11:10
Session 1: Digital engineering I
Chair: Min Liao
11:10
20 mins
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Digital engineering for improved aircraft structural integrity program execution
Chuck Babish
Abstract: The United States Air Force (USAF) has spent considerable effort describing a Digital
Transformation vision and establishing initiatives to implement the key tenets for all programs.
One of the key tenets is developing a Digital Engineering (DE) capability with the purpose of
“achieving a measure of authoritative virtualization that replaces, automates, or truncates
formerly real-world activities” as stated in official correspondence to all USAF members. The
USAF strategy to implement DE in programs was further defined as the seven discrete efforts
described below:
1. develop digital models of systems,
2. develop a digital twin and digital thread,
3. implement an integrated digital environment,
4. employ a tailored DE strategy for contracts with industry,
5. ensure organizational readiness for DE,
6. implement digital acquisition, and
7. track digital maturity metrics.
DE is not a new concept for Aircraft Structural Integrity Program (ASIP) execution defined in
MIL-STD-1530. The ASIP has always embraced, utilized, and relied upon models and data to
conduct and continuously update the structural analyses used as the primary basis for structural
certification throughout the entire aircraft life cycle. However, there is more work to be
accomplished to continue on the journey toward a comprehensive DE environment for proper
ASIP execution and the Digital Transformation initiative is an excellent opportunity to
significantly advance the state-of-the-art.
This paper will define terms such as DE, Digital Thread, and Digital Twin as they relate to ASIP
execution and describe the considerations, key models, data, and tools commonly used by
aircraft programs. The paper will present some use cases and benefit examples for expanding the
use of DE for ASIP execution as well as some challenges that must be overcome. Finally, the
paper will present the path forward to improve DE for ASIP execution including competencies
and personnel required, priorities for aircraft in development, and priorities for aircraft in
sustainment.
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11:30
20 mins
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Determination of composite material finite width correction factors using machine learning strategies
Yuval Freed
Abstract: Aviation products made of composite materials are designed as damage tolerant. That is, it is assumed that the structure contains some sort of imperfection, either induced during manufacture, assembly or service, that may remain undetected throughout the entire lifetime of the aircraft. A common industry practice for determination of strength allowables is usage of open hole specimens (per ASTM D6484) that represent such imperfections.
Strength allowables obtained from such standard specimens strongly depend upon the specimen geometrical dimensions (especially the ratio between the hole diameter and the specimen width). As opposed to metallic structures, for structures made of composite materials the laminate layup also plays a major role affecting the strength allowables. When the design introduces a short edge distance or layup which is significantly different from that of the test specimen (which is usually a quasi-isotropic), finite width correction factors should be applied to the damage tolerance strength allowables to ensure the structural integrity of the composite part.
Machine Learning (ML) algorithms have been used to study different characteristics of composite materials in the past few years. The main advantage of using such approaches over standard numerical analyses is their capability to efficiently perform regression based on relatively small dataset. ML approaches can also deal with large data very efficiently. While there are many ML strategies available, they all similar to each other in their regression process. As a first step, the algorithm is 'trained' with respect to a given dataset. In this process, the algorithm establishes correlations between the different data points with respect to predefined characteristics. It is strongly recommended to use a physical-based model for the training procedure, especially if the training process is based on relatively small dataset. Once these relations are established, the entire investigated domain can be spanned using regression.
Several machine learning algorithms were utilized in this study to obtain finite width correction factors for IM7/8552 unidirectional composite material. The proposed methodology also includes a procedure to determine the number of required training data points using the Gaussian Process Regression (GPR) algorithm. Finite width correction factors carpet plots were produced, allowing the designer to easily obtain the required correction factor for a given layup and geometrical characteristics.
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11:50
20 mins
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Development and demonstration of damage tolerance airframe digital twin methods and tools
Yan Bombardier, Guillaume Renaud, Min Liao
Abstract: The National Research Council of Canada (NRC) has been developing an airframe digital twin (ADT) framework and tools to support structural life-cycle management for the Canadian Department of National Defence (DND). The overall goal of this framework is to improve the accuracy and efficiency of diagnosis and prognosis of the structural integrity of individual aircraft components in order to make better maintenance decisions. It relies on state-of-the-art structural analysis probabilistic modelling techniques, such as high-fidelity finite element modelling, advanced crack growth simulations, and quantitative risk assessment. It also provides the capability to periodically update the probabilistic inputs of these models as new information about the airframe becomes available (inspection results, individual aircraft usage, etc.).
This paper provides an overview of the ADT framework being developed by NRC and presents recent developments made to improve its tools and methods. The developed methods and tools are demonstrated with a case study from the durability and damage tolerance life extension test of a retired CF-188 inboard leading edge flap (ILEF) carried out by NRC. For this case study, NRC monitored the risk at the blended and shot-peened ILEF transmission lugs critical radii using the ADT framework. Sensitivity studies were performed to assess the effects of assumptions about crack detection capabilities and initial damage state on the resulting probability of failure.
This paper also presents a new statistical inference model that is used in the ADT framework to update the damage state of individual airframe components based on non-destructive inspection results. This model provides the ability to develop better physics-based models by segregating different initial damage types, e.g. pores, surface scratches, and/or pits, and updating their contributions as non-destructive inspection results are obtained.
The initial results show that the current ADT framework provides a better probabilistic representation of the future state of aircraft fleets and allows more accurate risk-informed decision-making for individual aircraft component maintenance actions.
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