# Design tools

1. Flight Optimization System (FLOPS)
McCullers (Aircraft Configuration Optimization including optimized flight profiles - Recent Experiences in Multidisciplinary analysis and optimization, Apr 1984 ) implemented the Flight Optimization System (FLOPS), which uses a modular approach to allow the user to select the appropriate modules for a given mission.
* Aerodynamics: empirical drag estimation technique with modification (Feagin, R.C. and Morrison, W.D. "Delta Method, an empirical drag buildup technique" Tech. rep., NASA, 1978)
Sommer and short's method for skin friction drag ("Free-flight Measurements of turbulent-boundary-layer skin friction in the presence of severe aerodynamic heating at Mach numbers from 2.8 to 7.0", Tech. rep., NASA, 1955)
user input: drag-polar corrected by scaling factors to address changes in wing area and engine sizing during optimization. 4
* Propulsion: 엔진 제작사 data
* Mission: 본 논문에 자세하게 안 나오고 (An Energy-Based Low-Order Approach for Mission Analysis of Air Vehicles in LEAPS)에서 energy equation 사용하는 걸로 나옴
* Numerical optimization schemes: Davidon-Fletcher-Powerll, Broyden-Fletcher-Goldfrab-Shano, and a Quadratic Extended Interior Panalty method.

conceptual design of new aircraft에 쓰기 위해서 개발 (minimum time to climb, minimum fuel to climb, minimum time to distance, minimum fuel to distance 를 목적함수로 flight profile optimization 함)

2. Transport Aircraft System Optimization (TASOPT)
Drela (Greutzer, E.M., "Design Methodologies for Aerodynamics, Structures, Weight and Thermodynamic Cycles," Tech. rep., Massachusetts Institute of Technology, Aurora Flight Sciences, Pratt & Whitney, March 2010) performs most predictions using low-order models derived from physics-based models rather than historical correlations. Optimized design is realizable and not an artifact of extrapolated data fits.
The standard trajectory equations are integrated over a parameterized mission profile to obtain the wieght of the fuel required, and flight equilibrium equations are enforced at the profile points
* Aerodyanmic model : 2D viscous and inviscid CFD results for a range of $C_L$ and Mach numbers

available at https://web.mit.edu/drela/Public/web/tasopt/
historical data의 의존하지 않는 design optimization framework를 만드는 것을 목표로 한 듯, noise랑 emission도 optimization의 constraints로 사용한 듯. Mission은 2D 간략한 모델을 쓰고 있다.

3. An Objective-Oriencted Framework for Aircraft Design Modelling and Multidisciplinary Optimization (pyACDT)
Martins from the University of Michigan is the leader of a prolific research group (Multidisciplinary Design Optimization Laboratory, MDO Lab) that has released some well known optimization architectures, including the πMDO environment, the framework pyOpt, and the popular optimization software OpenMDAO (Gray, J.S.; Hwang, J.T.; Martins, J.R.R.A.; Moore, K.T.; Naylor, B.A. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization. Struct. Multidiscip. Optim. 2015, 59, 1075–1104.).

Perez and Martins ("pyACDT: An Objective-Oriencted Framework for Aircraft Design Modelling and Multidisciplinary Optimization" 2010) takes an object-oriented approach to modeling and analyzing design concepts. => Brequet range equation are used to determine the amount of fuel consumped over each of the segements.

Henderson et al.(R.P. Henderson, J.R.R.A. Martins, R.E. Perez, "Aircraft conceptual design for optimal environmental performance", Aeronaut. J. 116 (2012) 1–22.) presented an object-oriented aircraft conceptual design toolbox, pyACDT, that analyzes a given mission profile to estimate the mission fuel-burn and point-performance parameters. The Breguet range equation was used to calculate the cruise range. This toolbox uses a potential flow panel method to predict the aerodynamic performance. \

일단 Martins가 만든 πMDO (pyMDO: An Object-Oriented Framework for Multidisciplinary Design Optimization)가 있었음 이게 2007년 나옴. 설계자나 해석하는 사람들이 편하게 쓸 수 있는 MDO platform을 개발한 건데 논문에는 MDO 구조에 대해서만 나오고 mission 이야기가 없음

그걸 Perez가 더 발전시켜 pyACDT가 있었음, 이것도 2010년에 나온거니까 이전에 나온 platform들이 많았는데 사용이 쉽고, 확장가능하고, 재사용가능하며 modeling과 해석에 다 사용할 수 있는 open-sourse에 modular, object-oriented aircraft design framework를 만들기 위해서 새로운 MDO platform을 만들었단다. Mission은 일단 Breguet range를 이용하긴 했는데 다른 performance estimation를 위해서 다른 equation들을 사용하였다.

