EAGER-DynamicData: Dynamic Data-Driven Avionics Systems for Flight Decision Support in Emergency Conditions

动态数据驱动航空电子系统（DDDAS）具有赋予飞机动态使用传感器数据来检测故障条件和精确模拟飞行计划的能力，以支持飞行员在紧急情况下的决策的潜力。 PI Varela将研究如何通过将测量数据与预先计算的错误特征和损坏性能配置文件相匹配来动态检测数据错误和设备故障。 一旦检测到故障类型，冗余数据将被用于在可能的情况下纠正仪器错误，并提高机载自飞行模拟器的保真度。 这项研究将使虚拟故障增强飞行模拟，以预测不同的飞行计划执行前的结果。 因此，DDDAS将支持飞行员在紧急情况下做出更明智的决策，它也将适用于自主无人驾驶航空航天器。 此外，用于数据流分析的新数学技术和相关软件可能适用于其他领域，包括健康监测和宇航服技术。 PI计划将所有开发的编程技术、运行时中间件和飞行数据以开放源代码的形式提供给社区。本研究项目将在与拟议的DDDAS模型相关的几个基本研究方向上研究方法并开发新技术：（1）本项目将增强并行并行编程，使其具有故障识别和容错能力。特别是，这项工作将扩展的非绑定和绑定状态的非绑定状态的非绑定变量与一个新的非绑定变量的状态：相关的不确定绑定。 这种增强将允许软件开发人员显式地对分布式冗余数据流进行建模，以便能够识别和容忍具有量化不确定性的故障。 该项目还将研究这一增强对异构性的影响，并研究并行编程模型已经提供的容错性。 (2)这个项目将研究扩展逻辑编程，以支持随机推理。 特别是，PI将创建语言扩展到标准的基于Horn子句的知识库，以纳入概率。 其他扩展将专门支持空间和时间数据流。 此外，PI将创建增量推理算法，以便在应用程序动态接收新数据时能够有效地重新计算查询。 (3)最后，该项目将研究基于云的可扩展数据分析技术。 PI将探索使用混合（私有和公共）云进行在线（实时）数据分析以及离线数据存储和处理。 混合云上数据流、存储和处理技术的弹性和可扩展性将实现多标准优化。 策略将包括优化分析性能、飞机到云通信和/或成本。 &DDDAS模型将应用于紧急情况下的飞行决策支持系统。 具体活动将包括：i）创建多保真度模型和增量算法，使DDDAS能够动态地注入来自飞机传感器的数据，ii）使飞机损坏/故障轮廓的概念形式化，以及iii）用实际飞行事故数据评估新的数学和计算技术。

Dynamic Data Driven Avionics Systems (DDDAS) have the potential to endow aircraft with the ability to dynamically use sensor data to detect failure conditions and accurately simulate flight plans in order to support pilot decisions in emergency scenarios. PI Varela will investigate how to dynamically detect data errors and equipment failures by matching measured data to pre-computed error signatures and damage performance profiles. Once a failure type is detected, redundant data will be used to correct for instrument errors when possible and to increase the fidelity of an onboard self flight simulator. This research will enable virtual failure enhanced flight simulations to predict the outcome of different flight plans before they are executed. DDDAS will thus support better-informed decision making for pilots in emergency conditions and it will also be applicable to autonomous unmanned air and space vehicles. Furthermore, new mathematical techniques and associated software for data streaming analytics will likely be applicable to other domains, including health monitoring and spacesuit technologies. The PI intends to make all developed programming technology, run-time middleware, and flight data available to the community in open-source form.This research project will investigate methodologies and develop new techniques in several fundamental research directions as they pertain to the proposed DDDAS model: (1) This project will enhance dataflow concurrent programming to make it fault-cognizant and fault-tolerant. In particular, this work will extend the unbound and bound states of dataflow variables with a new dataflow variable state: correlated uncertainly bound. This enhancement will allow software developers to explicitly model distributed redundant data streams to be able to recognize and tolerate failures with quantified uncertainty. The project will also study the impact of this enhancement on the heterogeneity and asynchrony tolerance already afforded by the dataflow concurrent programming model. (2) This project will investigate extensions to logic programming to support stochastic reasoning. In particular, the PI will create language extensions to standard Horn clause-based knowledge bases to incorporate probabilities. Additional extensions will specifically support spatial and temporal data streams. Furthermore, the PI will create incremental reasoning algorithms to be able to recompute queries efficiently as applications dynamically receive new data. (3) Finally, this project will investigate cloud-based techniques for scalable data analytics. The PI will explore the use of hybrid (private and public) clouds for online (real-time) data analytics as well as for offline data storage and processing. Elasticity and scalability of data streaming, storage, and processing techniques on hybrid clouds will enable multi-criteria optimization. Policies will include optimizing for analytics performance, aircraft-to-cloud communication, and/or cost. 
The DDDAS model will be applied to flight decision support systems in emergency conditions. Specific activities will include: i) creating multi-fidelity models and incremental algorithms that will allow DDDAS to inject data from aircraft sensors dynamically, ii) formalizing the notion of aircraft damage/failure profiles, and iii) evaluating the new mathematical and computational techniques with actual flight accident data.