Synopsis:
This course covers the fundamentals of radar systems. The basic radar techniques are discussed including moving target indication, pulse Doppler, measurement of range and velocity, signal-to-noise ratio and clutter cancellation. Various types of radar system are covered: synthetic aperture, weather radar, bistatic, search, tracking, and more. Radar system hardware is also discussed.
Key Topics:
- The objective is to provide all students with an understanding of fundamental radar principles and techniques, and how they are used in a variety of radar applications.
- Concepts are introduced with both mathematical explanations and graphical illustrations. Therefore students without a strong math background can grasp the physical principles.
- The course content includes a review of the required background material, the introduction of basic radar theory and techniques, and discussion of several radar systems and applications.
- The course is self-contained in that all of the background material is included.
- There are an extensive number of worked examples.
- Some MATLAB software is also provided and MATLAB examples are included in the lectures.
Who Should Attend:
The course will benefit engineers both young and old who need a basic understanding of gas turbine engine systems & components and the associated design process. The course is designed for engineers with some familiarity of basic aerodynamics, gas dynamics & thermodynamics. Some knowledge of engine cycles will also be useful.
Instructed by Bong Wie, Professor of Aerospace Engineering at Iowa State University
- This course introduces a practical approach to flight dynamics and control of aircraft, missiles, and hypersonic vehicles, which utilizes MATLAB’s computational tools of control systems design and simulation
- It will also cover a variety of flight control design examples to enhance the learning experience. They include Boeing 737 Max aircraft’s MCAS (Maneuvering Characteristics Augmentation System); SAS (Stability Augmentation Systems); ILS (Instrument Landing Systems); skid-to-turn, bank-to-turn, and coordinated turn of flight vehicles
- All students will receive an AIAA Certificate of Completion at the end of the course
Instructed by Dr. Florian Menter, Chief Scientist ANSYS Germany GmbH; developer of the widely used Shear-Stress Transport (SST) turbulence model.
Instructed by Dr. Ian Halliwell, Northwind Propulsion Inc. and Mr. Clement Joly, SoftInWay
- This student favorite online course covers the complete spectrum of the Gas Turbine Engine design process
- All students will receive an AIAA Certificate of Completion at the end of the course
Instructed by Teaching Science and Technology Inc. (TSTI)
- The Foundations of Digital Engineering course prepares knowledge workers on the front line of this transformation to navigate the transition to the 4th Industrial Revolution – Digital Transformation.
- All students will receive an AIAA Certificate of Completion at the end of the cou
- A Hands-on Approach to Understanding the Processes, Practicalities and Potentials of MBSE for your Projects
- Includes a copy of the course textbook Applied Space Systems Engineering and a complete set of course notes
This 1.5-day course provides a broad overview of the processes, practices, tools, and techniques that comprise the emerging discipline of model-based systems engineering (MBSE) with emphasis on practical application. The course examines the “why,” “what” and “how” of MBSE beginning with its basic value proposition. The course focuses on six central themes that comprise the unique advantages MBSE offers—Capturing, Connecting, Controlling, Communicating, Collaborating and Cycling. In this course, the foundations of MBSE are established by reviewing the systems engineering fundamentals and what it means to “model” SE. Ontologies, modeling languages and frameworks are then reviewed to establish a basis for their use in any MBSE project. From this foundation, participants are guided through a hands-on exercise to model a simple system.
Synopsis:
Computational modeling is becoming more prevalent in the engineering analysis and design process. This increased reliance means that one must understand the accuracy of the computational models. A first step in understanding the accuracy is to identify the model input parameters for which the computational model is most sensitive. The course will specifically focus on the following techniques for determining sensitivity information: differentiation of analytical models, finite difference of computational models, complex step method, software differentiation, sensitivity equation methods, adjoint methods, and sampling methods (Monte Carlo and Latin Hypercube). Techniques for propagating uncertainty in model inputs through computational models will also be presented. Uncertainty propagation techniques that use the sensitivity information (first-order techniques) and more general techniques based on sampling are covered in the course. The final topic covered is validation of computational models. Validation is a process to assess the accuracy of computational models by comparing to experimental data.
Key Topics:
- Insights and application of sensitivity analysis
- Approaches for computing sensitivity coefficients
- Uncertainty propagation through computational models
- Sampling-based methods for propagating uncertainty and performing sensitivity analysis
- Methodology for validation of computational models
Who Should Attend:
This course is intended for engineering analysts that are faced with determining the sensitivity of computational models to parameters in their models. The minimum background is a BS in engineering (or related field). Managers directing the activities of staff responsible for sensitivity analysis would also benefit from this course.
This comprehensive 2-day course covers systems thinking for addressing complexity in the development of modern aerospace systems. Applying a systems approach provides insight into the unexpected ways a system will behave due to complexity. Learning how to deal with scale, interdependencies and interconnectedness in large systems uncovers leverage points for managing complexity. The course covers complexity management from a number of perspectives, including organizational learning, quantitative metrics and system visualization techniques.