Henderson 논문은 Perez 논문의 application임

OpenMDAO에는 Hwang이 만든 mission analysis를 사용한 듯
Hwang이 한거는 (Parallel allocation-mission optimization of a 128-route network) 을 reference로 제시했는데

Hwang이 만들거를 KAO가 upgrade한 건가? (Modular adjoint approach to aircraft mission analysis and optimization (2015))
이 논문에는 2차원 assumption임

* Target cl(h, M, alpha, eta) ct(h,M,t) and cm (h, M, alpha, eta)이 존재 해서 이걸 맞춰줌 surrogate model 사용한 듯
* solves the vertical and horizontal equilibrium equations, trim condition, and rfinary differential equation for fuel weight.

Direct and indirect (Betts J. T., "Survey of numerical methods for trajectory optimization" )
The direct approach applies the optimility conditions after discretizing the equilibrium conditions while the indirect approach differentiates the equilibriums first and then discretizes.

Controller 없이 뭔가를 했는데 constraint Aggregation using Kreisselmeier Steinhauser (KS) Function을 이용했단다. 참고문헌은 (G. Kreisselmeier and R. Steinauser. Systematic control design by optimizing a vector performance index. In international Federation of Active Controls Syposium on Computer-Aided Design of Control Systems, Zurich, Switzerland, 1979) minimize the gains in controllers of a closed loop feedback control system, while having the outpue specifications as performance indices (constraints) => sequential unconstrained minimization technique (SUMT) where the pseudo-objective is the KS function that combines the objective function and inequality constrinats into one composite function.
$$KS(f(x),g_{j}(x))=\frac{1}{\rho}\log_{e}\left(\sum_{i}^{n_{f}}e^{\rho f(x)} + \sum_{i}^{n_{g}}e^{\rho g_{j} (x)}\right)$$

그 전에 우리 교수님 mission analysis가 있긴 한데... OpenMDO로 쓰진 않은 듯
Liem ("Aerostructural design optimization of a 100-passenger regional jet with surrogate-based mission analysis" 2013 Aviation Technology, Integration, and Operations Conference) fuel fractions only for the startup, taxi, takeoff, and landing segments of the mission. The fuel burn for the climb, cruise and descent segments are derived from the range euqation and the flight equilibrium equations at each control point (R.P. Liem, G.K.W. Kenway, J.R.R.A. Martins, Multimission aircraft fuel burn minimization via multipoint aerostructural optimization, AIAA J. 53 (1) (2015) 104–122)
Liem, R.P., Mader, C. A., and Martins, J. R., "Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis", Aerospace Science and Technology, Vol. 43, 2015, pp. 126-151

include simplified analytical or empirical models to represent the physics: B. Yan, P.W. Jansen, R.E. Perez, Multidisciplinary design optimization of airframe and trajectory considering cost and emissions, in: 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization, (MAO) Conference, Indianapolis, IN, 2012, AIAA 2012-5494.

Koko ("Aerostructural and Trajctory Optimization of Morphing Wing tip Devices", Master's thesis, Delft university of Technology, Oct. 2011) utilize the midpoint rule such that the values at the midpoint of each discretized flight interval is used to represent all position, speed, time, rate of climb, thrust, and fuel consumption associated with that particular flight interval.

이 분 MDO lab michigan 인데 자료를 찾아볼 수가 없음

4. The Program for Aircraft Synthesis Studies (PASS) - ADW
PASS is a conceptual design tool that evaluates all aspects of the mission performance (PASS, Program for Aircraft Synthesis Studies Software Package, Desktop Aeronautics, Inc., Palo Alto, CA, 2005, http://www.desktop.aero/pass.php.). This software package can incorporate several analyses, including linear aerodynamic models for lift and inviscid drag, sonic boom prediction for supersonic cases, weight and center of gravity estimation, and full mission analysis. These rapid analyses are coupled with optimization tools for aircraft design optimization. The fuel-burn computations mentioned above are done with simplifications of the aircraft performance and the mission profile, which can reduce the accuracy of the predicted fuel burn. For example, the constant product of L/D, the inverse of TSFC, and the flight speed assumed in the Breguet range equation does not reflect actual aircraft operation, since these values vary across the flight operating points in the mission profile. Moreover, most fuel burn computations focus on the cruise portion, which is critical for long-range missions

The PASS program perform tradeoffs in a much more detailed geometry parameter space, but still rely on simple drag and engine
performance models.

