Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training and testing AI Air Traffic Control (ATC) agents. There is a developing regulatory landscape for this kind of novel technology. Regulatory requirements are expected to be application specific, and may need to be tailored to each specific use case. We draw on emerging guidance for both Digital Twin development and the use of Artificial Intelligence/Machine Learning (AI/ML) in Air Traffic Management (ATM) to present an assurance framework. This framework defines actionable goals and the evidence required to demonstrate that a Digital Twin accurately represents its physical counterpart and also provides sufficient functionality across target use cases. It provides a structured approach for researchers to assess, understand and document the strengths and limitations of the Digital Twin, whilst also identifying areas where fidelity could be improved. Furthermore, it serves as a foundation for engagement with stakeholders and regulators, supporting discussions around the regulatory needs for future applications, and contributing to the emerging guidance through a concrete, working example of a Digital Twin. The framework leverages a methodology known as Trustworthy and Ethical Assurance (TEA) to develop an assurance case. An assurance case is a nested set of structured arguments that provides justified evidence for how a top-level goal has been realised. In this paper we provide an overview of each structured argument and a number of deep dives which elaborate in more detail upon particular arguments, including the required evidence, assumptions and justifications.

翻译：数字孪生融合了仿真、运行数据与人工智能（AI），具备为航空业带来显著效益的潜力。产业界与学术界合作开展的"蓝鸟项目"开发了一个概率性英国航路空域数字孪生，作为训练与测试AI空中交通管制（ATC）智能体的环境。针对此类新兴技术，监管体系正在逐步形成。监管要求预计将因具体应用而异，并可能需要针对每个特定用例进行定制。我们借鉴数字孪生开发及人工智能/机器学习（AI/ML）在空中交通管理（ATM）中应用的新兴指导原则，提出一个保证框架。该框架界定了可操作的目标及所需证据，以证明数字孪生能够准确表征其物理对应体，并在目标用例中提供充分的功能性。它为研究者提供了一种结构化方法，用以评估、理解并记录数字孪生的优势与局限，同时识别可提升保真度的改进领域。此外，该框架可作为与利益相关方及监管机构沟通的基础，支持围绕未来应用的监管需求展开讨论，并通过一个具体可运行的数字孪生实例为新兴指导原则的完善做出贡献。该框架采用名为"可信与伦理保证"（TEA）的方法论来构建保证案例。保证案例是一组嵌套的结构化论证，为顶层目标的实现方式提供合理证据。本文概述了各结构化论证，并通过若干深度案例分析对特定论证（包括所需证据、假设与合理性依据）进行了详细阐述。