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A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

AIR5317 establishes the foundation for developing a successful APU health management capability for any commercial or military operator, flying fixed wing aircraft or rotorcraft. This AIR provides guidance for demonstrating business value through improved dispatch reliability, fewer service interruptions, and lower maintenance costs and for satisfying Extended Operations (ETOPS) availability and compliance requirements.

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 An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. AS13002 defines the process for qualifying an Alternate Inspection Frequency Plan for suppliers within the aero-engine sector.  This two-day course will provide common requirements for developing and qualifying an alternate inspection plan, other than 100% inspection of all features.  This course is designed to cover the basic elements of the process to be applied to design characteristics (as defined in AS9102), and parts or inspection processes as defined by the purchaser.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004.

The aerospace industry is facing immense challenges due to increased design complexity and higher levels of integration, particularly in the electrification of aircraft. These challenges can easily impact program cost and product time to market. System electrification and electromagnetic compatibility (EMC) have become critical issues today. In the context of 3D electromagnetics, EMC electromagnetic compatibility ensures the original equipment manufacturer (OEM) that radiated emissions from various electronic devices, such as avionics or the entire aircraft for that matter, do not interfere with other electronic products onboard the aircraft.

Thermal Management Techniques in Avionics Cooling Curing the Porosity Problem in Additive Manufacturing Space-Qualified Crystal Oscillators Reimagining Automated Test During a Pandemic EW: New Challenges, Technologies, and Requirements Software Enables New-Age, Flexible Test Solution for Analog and Digital Radios Formal Process Modeling to Improve Human-Decision-Making During Test and Evaluation Range Control Using the Innoslate software tool to formally model the process of conducting test range events can expose previously overlooked ambiguities and identify high-value decision points? Test and Evaluation of Autonomy for Air Platforms Tools, approaches, and insights to confidently approach the safe, secure, effective, and efficient testing of autonomy on air platforms.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

Tanks play a pivotal role in swiftly deploying firepower across dynamic battlefields. The core of tank mobility lies within their powertrains, driven by diesel engines or gas turbines. To better understand the benefits of each power system, this study uses geo-location data from the National Training Center to understand the power and energy requirements from a main battle tank over an 18-day rotation. This paper details the extraction, cleaning, and analysis of the geo-location data to produce a series of representative drive cycles for an NTC rotation. These drive-cycles serve as a basis for evaluating powertrain demands, chiefly focusing on fuel efficiency. Notably, findings reveal that substantial idling periods in tank operations contribute to diesel engines exhibiting notably lower fuel consumption compared to gas turbines. Nonetheless, gas turbines present several merits over diesel engines, notably an enhanced power-to-weight ratio and superior power delivery.

The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for hypoid-type, automotive gear units, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. Appendix A is a mandatory part of this standard. The information contained in Appendix A is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI).

The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for hypoid-type, automotive gear units, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. Appendix A is a mandatory part of this standard. The information contained in Appendix A is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI).

The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for hypoid-type, automotive gear units, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. Appendix A is a mandatory part of this standard. The information contained in Appendix A is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI).

The modeling and simulation pyramid in defense states it clearly: Multi-Level modeling and simulation are required. Models and simulations are often classified by the US Department of Defense into four levels—campaign, mission, engagement, and engineering. Campaign simulation models are applied for evaluation; mission-level simulations to experiment with the integration of several macro agents; engagement simulations in engineered systems development; and engineering-level simulation models with a solid foundation in structural physics and components. Models operating at one level must be able to interact with models at another level. Therefore, the cure (“silver bullet”) is very clear: a comprehensive framework for Multiple Resolution Modeling (MRM) is needed. In this paper, we discuss our research about how to construct MRM environments.

This work covers the historical development of Built-In-Test (BIT) for fiber optic interconnect links for aerospace applications using Optical Time Domain Reflectometry (OTDR) equipped transceivers. The original failure modes found that installed fiber optic links must be disconnected before diagnosis could begin, often resulting in “no fault found” (NFF) designation. In fact, the observed root cause was that most (85%) of the fiber optic link defects were produced by contamination of the connector end faces. In March of 2006, a fiber optics workshop was held with roughly sixty experts from system and component manufacturers to discuss the difficulties of fiber optic test in aerospace platforms. During this meeting it was hypothesized that Optical Time Domain Reflectometry (OTDR) was feasible using an optical transceiver transmit pulse as a stimulus. The time delay and amplitude of received reflections would correlate with the position and severity of link defects, respectively.

Future fighters will require more compact, lighter weight, small gas turbine auxiliary power units (APUs) capable of faster starting, and operation, up to altitudes of 50,000 ft. The US Air Force is currently supporting an Advanced Components Auxiliary Power Unit (ACAPU) research program to demonstrate the technologies that will be required to accomplish projected secondary power requirements for these advanced fighters. The requirements of the ACAPU Program represent a challenging task requiring significant technical advancements over the current state-of-the-art, prominent among which are: Small high heat release high altitude airbreathing combustors. High temperature monolithic ceramic and metallic small turbines. Capability to operate, and transition from non-airbreathing to airbreathing modes. This paper discusses these challenging requirements and establishes technology paths to match and exceed the required goals.

Abstract This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function.