Title:
A Framework for General Aviation Aircraft Performance Model Calibration and Validation
A Framework for General Aviation Aircraft Performance Model Calibration and Validation
| dc.contributor.author | Puranik, Tejas G. | |
| dc.contributor.author | Harrison, Evan D. | |
| dc.contributor.author | Mavris, Dimitri N. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | en_US |
| dc.date.accessioned | 2019-10-22T13:37:58Z | |
| dc.date.available | 2019-10-22T13:37:58Z | |
| dc.date.issued | 2018-06 | |
| dc.description.abstract | A wide range of aircraft performance and safety analyses are greatly facilitated by the development and availability of reliable and accurate aircraft performance models. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraft. However, in reality, this is almost never the case, either owing to underlying simplifications or assumptions or due to the limited fidelity of available or applicable analysis tools. In such cases, model calibration is required in order to fine tune the behavior of available performance models to obtain the desired agreement with the truth model. In the case of point-mass steady-state performance models, challenges arise due to the fact that there is no obvious, unique metric or flight condition at which to assess the accuracy of the model predictions, and since a large number of model parameters may potentially influence model accuracy. This work presents a systematic two- level approach to aircraft performance model calibration that poses the calibration as an optimization problem using the information available. The first level consists of calibrating the performance model using manufacturer-developed performance manuals in a multi objective optimization framework. If data is available from flight testing, these models are further refined using the second level of the calibration framework. The performance models considered in this work consist of aerodynamic and propulsion models (performance curves) that are capable of predicting the non-dimensional lift, drag, thrust, and torque produced by an aircraft at any given point in time. The framework is demonstrated on two popular and representative single-engine naturally-aspirated General Aviation aircraft. The demonstrated approach results in an easily-repeatable process that can be used to calibrate models for a variety of retrospective safety analyses. An example of the safety analyses that can be conducted using such calibrated models is also presented. | en_US |
| dc.identifier.citation | Puranik, T. G., Harrison, E., Min, S., Chakraborty, I., & Mavris, D. N. (2018). A Framework for General Aviation Aircraft Performance Model Calibration and Validation. In AIAA AVIATION Forum. 2018 Aviation Technology, Integration, and Operations Conference. https://doi.org/10.2514/6.2018-3191 | en_US |
| dc.identifier.doi | 10.2514/6.2018-3191 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/61957 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | ASDL; | en_US |
| dc.subject | Model calibration | en_US |
| dc.subject | Genetic algorithm | en_US |
| dc.subject | Aircraft performance | en_US |
| dc.title | A Framework for General Aviation Aircraft Performance Model Calibration and Validation | en_US |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.author | Mavris, Dimitri N. | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAuthorOfPublication | d355c865-c3df-4bfe-8328-24541ea04f62 | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | a8736075-ffb0-4c28-aa40-2160181ead8c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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