Exeley Inc.

Zhenghua, Xin

Guolong, Chen

Li, Hong

Song, Qixiang

Lei, Chen

Yaqi, Shi

Gang, Wu

THE IMPLEMENTATION FOR THE INTELLIGENT HOME CONTROL SYSTEM BASED ON THE ANDROID AND ZIGBEE

Wang, Lijuan

Liu, Xiaojing

RESEARCH ON UNDERWATER TARGET SIGNAL DETECTION AND RECOGNITION PROCESSING ALGORITHM

L. Jaheel, Hamdan

Beiji, Zou

SECURITY METHOD FOR COMBINATION STEGANOGRAPHY ALGORITHMS ON TRANSFORM DOMAIN FOR JPEG IMAGES

VAIČIŪNAS, Gediminas

ASSESSMENT OF RAILWAY LINES: AN EFFICIENCY RATING ANALYSIS FOR BALTIC COUNTRIES

HANISZEWSKI, Tomasz

English

PRELIMINARY MODELLING STUDIES OF AN EXPERIMENTAL TEST STAND OF A CRANE, FOR INVESTIGATION OF ITS DYNAMIC PHENOMENA OF LIFTING AND DRIVING MECHANISM

JANÁČEK, Jaroslav

KVET, Marek

TRANSPORT PROBLEMS                                                        2017 Volume 12 Issue: Special Edition 
PROBLEMY TRANSPORTU                                                                DOI: 10.20858/tp.2017.12.se.12 
 
Keywords: detrimental event; robust service system; 
transportation performance; critical arc characteristic 
 
Jaroslav JANÁČEK*, Marek KVET 
University of Žilina, Faculty of Management Science and Informatics 
Univerzitná 8215/1, 01026 Žilina, Slovakia 
*Corresponding author. E-mail: jaroslav.janacek@fri.uniza.sk 
 
 
 
CHARACTERISTIC OF A CRITICAL NETWORK ARC IN A SERVICE 
SYSTEM 
 
Summary. Vulnerability of a transportation network influences, importantly, the 
function of public service systems constituted on the network. To be able to study the 
importance of an individual network arc for proper functionality of the system, we 
suggested a characteristic function of the arc transit time elongation together with its 
upper and lower estimations. We also suggested and verified an algorithm that enables 
the determination of parameters of the characteristic estimation, and we provide a reader 
with a computational study performed with real road networks. 
 
 
1. INTRODUCTION 
 
Performance of each service system depends on reliability of the associated transportation network 
[4, 8-10]. In a real transportation network, various random events may occur, which can considerably 
elongate the time necessary to traverse the affected arc [7]. The effect of individual arc collapse was 
studied in [4, 9, 10], and some measures such as “Network robustness index” and “Network trip 
robustness” were also suggested. The individual arc elongation may or may not influence performance 
of the service system. It depends on the position of the arc with respect to paths, which are or can be 
used for transport from service centers to the system users [3]. Within this paper, we focus on 
identification of the network arcs, which have decisive impact on a given service system performance 
in the case when traversing time of the arcs is elongated owing to some detrimental event. We suggest 
a characteristic of an individual arc that enables the estimation of the impact of arc failure on the 
system performance, and we also present an algorithm that enables the determination of parameters of 
the characteristic. Furthermore, we provide a classification of possible characteristics. 
The remainder of the paper is organized as follows. The next section introduces notions connected 
with performance of a service system. The third section contains the description of the algorithm for 
arc characteristic determination, together with a suggestion of characteristic classification. The fourth 
section presents the numerical experiments with the algorithm performed on models of real road 
networks. The final section summarizes the obtained findings. 
 
 
2. SERVICE SYSTEM AND ARC CHARACTERISTIC 
 
The considered service system is designed to provide users spread over some geographical area 
with some specific service (e.g. medical first aid, fireman service etc.). The system users are 
concentrated in dwelling places, which represent nodes of the transportation network connecting the 
dwelling places and some other sites, where a service facility may be located. Structure of a service 
system is determined by a deployment of service centers, from which the given kind of service is 
provided. It is assumed that the shortest path from the nearest service center to a serviced user location 
142                                                                                                                               J. Janáček, M. Kvet 
 
is used [1-3, 6]. The system is represented by a set I1 of p service center locations and by a set J of user 
locations, where volume bj of demand is associated with the user location j. We assumed that the 
transportation network used for servicing of user demands is symmetrical, which means that each link 
connecting pair of nodes can be traversed in both directions. Symbol tij denotes the time length of the 
shortest path from i to j in the underlying network. The set of associated arcs forming the path is 
denoted by Pij. Let us denote ass(j)ÎI1 the center, which provides user j with service. Then, the 
original total transport performance TP* of the service system can be computed according to (1). 
 