A notable example of research effort involving some level of simulation-based performance analysis optimization is given by the work of Kroo from Stanford University
(Kroo, I. An interactive system for aircraft design and optimization. In Proceedings of the Aerospace Design Conference, Irvine, CA, USA, 3–6 February 1992)
(Kroo, I.; Altus, S.; Braun, R.; Gage, P.; Sobieski, I. Multidisciplinary optimization methods for aircraft preliminary design. In Proceedings of the 5th Symposium on Multidisciplinary Analysis and Optimization, Panama City Beach, FL, USA, 7–9 September 1994)
with his Collaborative Application Framework for Engineering (CAFFE)
(Antoine, N.E.; Kroo, I.M. Framework for Aircraft Conceptual Design and Environmental Performance Studies. AIAA J. 2005, 43, 2100–2109)
(Allison, J.; Roth, B.; Kokkolaras, M.; Kroo, I.; Papalambros, P. Aircraft Family Design Using Decomposition-Based Methods. In Proceedings of the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, VA, USA, 6–8 September 2006)

* 엄청 오래된 프로그램이라 지금 자료가 잘 없는 듯 아마도 SUave에 흡수되지 않았을까? 근데 Kroo의 흔적을 찾아보다가 "Multidisciplinary optimization methods for aircraft preliminary design" 그래프에서 Brequet range를 사용했을 것 같은 증거가 나옴

5. Stanford University Aerospace Vehicle Environment (SUave)
(Alonso,J. J., "Stanford University Aerospace Vehicle Environment," Jan 2014.) modular architecture, a pseudo-spectral approach in discretizing control points within each flight segment. The flight equilibrium equations are enforced at these control points and the fuel consumption is obtained by integrating over the segments. A piece of open-source software, written in Python, developed at the University of Stanford, California, USA [Stanford Aerospace Design Lab. SUAVE. 2019. Available online: http://suave.stanford.edu/ (accessed on 20 September 2020).]. It comes with lots of interesting features, among which is the possibility to analyze unconventional configurations (e.g., blended-wing body) with different levels of fidelity, and there is the possibility to take into account different sources of energy (e.g., solar power). However, it has poor visualization features and no dedicated input files, lowering its user-friendliness.

6. PIANO high-fidelity model
The most conprehensive aircraft performance analysis software. However, the simplification of flight trajectories and parameters can underestmate fuel consumption by 7-27% for 50% of the flights
=> discrepancies might be due to "the coarse setting of flight condition", which cannot correctly represent the reality.
A professional tool by Lissys Limited, UK, for the analysis of commercial aircraft available since 1990. It is used in preliminary design, competitor evaluation, performance studies, environmental emissions assessments, and other developmental tasks by airframe and engine manufacturers, aviation research establishments, and governmental or decision-making institutions
throughout the world (Lissys Ltd. Piano. 2014. Available online: http://www.piano.aero/).
PAINO by Lissys Ltd ("PIANO User's Manual," 2010) aircraft conceptual design tool. excat methods are unknown. The trajectory optimization is performed by the Nelder-Mead Sequential Simplex algorithm, which is a gradient-free heuristic method.

다운받아서 해보려고 했는데 777-300ER은 free package에 포함되어 있지 않고 사려면 엄청 비쌈
performance analysis 하는 모듈들 참고할만 한데....
performance analysis랑 design tool이 따로 있음
(문서 페이지: https://www.lissys.uk/pug/c00.html)

7. AAA (Advanced Aircraft Analysis).
DARcorperation developed APP(Aircraft Performance Program) for aircraft performance analysis which is used as a performance module for the company’s aircraft design program, AAA(Advanced Aircraft Analysis)(URL : http://www.darcorp.com/) Commercial software developed by DARCorporation, Lawrence, Kansas, USA, and widely used by industries and Universities. The tool is suitable for conceptual and preliminary design phases of both conventional and unconventional fixed wings aircraft configurations. The software allows for multi-fidelity analyses, combining classical and fast semi-empirical methodologies with physics-based methods. In addition, a graphic user interface provides for the required user-friendliness

