With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational improvements that employ existing aircraft and require no new infrastructure are fit for this goal. While quantified data collected throughout aviation, such as arrival/departure statistics and flight data, have been well-utilized, text data collected through safety reports have not been leveraged to their full extent. In this paper, a methodology is presented that can use aviation text data to identify high-level causes of flight delays and cancellations, using delays as a metric of operational inefficiency. The dataset is extracted from the Aviation Safety Reporting System (ASRS), which includes voluntary safety incident reports in text narrative and metadata formats. The methodology uses natural language processing tools, K Means clustering, and dimensionality reduction by t-Distributed Stochastic Neighbor Embedding (t-SNE) to categorize and visualize narratives. The method identified 7 major clusters and a total of 23 sub-clusters. A comparison between the subclusters’ topics and the causes of flight delays revealed by the quantified data shows that the ASRS database provides a unique safety perspective to delay cause identification, as illustrated by the method’s identification of maintenance as the main cause of delays, rather than weather.

This paper explores the design spaces of a thin-haul and a regional aircraft with parallel hybrid-electric propulsion architectures for a 2030 entry into service date. Notional technology reference aircraft models were developed for a 19- and a 50-passenger aircraft based on publicly available data on Beechcraft 1900D and ATR 42-600, respectively. A set of airframe and propulsion system technologies projected to reach maturity by 2030 was infused into the aircraft models. Parametric, physics-based models were created for the charge-depleting hybrid architecture. Different modes of operation were identified and parameterized with a range of design variables to investigate the feasibility and trade space for peak power shaving, climb power boosting, electric taxi, battery usage schedules, and in-flight battery recharge strategies. Thousands of electrified aircraft concepts with varying electrification, operation, and technology scenarios were sized under the same system-level requirements as their conventional counterpart. The resulting multidisciplinary design space exploration environment was used to identify the optimum vision system designs and modes of operation for the minimum block fuel-burn objective. It has been found that both vehicle classes with the charge-depleting parallel hybrid electric architecture provided fuel-burn benefits over their 2030 advanced technology counterparts under certain operational modes.

This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology’s Aerospace Systems Design Laboratory. This system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to identify named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirror the complexity of real-world aviation safety reports. This method significantly improves the model’s generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted Aviation-BERT-NER’s robustness, with a precision of 95.34%, recall of 94.62%, and F1 score of 94.78% when evaluated over 50 manually annotated (BIO tagged) paragraphs. This work addresses a critical gap in English language NER models for aviation safety, promising substantial improvements in the analysis and understanding of aviation safety reports.

To achieve improvements in aircraft efficiency and reduced environmental footprint, aircraft must incorporate novel concepts, architectures, and technologies, such as distributed electric propulsio...

A typical aircraft certification process consists of obtaining a type, production, airworthiness, and continued airworthiness certificate. During this process, a type certification plan is created that includes the intended regulatory operating environment, the proposed certification basis, means of compliance, and a list of documentation to show compliance. This paper extends previous work to demonstrate a model-based framework for the management of these certification artifacts for normal category airplanes. The developed framework integrates the regulatory rules and approved means of compliance in a single model while using best-practices found in Model-Based Systems Engineering (MBSE) literature. This framework, developed using SysML in MagicDraw captures not just the textual requirements and verification artifacts, but also their relationships and any inherent meta-data properties via custom defined stereotype profiles. Additionally, a simulation capability that automates the extraction and export of the applicable rules (certification basis) and corresponding means of compliance for any aircraft under consideration at the click of a button has been developed. The framework also provides numerous additional benefits to different stakeholders that have been described in detail with examples where necessary.