å
Î
=
Jj
jjassjtbTP ),(
*           (1) 
 
Let us denote (u, v) an arc with traversing time tuv. We will deal with the impact of elongation of tuv 
by D on the total transport performance of the system. We realize that arc (u, v) may or may not be the 
element of a path Pass(j),j for some jÎJ. If the arc does not belong to any of the paths, it cannot influence 
the transport performance and it will be called irrelevant. A relevant arc can belong to several paths. 
We denote JuvÍ J the set of jÎJ, for which (u, v) Î Pass(j),j. Now, we try to analyze the progress of 
transport performance TP(D) with increasing value of D representing an elongation of the traversing 
time tuv. If the value of D is near to zero, the value TP(D) will grow linearly with a slope (derivative) 
B1, because each original path Pass(j),j for any jÎJuv stays the shortest one. The starting slope B1 of the 
piece-wise linear increasing concave function TP(D) equals the sum of all demands bj of all jÎJuv, 
which have no equivalent alternative path to a service center. The equivalent alternative path means a 
path that does not contain the arc (u, v) and has the same length as the original shortest path Pass(j),j. 
When D reaches the value, for which some of the paths result in not being the shortest one, then the 
associated user j will be serviced by the shortest path in the updated network to some service center 
from I1, and this path will not contain the arc (u, v). Then the slope of TP(D) behind the enlarged D 
decreases by bj. This situation can repeat several times during increase D up to some value e2, when 
the slope of TP(D) decreases last time (see Fig. 1).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1. Progress of the transportation performance TP(D) depending on elongation D is depicted by a full line; the 
           upper and lower bounds of the progress are depicted by dotted and dashed lines, respectively 
 
When D exceeds the value e2, the slope of TP(D) stays constant. This final slope will be denoted by 
B2. The value of B2 is the sum of all user demands bj of users jÎJuv, which can be connected to a 
service center only by a path containing the arc (u, v). The symbol P denotes the value of TP(e2) for 
the elongation D=e2. Elongation of the original traversing time tuv behind the value e2 affects only the 
users, who cannot avoid using the arc (u, v) on arbitrary path to a service center. We have to note that 
D 0 
TP* 
TP(D) 
e2 e1 
P+TP* 
B1 
B2 
Characteristic of a critical network arc in…                                                                       143. 
 
a special relation holds for a symmetrical network. If any user from Juv has an alternative path not 
containing the arc (u, v), then all users from Juv have their own alternative paths. 
The values of e2, B1, B2 and P are sufficient to determine graphs of the upper or lower estimation of the 
characteristic TP(D)-TP*. The value of e1 can be computed according to (2) under assumption B1 >B2. 
 
21
22
1 BB
eBPe
-
-
=             (2) 
 
In the case B1=B2, the characteristic is a linear function with the slope B1. In this case, we set e2= 
e2=0. It follows from the network symmetricity that if B1 >B2, then B2 = 0. 
The relevant arcs satisfying B1 >0 can be classified according to coefficients B1 and B2. Three basic 
classes of the relevant arcs will be distinguished. 
 
a)   B1 >B2=0 and e1 = e2; piece-wise linear concave function limited from above 
 
b)   B1 >B2=0 and e1 < e2; piece-wise linear concave function limited from above 
 
c)   B1 =B2; linear growing function  
 
An algorithm, which enables to determine the values of e2, B1, B2 and P, is introduced in the next 
section. 
 
