• ARCSIM Vehicle Dynamics Simulation Software

  This project integrates ARC-developed code from Thrust Area 1 to produce a working software package. The goal is to speed the transfer of new ARC technology to industry and the army. The project draws on an architecture called the simulation graphical user interface (SGUI) to rapidly create full simulation environments with point-and-click graphical interfaces, plotting and wire-frame animation. The deliverable for the project is ARCSim, a software package that runs on desktop PCs with Windows 9x or Windows NT. It includes a mixture of working simulation programs, working viewer programs, data files generated by external programs and simulation programs generated in the research. PC Version.

• Crewman's Associate for Path Control (CAPC)

  CAPC is a simulation environment for investigating road-departure prevention systems and supporting subsystems, including: roadway sensing, road geometry estimation, vehicle parameter estimation, differential braking control systems, driver state assessment. The environment includes a computer mouse-driven simulator and a numerical simulation. A large selection of models and controllers are available via pull-down menus while an animation of vehicle motion and sensor views helps the user visualize the results. The CAPC tool was used to design and test a vision-based road-departure prevention system implemented and demonstrated in 1995 on a Ford Taurus SHO. PowerPC and 68K Mac Versions.

• Computer-Aided Model Building Automation System (CAMBUS)

  CAMBAS is an automated modeling environment for generating Proper Models of dynamic systems. This software is developed to demonstrate the system decomposition modeling technique and also to implement the Model Deduction Algorithms developed in the Automated Modeling Laboratory. CAMBAS uses expandable templates of components (in bond graph form) stored in libraries, which the design engineer selects to build a high level representation of the system. Then the software automatically assembles the global dynamic model of the system. This system model is processed by the deduction search algorithms in order to generate the Proper Model that is simple, yet accurate enough to meet the user specified engineering objectives. The outcome of CAMBAS is the state space Proper Model in matrix form. SunOS Version.

• Diesel Engine Simulation (DES)

  This is a zero-dimensional thermodynamic simulation of a direct injection single- or multi-cylinder diesel engine. The diesel four-stroke cycle is treated as a sequence of continuous processes: intake, compression, combustion (including expansion), and exhaust. Engine cycle, heat transfer, and turbulent flow ouput data and plots are generated by the simulation.

• Optimal Hypergraph-Based Decomposition of Design Problems

  This software implements a hypergraph-based methodology to partition design problems for their solution in a distributed computing environment. A design problem is defined by the dependencies of the design relations on the design variables and weighting coefficients that represent design relation computational costs and the strength of their interdependence. In an optimal partition the original design problem is divided into weakly connected subproblems, which are balanced in size and have to be solved using with a coordination strategy.

• Optimal ILP-Based Decomposition of Design Problems

  This software implements an integer programming formulation for synthesizing hierarchically decomposed optimal design problems. A design problem is defined by the dependencies of the design relations on the design variables. In an optimal partition the original design problem is divided into weakly connected subproblems, which are balanced in size, and a master problem that implements the coordination strategy.

• Nonlinear Constrained Optimization

  This a web implementation of the Sequential Quadratic Programming algorithm to maximize a nonlinear function subject to nonlinear constraints. The general nonlinear constrained optimization problem can be stated as

  Max { f(x) : g(x) <= 0, h(x) = 0 }

  where x is the vector of optimization variables, f(x) is the objective function to be maximized, g(x) is the vector of inequality constraints, and h(x) is the vector of equality constraints. Simple bounds on x can be included as inequality constraints.

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