이거 roskam 기본으로 만든 거
All calculations are based on 2 DOF point-mass equations 라고 적혀있음 (https://www.darcorp.com/wp-content/uploads/2018/10/APP6016_Presentation.pdf)

8. RDSwin
RDS-professional and RDS-student were developed and maintained by Conceptual Research Corporation[URL : http://www.aircraftdesign.com/, accessed[March, 10, 2013].]. They also have a common mission performance analysis module inside.
RDSwin. Developed by the design and consulting company founded by Daniel P. Raymer, Conceptual Research Corporation, Playa del Rey, CA, USA, this commercial software was conceived to support industries, governments, and universities during preliminary aircraft design activities. It performs MDAO, as well as trade studies, and comes with a graphic user interface to enhance user-friendliness. The tool is suitable both for commercial transport aircraft and military fighters, giving to users the possibility to experiment also with unconventional configurations [Raymer, D. RDS-Win Aircraft Design Software. 2019. Available online: http://www.aircraftdesign.com/rds.shtml (accessed on 20 September 2020).].

Raymer가 만든 거. 사이트에 뭐가 없고 아마존에서 책사고 프로그램 사라는 얘기임
Raymer 책 그대로 한거면 2 linear momentum equation만 고려

9. Adaptable Design Synthesis Tool (ADST)
The in-house aircraft synthesis tool developed by Lockheed Martin (Mavris, D. N., Soban, D. S., and Largent, M. C., “An Application of a Technology Impact Forecasting(TIF) Method to an Uninhabited Combat Aerial Vehicle”, 1999 AIAA/SAE World Aviation Congress, San Francisco, CA, Oct. 19-21, 1999, SAE/AIAA 1999-01-5633.). In this code, the aircraft design is perturbed by changing the wing geometric parameters from the baseline, and the aircraft is then fuel sized to meet the design mission. ADST equips with a performance module called MAPS (Mission Analysis and Performance System).

자료가 없음

10. Pacelab Suite
A commercial software suite, written in C#, developed by the German company Pace, part of the Italian group TXT E-solutions, Milan Italy (TXT Group. Pacelab APD. 2019. Available online: https://www.txtgroup.com/markets/solutions/pacelab-apd). This software has rapidly become a leader on the aircraft preliminary design market due to its user-friendliness and its robust and efficient software architecture. The suite is made up of several interconnected modules, each of which adds very important features to the base version (e.g., on-board systems architecture or detailed cabin layout definition). However, some methodologies and databases lack the required scientific know-how that only research centers or universities can provide.

상업용이라 research 관련 자료가 없다.

11. Aircraft Design and Analysis Sorftware (ADAS)
Software for the conceptual/preliminary design of transport aircraft (transport jet, regional turboprops, business jet) and light aircraft developed at the University of Naples Federico II, Naples, Italy, by Fabrizio Nicolosi and Giuseppe Paduano. The software, in development since 2005, is completely written in Visual Basic and comes with a dedicated graphic user interface to enhance user-friendliness. Its architecture provides for independent design modules; however, it was not conceived for MDAO applications.

논문에 equation이 하나도 없음(Nicolosi, F.; Paduano, G. Development of a software for aircraft preliminary design and analysis. In Proceedings of the 3rd CEAS Congress, Venice, Italy, 24–26 October 2011; Council of European Aerospace Societies: Brussels, Belgium, 2011.)
Stanford 자료 참고 많이 하고 flight performance는 Roskam 책이랑 (McCormick B.W., Aerodynamics, Aeronautics and Flight Mechanics, 1st Ed., John Wiley & Sons Inc., New York New York, 1979.) 랑 (Hartman E.P., Bierman D., "Report No 640, THe Aerodynamic Characteristics of Full Scale Propeller Having 2, 3 and 4 blades of Clark Y and A.F. 6 Airfoil SEctions" NACA, 1938.)
아래에 CEASIOM랑 합쳐졌다는데 SDSA랑의 구조를 잘 모르겠음