 
3. COMPUTATION OF THE ARC CHARACTERISTIC PARAMETERS 
 
The presented algorithm uses Bellman, Ford and Moor algorithm [5] to compute the shortest paths 
from all users to the nearest service center in the adjusted network. The resulting nearest center to a 
user j will be denoted by ass(j) and the time length of the associated path will be denoted by t’ass(j),j. 
Let T be enough big constant, e.g. sum of all arc traversing times is satisfactorily big. Then, the 
algorithm for determination of arc (u, v) characteristic parameters can be stated as follows: 
 
0. Set B1=0, B2=0 and P=0. 
 
1. Change evaluation tuv of the arc (u, v) so that the updated evaluation equals tuv+T. 
 
2. Compute new shortest paths from each jÎJuv to the nearest service center. The results of the 
computation are the subscript ass(j) of the nearest service center and the length t’ass(j),j of the 
new path. 
 
3. Process all users j from Juv in the following way: 
If t’ass(j),j ≥ tass(j),j+T then 
         update B2= B2+bj 
else   update B1= B1+bj 
         e2= max{ e2, t’ass(j),j - tass(j),j} 
         P= P+( t’ass(j),j - tass(j),j) bj. 
 
4. If B1= 0 then 
         set e1=0 and e2=0 
else   update e1=P/B1 
         B1= B1+ B2 
         P= P+B2 e2. 
 
144                                                                                                                               J. Janáček, M. Kvet 
 
5. Terminate. 
 
After the algorithm has been performed, the upper and lower bounds of the characteristic of arc  
(u, v) are defined according to the following definitions: 
 
If D<e1 then Uc(D)=B1D else Uc(D)=B1 e1+ B2D. 
 
If D<e2 then Lc(D)=(P/e2)D else Lc(D)=P+ B2D. 
 
 
4. COMPUTATIONAL STUDY 
 
Within this section, we present a computational study focused on real network arc characteristics 
regarding a given public service system. 
The used benchmarks were derived from the real emergency healthcare system, which was 
originally implemented in selected regions of Slovakia. For each self-governing region, i.e. Bratislava 
(BA), Banská Bystrica (BB), Košice (KE), Nitra (NR), Prešov (PO), Trenčín (TN), Trnava (TT) and 
Žilina (ZA), all cities and villages (communities representing elements of the set J) with 
corresponding number bj of inhabitants were taken into account for each j from J. The coefficients bj 
were rounded to hundreds. These sub-systems cover demands of all communities – towns and villages 
spread over the particular regions by a given number of ambulance vehicles. In the benchmarks, the 
set of communities represents both the set J of users’ locations and also the set I of possible service 
center locations. The cardinalities of these sets vary from 87 to 664 according to the considered region. 
The number p of located centers was derived from the original design, and it varies from 22 to 46. 
Individual subnetwork parameters are described in Table 1. 
 
Table 1 
Parameters of the individual networks, in which  
the service systems were designed 
 
 
Region 
 
 
| J |  
 
| I | 
 
p 
BA 87 87 25 
BB 515 515 46 
KE 460 460 38 
NR 350 350 36 
PO 664 664 44 
TN 276 276 26 
TT 249 249 22 
ZA 315 315 29 
 
 
The final set of center locations was obtained by solving weighted p-median problem for each self-
governing region. Then, the set of relevant network arcs was identified and upper and lower 
estimations of the characteristic for each relevant arc were determined regarding the designed service 
system. The set of relevant arcs was partitioned into three classes according to the types of 
characteristics mentioned at the end of Section 2. The classes a) and c) contain such arcs, for which the 
upper and lower characteristic estimations coincide and thus upper and lower estimations describe 
exactly the arc characteristic. The class b) contains network arcs, in which upper and lower 
estimations differ. The mean of upper and lower characteristic estimations were computed for each arc 
Characteristic of a critical network arc in…                                                                       145. 
 
of the class b) on characteristic domain from 0 to e2 to evaluate mean of maximal deviation of the 
estimations from the actual characteristic. 
To perform the algorithm described in the previous sections, the programming environment 
NetBeans 8.1 was used and the associated algorithms were programmed in Java language and the 
experiments were run on a PC equipped with the Intel® Core™ i7 5500U processor with the 
following parameters: 2.4 GHz and 16 GB RAM.  
The achieved results are summarized in the Table 2, where “Rel. arcs” means the number of 
relevant arcs that were found. The denotations “a) arcs”, “b) arcs” and “b) arcs” denote columns where 
the number of arcs of type a), b) and c) are plotted, respectively. The column denoted by “avg. dif. 
[%]” contains average of difference of upper and lower characteristic estimations on characteristic 
domain from 0 to e2 for arcs of the class b) expressed in percentage of P. 
Table 2 
Parameters of the individual subnetworks, in which the public service  
systems were designed 
 
