of the wing, producing local stalling inboard while maintaining some lift coefficient margin to stall over the outer portion of the wing. *This margin in lift coefficient to stall is used to protect against initial local stalling over the outer portion of the wing, which leads to severe roll off and loss of aileron control at the stall. *For this reason, the airplane maximum lift coefficient can never be as high as the airfoil maximum lift coefficients.
  • #7: Single slotted flaps are standard for personal/utility aircraft. Single slotted flap chords are usually in the range of 25% and 30% of the chord, and have a max deflection of 35 degrees.
  • #11: Ref: Loftin’s
  • #12: SSLW: second segment limited weight
  • #16: Characteristic lines represent left-and right-running mach waves Characteristic mesh consists of intersecting mach waves crossing the flow field. Local mach angle (mu) is a function of mach number, which is function of x & y
  • #18: Characteristic lines represent left-and right-running mach waves Characteristic mesh consists of points of intersection of mach waves crossing the flow field. Local mach angle (mu) is a function of mach number, which is function of x & y
  • #21: We have obtained an accurate graphical result as shown above. Further optimization will be done at the analysis to the solution of the boundary points.
  • #34: *The maximum lift coefficient in the cruise configuration is dependent on two primary parameters, the spanwise variation of local wing section lift coefficients, as the wing approaches the angle of attack for stall, and the wing airfoil section maximum lift coefficients, which are unique values for each airfoil section. *For performance reasons, airfoil sections with high values of maximum lift coefficient are usually selected to achieve the highest value of airplane maximum lift coefficient. *However, as shown in fig.11.2, the airfoil section maximum lift coefficient values are usually varied across the span, so that the wing spanwise lift distribution will reach values of the airfoil maximum lift coefficients over the inboard portion of the wing, producing local stalling inboard while maintaining some lift cofficient margin to stall over the outer portion of the wing. *This margin in lift coefficient to stall is used to protect against initial local stalling over the outer portion of the wing, which leads to severe roll off and loss of aileron control at the stall. *For this reason, the airplane maximum lift coefficient can never be as high as the airfoil maximum lift coefficients. *This margin in lift coefficient to stall is used to protect against initial local stalling over the outer portion of the wing, which leads to severe roll off and loss of aileron control at the stall. *For this reason, the airplane maximum lift coefficient can never be as high as the airfoil maximum lift coefficients.
  • #35: D/q has the units of area, and may also be written as f, the equivalent flat plate area (or equivalent parasite drag area). Most components (wing, tail, fuselage, etc.) area aerodynamically smooth, so the drag is mostly due to skin friction, and drag is calculated using skin friction coefficient. Some components, such as deployed landing gear and flaps, and flap hinges are bluff, so drag is mostly due to separation. For these cases, drag is often calculated based directly on the equivalent flat plate area f, and skin friction drag is not calculated. Drag of bluff components may be stated as f/(unit area), in which case this coefficient must be multiplied by the component frontal area (normal to the flow) to obtain the value of f. The skin friction coeff. decreases with increasing RN.
  • #39: The higher of the two load factors must be used for structural design.