12. CEASIOM: A conceptual aircraft design tool
A conceptual aircraft design framework by CFS Engineering, Lausanne, Switzerland, written in Python, developed within the frame of the SimSAC (Simulating Aircraft Stability And Control Characteristics for Use in Conceptual Design) Specific Targeted Research Project (STREP) approved for funding by the European Commission 6th Framework Programme on Research, Technological Development and Demonstration. CEASIOM is meant to support engineers in the conceptual design process of the aircraft, with emphasis on the improved prediction of stability and control properties achieved by higher-fidelity methods than found in contemporary aircraft design tools. Moreover, CEASIOM integrates into one application the main design disciplines: aerodynamics, structures, and flight dynamics, impacting on the aircraft performance. However, the framework does not carry out the entire conceptual design process; thus, it requires as input an initial layout. as the baseline configuration that it then refines and outputs as the revised layout (CFS Engineering. CEASIOM. 2019. Available online: https://www.ceasiom.com/wp/ ). For this reason, the framework has been used in combination with the above-mentioned ADAS software (Zhang, M.; Rizzi, A.; Nicolosi, F.; De Marco, A. Collaborative Aircraft Design Methodology using ADAS
Linked to CEASIOM. In Proceedings of the 32nd AIAA Applied Aerodynamics Conference, Atlanta, GA, USA, 16–20 June 2014; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2014.).

완전 powerful하고 완성도 높아보임 제어기 설계도 할 수 있게 되어 있는 것 같은데 그래서 6DOF 씀 (sub module로 SDSA:Simulation and Dynamic Stability of an Aircraft 쓰는데 https://www.meil.pw.edu.pl/add/ADD/Teaching/Software 여기 참고)
이 논문도 참고함(Elements of computational flight dynamics for complete aircraft)

13. Aircraft Design Software (ADS)
A piece of commercial software developed by OAD (Optimal Aircraft Design), Belgium, after six years of development which has become a standard for the conceptual design of the modern generation of light aircraft. The tool is suitable for several kind of customers, among which are aircraft designers, amateur builders, universities, and research institutes (Optimal Aircraft Design (OAD). ADS-Aircraft Design Software. 2016. Available online: http://www.
pca2000.com/en/index.php)

14. Decision Environment for Complex Designs (DECODE)
Other interesting works in the MDO field are the Decision Environment for Complex Designs (DECODE) project at the University of Southampton (Gorissen, D.; Quaranta, E.; Ferraro, M.; Schumann, B.; van Schaik, J.; Gisbert, M.B.I.; Keane, A.; Scanlan, J.
Value-Based Decision Environment: Vision and Application. J. Aircr. 2014, 51, 1360–1372), based on the value-driven design concept.

University of Southampton에서 개발한 건데 참고문헌에 식인 안나옴 operation 고려하는게 나오는데 이건 좀 더 aircraft 사용에 따른 성능 감퇴를 말하는 것 같고 뭐가 자세히 안나옴
이 논문도 참고함(Architecting a Decision Environment for Complex Design Evaluation)

15. A standards-based system for parametric computer aided conceptual design of aircraft (ACSYNT)
The ACSYNT program (S. Jayaram, A. Myklebust, and P. Gelhausen. ACSYNT — A standards-based system for parametric computer aided conceptual design of aircraft. AIAA Paper 92-1268, Feb 1992.),(W.H. Mason and T.K. Arledge. ACSYNT aerodynamic estimation — An examination and validation for use in conceptual design. AIAA Paper 93-0973, Feb 1993.) likewise relies on simple models, with a more detailed treatment of the geometry via its PDCYL (M.D. Ardema, M.C. Chambers, A.P. Patron, A.S. Hahn, M. Hirokazu, and M.D. Moore. Analytical fuselage and wing weight estimation of transport aircraft. NASA TM 110392, May 1996.) extension.

이거 너무 오래됨

16.
The Chinese research on virtual simulation architectures inspired by the “design for operations” idea (Liu, H.; Tian, Y.; Zhang, C.; Yin, J.; Sun, Y. Evaluation Model of Design for Operation and Architecture of Hierarchical Virtual Simulation for Flight Vehicle Design. Chin. J. Aeronaut. 2012, 25, 216–226).