 
Region 
 
 
Rel. arcs  
 
a) arcs 
 
b) arcs 
 
c) arcs 
 
b) avg. dif. 
[%] 
BA 83 49 17 17 15.17 
BB 593 191 201 201 17.86 
KE 523 185 171 167 17.81 
NR 408 194 148 66 17.26 
PO 753 177 295 281 18.39 
TN 302 105 91 106 18.18 
TT 301 166 90 45 16.91 
ZA 370 105 114 151 16.51 
 
The results of the computational study performed on real transportation networks of individual 
self-governing regions of the Slovak Republic show that there is a considerable portion of c-class arcs 
in each transportation network. The size of the portion is approximately one-third of the total number 
of arcs. It means that these arcs have no detour in the given network, and thus these arcs deserve a 
special attention by the network administrator. 
It can be noticed that the portion of a-class arcs represent also one-third of the total number of arcs, 
which means that our upper estimation of the arc characteristic is equivalent to the exact arc 
characteristic. 
Regarding upper and lower estimations of the b-class arc characteristic, the values reported in the 
last column of the table indicate that the characteristic values may be at most by 19 percent lower than 
the suggested upper estimation. 
 
 
5. CONCLUSIONS 
 
This paper was focused on means of network arc importance investigation, where the importance is 
considered to be relative regarding a given public service system. A smart algorithm was suggested to 
find parameters describing the upper and lower estimations of the characteristics of relevant arcs. 
These estimations enable the evaluation of the importance of an individual arc for proper functioning 
of the service system. The obtained parameters enable the partitioning of the set of relevant arcs into 
several classes and separate the cut-arcs from the arcs that have an alternative if the arc collapses. This 
way, we presented a tool for transportation network analysis from the point of performance of a 
service system. The attached computational study demonstrates the use of this algorithm for real road 
network analysis and also gives a level of accuracy of the lower and upper estimations regarding the 
exact arc characteristic. 
146                                                                                                                               J. Janáček, M. Kvet 
 
The future research in this field will be focused on exploitation of the suggested algorithm and 
associate arc evaluations for construction of an effective set of detrimental scenarios destined for 
robust service system designing. 
 
 
Acknowledgement 
 
This work was supported by the research grants VEGA 1/0518/15 “Resilient rescue systems with 
uncertain accessibility of service”, VEGA 1/0463/16 “Economically efficient charging infrastructure 
deployment for electric vehicles in smart cities and communities”, APVV-15-0179 “Reliability of 
emergency systems on infrastructure with uncertain functionality of critical elements”. 
 
 
References 
 
1. García, S. & Labbé, M. & Marín, A. Solving large p-median problems with a radius formulation. 
INFORMS Journal on Computing. 2011. Vol. 23. No. 4. P. 546-556. 
2. Janáček, J. & Kvet, M. Relevant Network Distances for Approximate Approach to Large p-
Median Problems. In: Proceedings of International Annual Conference "GOR". Hannover: 
Leibnitz Universitat Hannover. 2012. P. 123-128. 
3. Jánošíková, L. Emergency Medical Service Planning. Communications –Scientific Letters of the 
University of Žilina. 2007. Vol. 9. No. 2. P. 64-68. 
4. Jenelius, E. Network structure and travel patterns: explaining the geographical disparities of road 
network vulnerability. Journal of Transport Geography. 2009. Vol 17. P. 234-244. 
5. Jukna, S. & Schnitger, G. On the optimality of Bellman–Ford–Moore shortest path algorithm. 
Theoretical Computer Science. 2016. Vol. 628. P. 101-109. 
6. Kvet, M. 2014. Computational Study of Radial Approach to Public Service System Design with 
Generalized Utility. In: Proceedings of International Conference "Digital Technologies". Žilina: 
University of Zilina. 2014. P. 198-208. 
7. Kvet, M. & Matiaško, K. Concept of dynamic index management in temporal approach using 
intelligent transport systems. Recent advances in information systems and technologies. 2017. 
Vol. I. Springer. P. 549-560.  
8. Majer, T. & Palúch, S. Rescue System Resistance to Failures in Transport Network. 
In: Proceedings of 34th International Conference "Mathematical Methods in Economics". 
Liberec: Technical University of Liberec. 2016. P. 518-522. 
9. Scott, D.M. & Novak, D.C. & Aultman-Hall, L. & Guo, F. Network robustness index a new 
method for identifying critical links and evaluating the performance of transportation networks. 
Journal of Transport Geography. 2006. Vol. 14. No. 3. P. 215-227. 
10. Sullivan, J.L. & Novak, D.C. & Aultman-Hall, L. & Scott, D.M. Identifying critical road segments 
and measuring system-wide robustness in transportation networks with isolating links: A link-
based capacity-reduction problem. Transportation Research Part A. 2010. Vol. 44. P. 323-336. 
 
