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Abstract To assist in initializing the conceptual design of hypersonic aircraft, we outline a new, systematic framework based on historical aircraft data and primarily composed of design data and regression models. It is a rapid, low-fidelity analysis to provide a starting point for the conceptual design process by (1) assessing the performance capabilities of four types of high-speed aircraft, (2) providing initial estimates for weights and geometry with uncertainty, and (3) exploring how changes in these affect performance within design spaces. Using this framework, an initial set of reasonable aircraft configurations is obtained based on speed, altitude, and payload requirements, which can serve to accelerate the design process and avoid unforeseen problems later in the design cycle. An example is provided to demonstrate the application of the framework to launch the conceptual design of a new hypersonic aircraft with a given set of mission requirements.

Abstract Letter from the Guest Editors

Engineering Events staff at SAE International in Warrendale, Pennsylvania, have extended the call for abstracts through September 21 for the organization’s AeroTech aerospace and defense technology conference, which will take place at the Fort Worth Convention Center in Fort Worth, Texas, March 14-16, 2023. Visit the AeroTech call for abstracts page for more information and to get started.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

Abstract Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network.

SAE International is inviting global participation in its AeroTech® aerospace and defense technology conference and exhibition, which is for the first time co-located with ASM International’s AeroMat, at the Pasadena Convention Center in Pasadena, California, March 15 through 17, 2022.

Abstract Autonomy has the potential to make the most radical impact by significantly reducing the number of soldiers in harm’s way and changing the military paradigm. Benefits of autonomy to improve the Army’s mission capabilities and the rapid evolution of military systems exerts pressure to develop these systems quickly. Since the associated technological development is highly fast paced and stochastic, approaches that develop systems for stochastic future scenarios are required. In this article we present a vision for the autonomous high-mobility military systems for that future. We discuss the ramifications of autonomy in five areas: (1) fleet organization, (2) physical attributes of high-mobility military systems, (3) individual behaviors of autonomous assets, (4) interactions between humans and autonomous systems, and (5) operation and teaming strategies. We present the future vision, implications, requirements, and technological challenges for each of the five areas.

Game-changing opportunities abound for the application of vehicle health management (VHM) across multiple transportation-related sectors, but key unresolved issues continue to impede progress. VHM technology is based upon the broader field of advanced analytics. Much of traditional analytics efforts to date have been largely descriptive in nature and offer somewhat limited value for large-scale enterprises. Analytics technology becomes increasingly valuable when it offers predictive results or, even better, prescriptive results, which can be used to identify specific courses of action. It is this focus on action which takes analytics to a higher level of impact, and which imbues it with the potential to materially impact the success of the enterprise. Artificial intelligence (AI), specifically machine learning technology, shows future promise in the VHM space, but it is not currently adequate by itself for high-accuracy analytics.

After manufacture, every military vehicle experiences a unique history of dynamic loads, depending on loads carried, missions completed, etc. Damage accumulates in vehicle structures and components accordingly, leading eventually to failures that can be difficult to anticipate, and to unpredictable consequences for mission objectives. The advent of simulation-based fatigue life prediction tools opens a path to Digital Twin based solutions for tracking damage, and for gaining control over vehicle reliability. An incremental damage updating feature has now been implemented in the Endurica CL fatigue solver with the aim of supporting such applications for elastomer components. The incremental updating feature is demonstrated via the example of a simple transmission mount component. The damage state of the mount is computed as it progresses towards failure under a series of typical loading histories.

This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs―with increased intelligence, performance, and autonomy. Clear distinctions are made between military problems and comparable commercial deep-learning problems. These considerations need to be understood by ATR engineers working in the defense industry as well as by their government customers. A reference design is provided for a next-generation ATR that can continuously learn from and adapt to its environment. The convergence of diverse forms of data on a single platform supports new capabilities and improved performance. This third edition broadens the notion of ATR to multisensor fusion. Radical continuous-learning ATR architectures, better integration of data sources, well-packaged sensors, and low-power teraflop chips will enable transformative military designs.