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The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2018 Standard. The class offers an explanation of geometric tolerances, including their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, the datum system, form and orientation controls, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 130 practice exercises, including more than 60 application problems. 

The 2-day foundational-level Fundamentals of GD&T course teaches the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2009 Standard. The class offers an explanation of geometric tolerances, their symbols, tolerance zones, applicable modifiers, common applications, and limitations. It explains Rules #1 and #2, form and orientation controls, the datum system, tolerance of position (RFS and MMC), runout, and profile controls. Newly acquired learning is reinforced throughout the class with more than 80 practice exercises. 

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). The course directly addresses improved aircraft system design tools and processes, which, when implemented will significantly contribute to simpler, lower cost and even safer airplanes that meet customer quality demands. Adding value to the high-leveraged area of design (reducing design by involving manufacturing) — which is what this course does — can start to reverse the current trend (overrun and long delay) and help aircraft companies be more profitable. 

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

This 3-day Fundamentals of GD&T course provides an in-depth study of the terms, rules, symbols, and concepts of geometric dimensioning and tolerancing, as prescribed in the ASME Y14.5-2018 Standard. The course can be conducted in three 8-hour sessions or with flexible scheduling including five mornings or five afternoons. 

A gerotor pump is a positive displacement pump consisting of inner and outer rotors, with axis of inner rotor offset from axis of outer rotor. Both rotors rotate about their respective axes. The volume between the rotors changes dynamically, due to which suction and compression occurs. A gerotor pump may be subject to erosion due to cavitation. This paper details about the CFD methodology that has been used to capture cavitation bubbles which might form during the operation of gerotor pump. A full scale (3D) transient CFD model for gerotor pump has been developed using commercial CFD code ANSYS FLUENT. The most challenging part of this CFD flow modeling is to create a dynamic volume mesh that perfectly represents the dynamically changing rotor fluid volume of the gerotor pump. Two different approaches have been used to model this dynamic mesh analysis in the Ansys Fluent tool - one method by using the traditional UDF script and, another method by using Python automation script.

The aim of this paper is to present a numerical analysis of high-speed flows over a missile geometry. The N1G missile has been selected for our study, which is subjected to a high-speed flow at Mach 4 over a range of Angle of attack (AoA) from 0° to 6°. The analysis has been conducted for a 3-dimensional missile model using ANSYS environment. The study contemplates to provide new insights into the missile aerodynamic performance which includes the coefficient of lift (CL) , coefficient of drag (CD) and coefficient of moment (CM) using computational fluid dynamics (CFD). As there is a lack of availability of data for missile geometry, such as free stream conditions and/or the experimental data for a given Mach number, this paper intends to provide a detailed analysis at Mach 4. As the technology is advancing, there is a need for high-speed weapons (missiles) with a good aerodynamic performance, which intern will benefit in reduction of fuel consumption.

Airframe section of rockets, missiles and launch vehicles are typically cylindrical in shape. The cylindrical shell is subjected to high axial load and an external pressure during its operation. The design of cylinders subjected to such loads is generally found to be critical in buckling. To minimize the weight of cylinders, it is typically stiffened with rings and stringers on the inner diameter to increase the buckling load factor. Conventionally the buckling load estimated by analytical or numerical means is multiplied by an empirical factor generally called Knockdown factor (kdf) to get the critical buckling load. This factor is considered to account for the variation between theory and experiment and is specified by handbooks or codes. In aerospace industry, NASA SP 8007 is commonly followed and it specifies the kdf as a lower bound fit curve for experimental data .

Gaganyaan programme is India's prestigious human space exploration endeavour. During the re-entry of the spacecraft, achieving the minimum terminal velocity is paramount to ensure the crew's safety upon landing. Therefore, acquiring accurate in-flight velocity data is essential for comprehensively understanding the landing dynamics and facilitating post-flight data analysis and validation. Moreover, terminal velocity plays a pivotal role in the qualification of parachute systems during platform-drop tests where the objective is to minimize the terminal velocity for safe impact. Terminal velocity also serves as a critical design parameter for the crew seat attenuation system. In addition to terminal velocity, it is equally necessary to characterize the horizontal velocities acting on the decelerating body due to various factors such as parachute sway and wind drift. This data also plays a central role in refining our systems for future enhancements.

The purpose of the Air Generation System is to provide a constant supply of conditioned fresh air to meet the necessary oxygen availability and to prevent carbon dioxide (CO2) concentrations for the occupants in an aircraft. The engine bleed energy or electrical load energy consumed towards this circumstance accounts to be approx. 5% of total fuel burn and in turn, contributes to the global emissions of greenhouse gases. This paper studies the improvement areas of the present conventional system such as fuel burn consumption associated with an aircraft environmental control system (ECS) depending on, the amount of bleed and ram air usage, electric power consumption. Improved systems for propulsion, power generation, sustainability, hybridization, and environmental control can be desirable for an aircraft.

Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviours and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining the outputs of these during the model integration phase, an overall health assessment of the aircraft will be generated.

It is important to accurately predict the aerodynamic properties for designing applications which involves fluid flows, particularly in the aerospace industry. Traditionally, this is done through complex numerical simulations, which are computationally expensive, resource-intensive and time-consuming, making them less than ideal for iterative design processes and rapid prototyping. Machine learning, powered by vast datasets and advanced algorithms, offers an innovative approach to predict airfoil characteristics with remarkable accuracy, speed, and cost-effectiveness. Machine learning techniques have been applied to fluid dynamics and have shown promising results. In this study, machine learning model called the back-propagation neural network (BPNN) is used to predict key aerodynamic coefficients of lift and drag for airfoils.

This full-day course is designed to equip engineering professionals with the knowledge and tools needed to combine the strengths of Design Engineering and Systems Engineering into Systems Design Engineering (SDE) principles. These principles will improve engineering efficiency and practically design more sustainable system-level products, all while strategically aligning with digital transformation objectives.

Design of Experiments (DOE) is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings. This course utilizes hands-on activities to help you learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run.

Design for Manufacturing and Assembly (DFM+A), pioneered by Boothroyd and Dewhurst, has been used by many companies around the world to develop creative product designs that use optimal manufacturing and assembly processes. Correctly applied, DFM+A analysis leads to significant reductions in production cost, without compromising product time-to-market goals, functionality, quality, serviceability, or other attributes. In this two-day course, you will not only learn the Boothroyd Dewhurst Method, you will actually apply it to your own product design!

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New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)