 
Received 09.03.2017; accepted in revised form 21.11.2017 
 


CHARACTERISTIC OF A CRITICAL NETWORK ARC IN A SERVICE SYSTEM

El-Sadi, Haifa

059
Aircraft Design Analysis, CFD and 
manufacturing
Haifa El-Sadi*
Wentworth institute of technology
550 Huntington, Boston, MA, USA
*correspondent author
Abstract.Aircraft design, manufacturing and CFD analysis as part of aerodynamic course, the 
students achieve sizing from a conceptual sketch, select the airfoil geometry and the tail geometry, calculate 
thrust to weight ratio and wing loading, use initial sizing and calculate the aerodynamic forces. The 
students design their aircraft based on the geometrical dimensions resulted from the calculations and use 
the model to build a prototype, test it in wind tunnel and achieve CFD analysis to be compared with the 
experimental results. The theory of aerodynamic is taught and applied as a project based. In this paper, the 
design process, aircraft manufacturing and CFD analysis are presented to show the effect of project based 
on student’s learning of aerodynamic course. This project based learning has improved and accelerated 
students understanding of aerodynamic concepts and involved students in a constructive exploration.
The analysis of the aircraft resulted in a study that revolved around the lift and drag generation of this 
particular aircraft. As to determine the lift and drag forces generated by this plane, a model was created in 
Solidworks a 3-D model-rendering program. After this model was created it was 3-D printed in a reduced 
scale, and subjected to wind tunnel testing. The results from the wind tunnel lab experiment were recorded. 
For accuracy, the same 3-D model was then simulated using CFD simulation software within Solidworks 
and compared with the results from the wind tunnel test. The values derived from both the simulation and 
the wind tunnel tests were then compared with the theoretical calculations for further proof of accuracy.
Keywords: computational fluid dynamics (CFD), design, aircraft, aerodynamic, wind tunnel
1. Introduction
The conceptual design phases begins a conceptual sketch and aims to determine key design parameters 
that the final aerodynamics will have to meet. This design will typically include the approximate wing and 
International Journal of Advanced Network  Monitoring  and Controls                     Year 2016, No. 02
International Journal of Advanced Network  Monitoring  and Controls                     Year 2016, No. 02
060
tail geometries, fuselage shape, and the internal locations of major components such as the engine, cockpit, 
payload/passenger compartments, landing gears, and fuel tanks. These design requirements are used to 
estimate the weight of the final aircraft by comparison to previous designs. The takeoff weight is a critical 
characteristic that will dictate the final size and shape of the airfoil. Iterative calculations for this weight 
are made using assumptions from previous aircraft designs and aerodynamic AIAA table standards [1, 4, 
5, and 7]. The final lift requirements will then determine required airfoil size and shape, with more iterative 
refinements made between steps.
Gennaro Zuppardi shows that an interactive and wholly automatized computer code has been developed 
on a microcomputer for the aerodynamic analysis of airfoils in incompressible now fields, it is intended 
to serve as a useful support in teaching aerodynamics. The code contains a number of modules (or 
blocks) for: (1) drawing the shape with the help of an interactive graphic device interfaced with the 
microcomputer; (2) computing the aerodynamic inviscid and viscous flow field and the aerodynamic 
coefficients; (3) modifying and/or correcting the body shape and then computing the new aerodynamic 
coefficients [6]. Mark Drela presents some of his views on teaching fluid dynamics and aerodynamics that 
the course syllabus stresses physical and mathematical understanding of underlying concepts rather than 
specialized engineering or computational skills, it
is argued that deep understanding is what enables the engineer or researcher to generate truly new ideas 
and work on out of the ordinary topics and to continue personal learning and development throughout a 
career [3, 7].
The goal of this paper and its research is to show the students the steps of aerodynamic design and perform 
their own design and compare three types of acquired lift results for their own designed aircraft. Following 
proven aerodynamic formulas and AIAA airfoil charts, assumptions were made to provide a baseline 
weight from which the iterations were run to refine the final design weight. This finalized weight was then 