17. 여기에는 mission modul 있는지 없는지도 모르겠음
optimization-based approaches such as those of Knapp (B. Knapp, Matt. Applications of a nonlinear wing planform design program. Master’s thesis, MIT, Aug 1996),

이것도 엄청 오래된 MIT 석사 논문. 목차만 봤는데 미션은 아예 없는 듯

18.
the WINGOP code of Wakayama (S. Wakayama. Lifting Surface Design Using Multidisciplinary Optimization. PhD thesis, Stanford, June 1994.),(S. Wakayama. Blended-wing-body optimization setup. AIAA Paper 00-4740, Sept 2000)

이거 엄청 오래된 Stanford 박사학위 논문. 자료가 없다.

## Fuel estimation

1. Data based model (largely depend on the availability of data) design space have been obseved during the design process these models cannot identify the souces and magnitude of the operation deviations. Even when some of them can be identified, the methods are not transparent enough to be implemented into fuel policy(36)

a. Linear regression
* O’Kelly, M.E., 2012. Fuel burn and environmental implications of airline hub networks. Transp. Res. Part D: Transp. Environ. 17, 555–567.
* J. Yanto, R.P. Liem, Aircraft fuel burn performance study: a data-enhanced modeling approach, Transp. Res . Part D 65 (2018) 574-595
* H. Khadilar, H. Balakrishnan, Estimation of aircraft taxi fuel burn using flight data recorder archives, Tranp. Res. Part D 17 (7) (2012) 523-537
* J. Yanto, R.P. Liem, Efficient fast approximation for aircraft fuel consumption for decision-making and policy analysis in: AIAA Modeling and simulation Technology Conference, AIAA Aviation Forum, Denver, Colorado, 2017

b. Nonlinear regression
* M.S. Ryerson, M. Hansen, L. Hao, M. Seelhorst, Landing on empty: estimating the benefits from reducing fuel uplift in US Civil Aviation, Environ. Res. Lett. 10 (9) (2015)

c. Decision tree
* Y.S. Chati, H. Balakrishnan, Statistical modeling of aircraft engine fuel flow rate 30th Congress of the International Council of the Aeronautical Science, 2016

d. Random forest boosting tree, neural network
* A. Trani, F.Wing-Ho, G.Schilling, H. Baik, A. Seshadri, A neural network model to estimate aircraft fuel consumption, in AIAA 4th Aviation Technology, Integration and Operatiob (ATIO) Forum, Chicago, IL, 2004
* Y.Horiguchi, Y. Baba, H. Kashima, M. Suzuki, H. Kayahara, J. Maneno, Predicting fuel consumption and flight delays for low-cost airlines in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, in: AAAI'17, AAAI Press, 2017, pp. 4686-4693.

e. The ensemble of machin learning method
* L. Kang, M. Hansen, Improving airline fuel efficiency via fuel burn predictionb and uncertainty estimation, Transp. Res.Part C 97 (2018) 128-146

2. ICAO Carbon Emission Calculator2
The International Civil Aviation Organisation (ICAO) developed the ICAO Carbon Emission Calculator2 to estimate the fuel burn and emissions with minimum input variables (ICAO, 2015. ICAO Carbon Emissions Calculator Methodology Version 8. < https://www.icao.int/environmental-protection/CarbonOffset/Documents/Methodology%20ICAO%20Carbon%20Calculator_v8-2015.pdf > (last access: 5 September 2018)). The calculation relies on a distance-based approach that is derived based on publicly available data. http://applications.icao.int/icec.

이거는 data driven model 임

3. Breguet range equation ( J.G. Coffin, A study of airplane range and useful loads, NACA-TR-69, NACA,
1920.) (Breguet, Calcul du Poids de combustible Consumme par un Avion en Vol Ascendant, Comptes Rendus Hebdomadaires des Seances de l'Academie des Sciences 177(1923) 870-872) 8,9,10(different level of fidelity) FAA and Eurocontrol categorization (S. Mondoloni Aircraft trajectory prediction errors: including a summary of error sources and data, FAA/Eurocontrol Action Plan 16(2006)) - six dof (C.M. Shearer, Coupled Nonlinear flight Dynamics, Seroelasticity, and control of very flexible aircraft, University of Michigan, 2006 Ph.D. thesis) point mass model (R. Dalmau, X Prats, Fuel and time savings by flying continuous cruise climbs: estimating the benefit pools for maximum range operations, Transp.Res Prt D 35(2015) 62-71)(A. Nuic, User manual for the Base od Aircraft Data revision 3.10, Atomoshere 2010) (A. Suchkov, S. Swierstra, A Nuic, Aircraft performance modeling for air traffic management applications, in: 5th USA/Europe Air traffic Management Research and Development SEminar 2003 pp-23-27) macroscopic behavior modeling approach and the tabular data appoach
(D.P.Raymer Aircraft design: a conceptual approach, AIAA 1999)(Druot MARILib paper)

