[pSeven] 헬리콥터 정적 및 동적 Loads에 대한 정확한 예측 Use case

pSeven예제
작성자
ablemax
작성일
2018-07-27 10:33
조회
301

Accurate Prediction of Static and Dynamic Loads for Helicopters


Industry: Aerospace | Product: pSeven Core | Company: Airbus Helicopters


Objective


One of the activities of the Airbus Helicopters flight test department is to measure loads for different components (through load sensors, or gauges) and flight configurations (described by the Flight Configuration Parameters, FCP). This data is used to establish the inspections and retirement time for each principal structural element of the helicopter and to list the parts which need to be re-designed to enhance the flight envelope. Before the in-flight measurements (required for certification) are done, analyses are carried out through models based on physics (combining aerodynamic and mechanical laws) to estimate the missing loads, which is a costly procedure.


h225 heliocpter


The objective is to automatically build accurate and robust approximation models from the existing load database for the automatic prediction of the missing helicopter static and dynamic loads as a function of FCP. Predictions will guarantee a drastic reduction in the time and workforce needed for such analysis.


Challenges


  • The loads' database used to build the approximation models with the highest predictive power for the selected type of helicopter is huge:

    • 66 loads (Main Rotor shaft bending, Main Rotor pitch rod load, etc.) with 2 available outputs (maximum signed static and dynamic loads),

    • many flight configurations regrouped into 32 maneuver families: 66 x 2 x 32 = 4224 cases in total, from which 3956 cases are left after the screening and filtering for zero/too small sample size.


  • The number of points for each case is disparate and needs to be taken into account for the model selection to guarantee accuracy and robustness:

    • 1.7% of the cases have 0 points

    • 4.6% of the cases have 1 point

    • 38% of the cases have fewer points than parameters

    • 32% of the cases have a small number of points

    • 23.7% of the cases have an appropriate number of points


  • Selection of the best model for each case should automatically be made.

  • The possibility to add or update new helicopters, load types, maneuvers and other parameters is to be provided.

  • The final user should be warned when the prediction may be unreliable or constant.

Solution


pSeven Core GT Approx module with its wide range of approximation techniques and its automatic selection of the appropriate approximation technique was applied to build 3 types of approximation models (automatically selected depending on the training sample size):


  • Response surface models

  • Gaussian processes

  • High-Dimensional Approximation

Also, constant models were used for cases with very small sample size.


The built-in pSeven Core Internal Validation functionality was applied to select the best model for each case. Thanks to this functionality, the cross-validation fit error check can be done in case the training sample size is limited in order to check the ability of the model to predict outputs in new input points. Cross-validation is a conventional approach to estimate model predictive power: one object (data point) is removed from the sample, the model is built with all the other objects and the fit quality is checked on the removed object. The procedure is repeated for all the objects in the sample, thus giving the average model prediction accuracy on the training sample.


The requirements for the models were to be accurate and robust, and in case it’s not possible to achieve (for example, due to small sample size) constant models were selected, and a report that no trustworthy results are available is generated, avoiding misleading predictions.


Results


For all static and dynamic loads, predictions were compared to Airbus Helicopters measurements for each maneuver family. The comparison is done for both all flight configurations and filtered ones when only the good approximations are considered. Three types of approximation models (depending on training data) are considered: models providing a high-accuracy, models with a low-accuracy due to an inherent scatter in the data and constant models.


Predictions showed a good accuracy when the data on which the model was based was good enough.


helicopter_loads_1


Prediction/measurement comparison (all flight configurations)


When considering all flights configurations, for dynamic loads, respectively 71% and 86% of predictions have a precision better than ±10% and ±20 %. Points marked in red in the above figure are constant approximation models, decreasing the accuracy of prediction.


helicopter_loads_2


Prediction/measurement comparison (filtered flight configurations, ~ 50% of all flight configurations for this load)


But when considering only the filtered flight configurations, and still for dynamic loads, respectively 89% and 98% of the predictions have an accuracy better than ±10% and ±20%.


The customer finds this approach very promising. About 50% of missing loads may be calculated by using approximation models with sufficient accuracy (< ±20 %), drastically reducing the time and workforce needed for such analysis.


 



 Alan Struzik (Airbus Helicopters), Evgeny Burnaev, Pavel Prikhodko (DATADVANCE) 의 "Surrogate Models for Helicopter Loads Problems" publication에 기초하여 작성되었습니다. 상세한 자료가 필요하신 분은 아래의 버튼으로 신청 부탁드립니다.


자료신청하러 가기 >>

전체 529
번호 제목 작성자 작성일 추천 조회
527
[pSeven] DATADVANCE를 NAFEMS World Congress 2019에서 만나보세요 !
ablemax | 2019.06.18 | 추천 0 | 조회 20
ablemax 2019.06.18 0 20
526
[BruceEYE] 인공지능 기반 영상분석 플랫폼 BruceEYE 적용 예시
ablemax | 2019.04.09 | 추천 0 | 조회 143
ablemax 2019.04.09 0 143
525
프로모션사례2 : 피닉스 큐브셋 Thermal Modeling (아리조나 주립대)
ablemax | 2019.03.27 | 추천 0 | 조회 100
ablemax 2019.03.27 0 100
524
프로모션사례1 : 대학 설계 경진대회 포뮬라1 스타일 경주자동차 파워저장장치 해석 (위스콘신대)
ablemax | 2019.03.27 | 추천 0 | 조회 105
ablemax 2019.03.27 0 105
523
[SINDA./FLUINT] Thermal Desktop 튜토리얼 영상 모음 2
ablemax | 2019.02.14 | 추천 0 | 조회 172
ablemax 2019.02.14 0 172
522
[SINDA./FLUINT] Thermal Desktop 튜토리얼 영상 모음 1
ablemax | 2019.02.14 | 추천 0 | 조회 212
ablemax 2019.02.14 0 212
521
[SINDA/FLUINT] Expanded Online Class Offerings Part 1 : Introduction to FloCAD
ablemax | 2019.01.24 | 추천 0 | 조회 248
ablemax 2019.01.24 0 248
520
[SINDA/FLUINT] 2019년 1월 뉴스레터 안내 (Veritrek: Extending Thermal Desktop..)
ablemax | 2019.01.24 | 추천 0 | 조회 235
ablemax 2019.01.24 0 235
519
[pSeven] Learn about UAV multidisciplinary optimization with pSeven innovative technologies
ablemax | 2018.12.17 | 추천 0 | 조회 290
ablemax 2018.12.17 0 290
518
[SINDA/FLUINT] 열해석 전문툴 Thermal Desktop을 이용한 ‘배관 내의 결빙 모델링’ 소개
ablemax | 2018.12.13 | 추천 0 | 조회 260
ablemax 2018.12.13 0 260