used to calculate the geometry of the wings, fuselage, airfoil, and tail section of the aircraft. This geometry 
was used to model the complete aircraft in computer aided design software and a 1:584 scale model was 3D 
printed for wind tunnel testing. The wind tunnel was used to measure lift and drag forces for various angles 
of attack that could then be compared to both the iterative calculations as well as the results calculated 
through computational fluid dynamics.
2. Design Analysis Methods
2.1 Phase #1 theoretical calculations
The theoretical calculations is a useful method as to further define the usage of the aircraft. These 
calculations will give an idea of the basic structure to the design team. Properties that are highly important 
to a newly designed aircraft are directly resulted from this stage of design. The design team will use this tool 
to determine the range and weight limitations of the aircraft. Before the modeling phase a type of wing will 
be selected and further refined in later phases of design.
The design weight was calculated by taking the weight of everything that was part of the plane and adding it 
all together. This was used to initially calculate the weight to be used for the design.
061
The empty weight was calculated in accordance with the Wo guess weight in order to iterate with the 
calculated design weight. By using multiple iterations the design weight could be narrowed down to one 
true value.
Empty Weight Fraction
The recalculated empty weight fraction was used with the intention of recalculating the design weight. 
Which would be the final weight that would be used in the design. This was necessary for not only 
recalculating the design weight but also the fuselage.
Recalculated Empty Weight Fraction
The recalculated design weight was taken in order to calculate the final design weight used in the design 
using the recalculated empty weight fraction. This formula is also used to determine the value of the 
recalculated fuselage length.
Recalculated Design Weight
Fuselage Length
Fuselage Area
Span
AR is an aspect ratio which is the ratio of its length to its chord. A low aspect ratio indicates short, stubby 
wings while a high aspect ratio indicates long narrow wings. This equation is used to determine the 
Aircraft Design Analysis, CFD and manufacturing
International Journal of Advanced Network  Monitoring  and Controls                     Year 2016, No. 02
062
true aspect ratio. The length of the wing span is determined using the aspect ratio and is also vital to the 
geometry and design of the aircraft. It’s determined by taking the square root of the product of the aspect 
ratio and area of the wing. The wing area is also a vital component to the design of the aircraft, calculated 
by simply multiplying the wing thickness by the span.
Wing Area
The horizontal tail is required in any plane for flight. With the fuselage and wings accounted for the design 
of the tail is all that is missing. The horizontal tail is determined by multiplying 0.1 by the thickness ratio.
Lift forces contrast with the drag force and is the component of the force of a fluid flowing past the aircraft 
perpendicular to the oncoming flow direction. The lift and forces were determined at different angles of 
attack from 0 to 15 degrees.
The first priority of testing was to render a three dimensional representation of the proposed design using 
Solidworks. Using the calculated dimensions for the fuselage. Following the fuselage, the wings needed 
to be modeled. The airfoil chosen for the aircraft was the NACA 2415. Using the calculated values for 
span, chord lengths (root, mean, and tip), mean chord span, one wing was modeled and then mirrored in 
Solidworks for consistency throughout the entire wing span, as shown in table 1 and Figure 1.
Table. 1 Aircraft Dimensions
063
Fig. 1 Solid works model of aircraft
3. PHASE #2 wind tunnel laboratory experiment
Upon completing all parts of the aircraft, they were assembled together to produce the final model of the 
proposed aircraft (as shown in Figure 2). This model was scaled down 1:585 in order to print it using the 
uPrinter 3-D printer located in the Manufacturing Center at Wentworth Institute of Technology. The 
material that the model was printed with was ABSplus Plastic
Fig. 2 The 3D printed model
4. PHASE #3 CFD simulation
A CFD study in the Solidworks program paralleled the experimental wind tunnel analysis. The same 
conditions were reproduced within the program. The exact same scaled model was also studied. As to 
Aircraft Design Analysis, CFD and manufacturing
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064
ensure accuracy each study was performed independently and uninfluenced by one another. The model 