4. Breguet range + fuel fraction factors (J.J.Lee, S.P.Lukachko, I.A.Waitz, A.Schafer, Historical and future trends in aircraft performance, cost and emissions, Annu. Rev. Engergy Environ. 26(1) 2001 167-200)(J. Li, J. Cai, Massively multipoint aerodynamic shape design via surrogate assisted gradient-based optimization, AIAA J. 58(5) (2020) 1949-1963)

* Roskam (J. Roskam, Airplane design part I: preliminary sizing of airplanes, design analysis and research corporation, Lawrence, Kansas 1997) for suggested fuel-fraction values corresponding to several mission phases for various aircraft types. Lee and Chatterji [43] presented approximation functions for the total fuel burn in the climb, cruise, and descent phases. To compute the fuel burn during climb, they applied a climb fuel-increment factor, which was defined as the additional fuel required to climb compared to the amount required to cruise the same distance, normalized with respect to the takeoff weight ( I.M. Kroo, Aircraft Design: Synthesis and Analysis, 1st edition, Desktop Aeronautics, Palo Alto, CA, Sept. 2006.).

* Fuel fraction : (Lee H.T and Chatterji, G., "Closed-form takeoff weight estimation model for air transportation simulation" 10th AIAA Aviation Technology, Integration and Operation (ATIO) Conference, 2010, p.9156)

* cruise (McCormick, Aerodynamics Aeronautics and FLight Machanics, Wiley, 1979)
fuel fraction factor(the ratio of final to initial wights within each segment is typically used for other flight segments)

* Current flight data statistics show that most aircraft operate outside their determined design spaces. (Randle's)

## Flight Data

1. Flightradar 24
2. Flight aware
3. Flight information system (PFIS)
5. ADSB
6. Quick Access Recorder (QAR)
The recording unit, which receives data from the flight data acquisition unit(s), is either a crash-protected device or a quick access recorder (QAR). The QAR is a device that allows convenient access to the recording medium and typically records more data than crashprotected devices.

Most of the aircraft flight parameters are stored by the Flight Data Recorder (FDR), commonly called the black box (although its colour is often orange). The set of data recorded is now standardised and contains all of the parameters that may be useful for accident investigation, systems monitoring, flight trajectory analysis and engine performance. As of August 2002, the National Transportation Safety Board (NTSB, United States) requires the records of at least 88 flight parameters for a transport category airplane. 3 These data include values for all the degrees of freedom of the aircraft, velocities, acceleration rates, controls positions and engine state (temperatures, pressures, fuel flow, rotation speeds), and quantities as different as computer failure, autopilot engagement, icing, traffic alerts and collision-avoidance systems.

## ATM
1. BlueSKy
The aim of BlueSky is to develop a fully portable, freely downloadable ATM simulator. (다른 상업용 ATM들 AirTop, SIMMOD, NARSIM)
an open-sourse air traffic simulator, has become popular in the air traffic management research community.
(Hoekstra, J.M.; Ellerbroek, J. BlueSky ATC Simulator Project: An Open Data and Open Source Approach.
In Proceedings of the 7th International Conference on Research in Air Transportation, Philadelphia, PA, USA,
20–24 June 2016; pp. 1–8.)

Delft 공대 joost 랩에서 개발한 flight simulator임. Aircraft design tool 아님.
그리고 air traffic이어서 약간 network modeling 같음. 여러대 한꺼번에 계산할 걸~ 글고 BADA 씀. motion equation 아마도 간단한 거 쓸건데... 자료가 잘 안 나와 있음 (repository: https://github.com/TUDelft-CNS-ATM/bluesky)
(Website: http://homepage.tudelft.nl/7p97s/BlueSky/download.html)

2. Aviation Environment Design Tool (AEDT)
AEDT considers flight schedules, trajctories, aircraft performance models, and emission factors to assess fuel burn, emissions, and noise (AEDT, 2017. Aviation Environmental Design Tool (AEDT) Technical Manual, Version 2d. U.S. Department of Transportation, Federal Aviation Administration.)
The AEDT fuel burn and emissions modules were previously known as the System for assessing Aviation’s Global Emissions (SAGE) (Kim, B.Y., Fleming, G.G., Lee, J.J., Waitz, I.A., Clarke, J.P., Balasubramanian, S., Malwitz, A., Klima, K., Locke, M., Holsclaw, C.A., Maurice, L.Q., Gupta, M.L., 2007. System for assessing Aviation’s Global Emissions (SAGE), Part 1: Model description and invertory results. Transportation Research Part D 12, pp. 325–346. https://doi.org/10.1016/j.trd.2007.03.007.).

