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

EAGER-DynamicData：动态数据驱动的航空电子系统，用于紧急情况下的飞行决策支持

• 批准号: 1462342
• 负责人: Carlos Varela
• 金额: $ 20万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462342&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Dynamic; Data; Driven

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.

动态数据驱动的航空电子系统（DDDAS）有可能赋予动态使用传感器数据来检测故障条件并准确模拟飞行计划以支持紧急情况下的飞行员决策的能力。 PI Varela将通过将测量数据与预计的误差签名和损害性能概况匹配，研究如何动态检测数据误差和设备故障。 一旦检测到故障类型，将在可能的情况下使用冗余数据来纠正仪器错误，并增加船上自飞行模拟器的保真度。 这项研究将使虚拟失败增强飞行模拟，以预测不同飞行计划的结果。 因此，DDDA将支持在紧急情况下为飞行员提供更明智的决策，它也适用于自动无人空气和太空车辆。 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，包括健康监测和太空服技术。 PI打算以开源形式为社区提供所有开发的编程技术，运行时间中间件和飞行数据。本研究项目将在几个基本研究方向上研究方法并开发新技术，因为它们与拟议的DDDAS模型有关：（1）该项目将增强该项目的数据流程的编程，以使其使其使其成为故障识别和缺陷型通知。特别是，这项工作将使用新的DataFlow变量状态扩展数据流量变量的未结合状态和界限：相关性不确定。 这种增强功能将使软件开发人员能够明确建模分布式冗余数据流，以便能够识别和容忍量化的不确定性。 该项目还将研究这种增强对数据流并发编程模型已经提供的异质性和异步公差的影响。 （2）该项目将调查逻辑编程的扩展，以支持随机推理。 特别是，PI将为基于标准的Horn子句的知识库创建语言扩展，以结合概率。 其他扩展将特别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，该项目将研究用于可扩展数据分析的基于云的技术。 PI将探索混合（私人和公共）云的使用用于在线（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。 政策将包括优化分析性能，飞机到云的通信和/或成本。 ＆＃8232; DDDAS模型将应用于紧急情况下的飞行决策支持系统。 特定活动将包括：i）创建多效率模型和增量算法，这些算法将使DDDA动态地注入来自飞机传感器的数据，ii）对飞机损坏/故障概况的概念进行形式化，以及III）评估新的数学和计算技术，并使用实际的飞行事故数据进行实际的数学和计算技术。