Systems engineering as applied to spacecraft. Introduction to spacecraft systems: propulsion, attitude dynamics and control, structures, communications, power, thermal control. Prerequisite: ASTE 280, PHYS 153L.ĪSTE 331ab Spacecraft Systems Engineering (3 units, 3 units) Space environment: atmosphere, gravity gradients, radiation. Spacecraft systems: attitude determination and control, power, thermal, command and data handling, telecommunication, structures and mechanisms, propulsion. Prerequisite: (PHYS 153L or PHYS 163L) and AME 310 corequisite: MATH 245.ĪSTE 330 Introduction to Spacecraft Systems and the Space Environment (3 units) Introduction to compressible and rarefied gas flows with applications to rocket propulsion and the dynamics of supersonic and hypersonic vehicles and spacecraft ionized gases and plasmas. Prerequisite: MATH 245, PHYS 153L.ĪSTE 305 Astronautical Gas Dynamics (4 units) Thermodynamics and statistical mechanics kinetics of atoms, molecules, and photons compressible fluid dynamics. Intended for lower-division students or those with little prior project experience.ĪSTE 301ab Thermal and Statistical Systems (3 units, 3 units) Participation in ASTE undergraduate student team projects. Introduction to rocket propulsion, spacecraft attitude dynamics and control, and space environment. Laboratory: IntroductionĪSTE 280 Foundations of Astronautical Engineering (3 units)Ĭoordinate systems and transformations. Elements of orbits, spacecraft systems, rocket Introduction to space, space exploration and the spaceīusiness. Gateway to the major in Astronautical Engineering. With that in the Catalogue, the Catalogue must be consideredĪSTE 101L Introduction to Astronautics (4 units) In the event that information on this website conflicts To improve the performance of your Monte Carlo simulations, you can distribute the computations to run in parallel on multiple cores using Parallel Computing Toolbox™ and MATLAB Parallel Server™.The USC Catalogue is the official source of information Running Monte Carlo Simulations in Parallel Simulink Design Optimization™ provides interactive tools to perform this sensitivity analysis and influence your Simulink model design. Monte Carlo simulations help you gain confidence in your design by allowing you to run parameter sweeps, explore your design space, test for multiple scenarios, and use the results of these simulations to guide the design process through statistical analysis. The design and testing of these complex systems involves multiple steps, including identifying which model parameters have the greatest impact on requirements and behavior, logging and analyzing simulation data, and verifying the system design. You can model and simulate multidomain systems in Simulink ® to represent controllers, motors, gains, and other components. Risk Management Toolbox™ facilitates credit simulation, including the application of copula models.įor more control over input generation, Statistics and Machine Learning Toolbox™ provides a wide variety of probability distributions you can use to generate both continuous and discrete inputs. Financial Toolbox™ provides stochastic differential equation tools to build and evaluate stochastic models. In financial modeling, Monte Carlo Simulation informs price, rate, and economic forecasting risk management and stress testing. MATLAB is used for financial modeling, weather forecasting, operations analysis, and many other applications. MATLAB ® provides functions, such as uss and simsd, that you can use to build a model for Monte Carlo simulation and to run those simulations.
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