Airworthiness certification is to ensure the safety of aircraft. With the surge in novel general aviation aircraft configurations and technologies, the Federal Aviation Administration replaced prescriptive design requirements with performance-based airworthiness standards in Federal Aviation Regulations Part 23 that governs the airworthiness of normal category airplane. The amendment ported over the accepted means of compliance (MoC) from prescriptive advisory circulars to a number of consensus standards from aviation community. Because these MoCs are scattered in multiple documents and cross-reference one another, the certification practice with this new format may be cumbersome and time-consuming.This paper proposes a Model-Based System Engineering (MBSE) approach that is envisioned to parametrically transform the document-centric exercise to a model-based process. The approach helps collect the FAR-23 regulations and the associated MoC in an integrated system model along with the relevant mappings between them. This allows users to automatically generate a compliance checklist for any specific certification requirement. Other benefits of the MBSE approach include circular referencing check, automatically propagating any future changes to the FARs or MoC standards through the model, and potential incorporation with early aircraft design.
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.
Novel architectures and technologies carry with them an uncertainty related to their reliability and associated safety risk. Existing safety assessment methods involve determining the severity of discrete functional failure and the corresponding probability. However, with the advent of novel aircraft architectural and operational concepts, traditional methods of establishing severity and probabilities failures are found lacking due to the scarcity of available data. The current work proposes a safety assessment method that uses architecture-specific performance models along with continuous functional hazard assessments to inform hazard severity. The probability of failures is determined using a Bayesian framework that does not falter when data is scarce. Taken together, it is expected that this new proposed methodology will enable a more accurate safety assessment of novel aircraft architectures and technologies. A safety assessment of an electric propulsion system powered by a fuel cell is conducted using the proposed methodology to serve as a proof of concept. Nomenclature C-FHA = Continuous Functional Hazard Assessment λ = Failure rate (per flight hour) ȳ = Available failure data a = Compliance action (decision) X = True value of compliance finding (unknown) δ = Decision rule L(X, a) = Loss function
General Aviation (GA) is likely to be at the forefront of a paradigm change in aviation, where the introduction of novel concepts such as Urban Air Mobility (UAM), architectures like −VTOL, and technologies like Distributed Electric Propulsion (DEP) are expected to make aircraft more efficient and reduce their environmental footprint. However, these architectures carry with them an uncertainty related to the off-nominal operational risk they pose. The limitations and off-nominal operational considerations generally postulated during traditional safety analysis may not be complete or correct for new technologies. While a lot of the literature surveyed focuses on improving traditional methods of safety analysis, it still does not completely address the limitations caused due to insufficient knowledge and experience with transformative technologies. The research objective of the present work is to integrate the Bayesian safety assessment framework developed previously by the authors with conceptual and 6-DoF performance models for DEP aircraft to evaluate off-nominal performance and reliability using information that is typically available in conceptual or preliminary design phases. A case study on the electric power architecture of the the NASA Maxwell X-57 Mod. IV is provided. A maximum potential flight path angle metric, as well as trimmability considerations using a 6-DoF model constructed using available literature help determine hazard severity of power degradation scenarios. Bayesian failure rate posteriors are constructed for the different components in the traction power system, which are used in a Bayesian decision framework. The results indicate that while most of the components in the traction power architecture of the X-57 Mod. IV are compliant with failure rate requirements generated, the batteries, cruise motors, and cruise motor-inverters do not meet those requirements.
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