created in the Solidworks program was then prepared for simulation. The meshing function of the 
simulation proved to be highly instrumental in attaining accurate results from the CFD. Figure 3 shows the 
contour of the pressure and figure 4 shows the streamlines of air around the aircraft.
Fig. 3 The pressure contour of the airplane
Fig. 4 The streamlines of the air around the airplane
The comparison between the experiments and the CFD simulation has been carried out as shown 
065
in Figures 5 and 6. At low angle of attack the difference between the experiments and CFD is slightly 
significant comparing to high angle of attack for both drag and lift forces. Parasitic drag in the CFD 
simulation was not indicative of what we found in the theoretical calculations. Further refinement of 
this model would likely reduce the percent difference observed, although not by much. In contrast, the 
lift percent error ranged from only 38 to 91% which is quite good considering the many assumptions 
made. Overall, a trend can still be seen in these two sets of data: a positive correlation between drag and 
lift as a function of angle of attack. As angle of attack increases, so do both drag and lift. This makes sense 
because as the plane pitches up more surface area is in contact with the flow, causing more drag. But at 
the same time, the increased angle of attack on the airfoil creates a greater pressure drop because the air 
moves faster over the top of the wing, attributing to more lift. This trend carried over to the comparison 
between the CFD and wind-tunnel tests of the 1:585 scale model. Teams were made aware that the wind 
tunnel would not be a very accurate measurement tool. It was not designed to simulate the conditions 
the project required, nor were the 3D models perfect representations of the CFD models. Despite these 
truths, percent error for both lift and drag fell between 6.56 and 99% for both the 22.5 and 29.7 mph wind 
tunnel trials. Given the circumstances, these results were considered successful, as they  still provide a valid 
representation of angle of attack’s effects on both lift and drag on aircraft.  In retrospect, more could have 
been done to reduce the gross percent errors that were experienced during the design process, primarily in 
testing equipment and procedure, but the concepts applied would remain the same. The three phases of 
design and assumptions that were made based on the A380 produced somewhat reasonable lift and drag 
results for the designed aircraft’s aerodynamics. Table 2 shows the comparison of the results of drag and lift 
forces.
Table. 2  Simulation and wind Tunnel
Aircraft Design Analysis, CFD and manufacturing
066
International Journal of Advanced Network  Monitoring  and Controls                     Year 2016, No. 02
Fig. 5 Comparing Drag-experiment and CFD vs. angle of attack
Fig. 6 Comparing Drag-experiment and CFD vs. angle of attack
5. Conclusion 
The three phases of design were critical in following a set design procedure, and using the assumptions 
to make reasonable estimates for our own model. While these assumptions assisted in moving the design 
along, they greatly contributed to the errors we would see between our different data trials: the theoretical 
manual calculations, CFD simulation, and scale model wind tunnel test. These various paths allowed us to 
better understand different means of data acquisition for airfoils, and helped affirm validity in our design 
process. 
Overall, a trend can still be seen in these two sets of data: a positive correlation between drag and lift as a 
function of angle of attack. As angle of attack increases, so do both drag and lift. This makes sense because 
as the plane pitches up more surface area is in contact with the flow, causing more drag. But at the same 
time, the increased angle of attack on the airfoil creates a greater pressure drop because the air moves faster 
067
over the top of the wing, attributing to more lift. 
It is concluded that this project-based learning has improved and accelerated students understanding 
of aerodynamic concepts and involved students in a constructive exploration
References 
[1] Anderson, Jr., J.D., 2012, Introduction to Flight, McGraw-Hill, seventh edition 
[2]  J.J. Bertin, Aerodynamics for Engineers, 5th ed., Prentice Hall, 2008.  
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[7] Mark Drela, “Assorted views on teaching of aerodynamics”, 16th AIAA Applied Aerodynamics 
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Aircraft Design Analysis, CFD and manufacturing


Aircraft Design Analysis, CFD and manufacturing

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