AEDT integrates existing noise and emissions models and helps assess interdependencies.
이거는 BADA를 끼고 있는 것 같음. air traffic model이라기 보다 연료모델, noise, emission 계산해주는데... 뭐로 분류해야될지 모르겠음. 여튼 항공시 설계 툴은 아님 (여기 참고:https://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/)

## Performance model
1. Base of Aircraft Data (BADA) https://badaext.eurocontrol.fr/.
Different aircraft types and flight routes can be simulated However, nominal flight trajectory and flight conditions are assumed for each route causing an aggregate approximation error of around 5%.

kinetic model이라고 언급되어 있음 (BADA: An advanced aircraft performance model for present and future ATM systems)
근데 energy equation 쓰는 듯

2. General Aircraft Modelling Environment (GAME)
One developed by Eurocontrol, is a kinematic-only performance model that can be used for air transport research. Compared to
BADA, it is less frequently used by air transport researchers. (Calders, P. G.A.M.E. Aircraft Performance Model Description; DIS/ATD Unit, DOC.CoE-TP-02002; Eurocontrol: Brussels, Belgium, 2002.)
The GAME method provides a direct model of the path characteristics of the aircraft without attempting to model the underlying physics. (이건 data model이라는 거임. 근데 fuel consumption 모델은 아니고 performance estimation에 쓰임)
참고문헌을 찾을 수가 없음 그나마 참고할만한 것이 "AIRCRAFT PERFORMANCE MODELING FOR AIR TRAFFIC MANAGEMENT APPLICATIONS" 임)

3. Compromised Aircraft Performance Model with Limited Accuracy (COALA)
Previous research in (Rosenow, J.; Fricke, H. Flight Performance Modeling to Optimize Trajectories; Deutsche Gesellschaft für Luft-und Raumfahrt-Lilienthal-Oberth eV: Bonn, Germany, 2016.) has produced a simplified aircraft performance model, which uses part of the BADA model data.

2개 liear momentum equations 이용함.
이거는 또 TOMATO (Toolchain for multicriteria Aircraft Trajectory Optimization) 랑 엮여 있음

4. FLIGHT
In development since 2006 at the University of Manchester, UK, by Dr. Antonio Filippone, FLIGHT is state-of-the-art software for the prediction and modeling of fixed wing aircraft performance. Through analyzing the performance of airborne vehicles and any sub-systems, FLIGHT can accurately map aircraft operation under all flight conditions, allowing for numerous
logistical variations. A unique benefit of the software is the ability to calculate the impacts of noise and LTO emissions, both within and around an airport (Filippone, A. Advanced Aircraft Flight Performance; Cambridge University Press: Cambridge, UK, 2012.,University of Manchester. FLIGHT. 2019. Available online: http://www.flight.mace.manchester.ac.uk/index.html).

5. a Segmented Mission Analysis Program for Low and High Speed Aircraft (NSEG)
Program NSEG is a rapid mission analysis code based on the use of approximate flight path equations of motion[ Hague, D. S., and Rozendaal, H. L., “NSEG–a Segmented Mission Analysis Program for Low and High Speed Aircraft, Vol. I Theoretical Development”, NASA CR-2807, 1977.]. It was originally developed by U.S. Air Force Aeronautical System Division of Wright-Patterson Air-Force Base. Equations vary with the segment types; accelerations, climbs, cruises, descents,and deceleration.
point mass approximation이라고 나옴 2D인 것 같은데....

6. Performance Engineering Programs (PEP)
Airbus has developed the Performance Engineering Programs (Airbus. Getting to grips with Aircraft Performance. Flight Oper. Support Line Assist. 2002, 2, 11–16.) that can be used as a standalone software tool.