Olivia J. Pinon Fischer

Attitude sensors determine the spacecraft attitude through the sensing of an astronomical object, field or other phenomena. The Sun and fixed stars are the two primary astronomical sensing objects. Attitude sensors are critical components for the survival and knowledge improvement of spacecraft. Of these, sun sensors are the most common and important sensor for spacecraft attitude determination. The sun sensor measures the Sun vector in spacecraft coordinates. The sun sensor calibration process is particularly difficult due to the complex nature of the uncertainties involved. The uncertainties are small, difficult to observe, and vary spatio-temporally over the lifecycle of the sensor. In addition, the sensors are affected by numerous sources of uncertainties, including manufacturing, electrical, environmental, and interference sources. This motivates the development of advanced calibration algorithms to minimize uncertainty over the sensor lifecycle and improve accuracy. Although modeling and calibration techniques for sun sensors have been explored extensively in the literature over the past two decades, there is currently no resource that consolidates and systematically reviews this body of work. The present review proposes a systematic mapping of sun sensor modeling and calibration algorithms across a breadth of sensor configurations. It specifically provides a comprehensive survey of each methodology, along with an analysis of research gaps and recommendations for future directions in sun sensor modeling and calibration techniques.

Braking systems are essential to aircraft safety and operational efficiency; however, the variability of carbon brake wear, driven by the intricate interplay of operational and environmental factors, presents challenges for effective maintenance planning. This effort leverages machine learning classifiers to predict wear severity using operational data from an airline’s wide-body fleet equipped with wear pin sensors that measure the percentage of carbon pad remaining on each brake. Aircraft-specific metrics from flight data are augmented with weather and airport parameters from FlightAware^® to better capture the operational environment. Through a systematic benchmarking of multiple classifiers, combined with structured hyperparameter tuning and uncertainty quantification, LGBM and Decision Tree models emerge as top performers, achieving predictive accuracies of up to 98.92%. The inclusion of environmental variables substantially improves model performance, with relative humidity and wind direction identified as key predictors. While machine learning has been extensively applied to predictive maintenance contexts, this work advances the field of brake wear prediction by integrating a comprehensive dataset that incorporates operational, environmental, and airport-specific features. In doing so, it addresses a notable gap in the existing literature regarding the impact of contextual variables on brake wear prediction.

Predictive maintenance in commercial aviation demands highly reliable and robust models, particularly for critical components like carbon brakes. This paper addresses two primary concerns in modeling carbon brake wear for distinct aircraft variants: (1) the choice between developing specialized models for individual aircraft types versus a unified, general model, and (2) the potential of transfer learning (TL) to boost model performance across diverse domains (e.g., aircraft types). We evaluate the trade-offs between predictive performance and computational efficiency by comparing specialized models tailored to specific aircraft types with a generalized model designed to predict continuous wear values across multiple aircraft types. Additionally, we explore the efficacy of TL in leveraging existing domain knowledge to enhance predictions in new, related contexts. Our findings demonstrate that a well-tuned generalized model supported by TL offers a viable approach to reducing model complexity and computational demands while maintaining robust and reliable predictive performance. The implications of this research extend beyond aviation, suggesting broader applications in component predictive maintenance where data-driven insights are crucial for operational efficiency and safety.

The design of complex aerospace systems requires a broad multidisciplinary knowledge base and an iterative approach to accommodate changes effectively. Engineering knowledge is commonly represented through engineering analyses and descriptive models with underlying semantics. While guidelines from systems engineering methodologies exist to guide the development of system models, creating a system model from scratch with every new application/system requires research into more adaptable and reusable modeling frameworks. In this context, this research demonstrates how a physics-based multidisciplinary analysis and optimization tool, SUAVE, can be leveraged to develop a system model. By leveraging the existing physics-based knowledge captured within SUAVE, the process benefits from the expertise embedded in the tool. To facilitate the systematic creation of the system model, an ontological metamodel is created in SysML. This metamodel is designed to capture the inner workings of the SUAVE tool, representing its concepts, relationships, and behaviors. By using this ontological metamodel as a modeling template, the process of creating the system model becomes more structured and organized. Overall, this research aims to streamline the process of building system models from scratch by leveraging existing knowledge and utilizing an ontological metamodel as a modeling template. This approach enhances formal knowledge representation and its consistency, and promotes reusability in multidisciplinary design problems.

The design of complex aerospace systems requires a broad multidisciplinary knowledge base and an iterative approach to accommodate changes effectively. Engineering knowledge is commonly represented through engineering analyses and descriptive models with underlying semantics. While guidelines from systems engineering methodologies exist to guide the development of system models, creating a system model from scratch with every new application/system is impractical. In this context, this research demonstrates how a physics-based multidisciplinary analysis and optimization tool, SUAVE, can be leveraged to develop a system model. By leveraging the existing physics-based knowledge captured within SUAVE, the process benefits from the expertise embedded in the tool. To facilitate the systematic creation of the system model, an ontological metamodel is created in SysML. This metamodel is designed to capture the inner workings of the SUAVE tool, representing its concepts, relationships, and behaviors. By using this ontological metamodel as a modeling template, the process of creating the system model becomes more structured and organized. Overall, this research aims to streamline the process of building system models from scratch by leveraging existing knowledge and utilizing an ontological metamodel as a modeling template. This approach enhances formal knowledge representation and its consistency, and promotes reusability in multidisciplinary optimization problems.