Eric Walters
Purdue University
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Publication
Featured researches published by Eric Walters.
IEEE Transactions on Power Systems | 2004
Steven D. Pekarek; Oleg Wasynczuk; Eric Walters; Juri Jatskevich; Charles E. Lucas; Ning Wu; Peter Lamm
A novel multirate method of simulating power-electronic-based systems containing a wide range of time scales is presented. In this method, any suitable integration algorithm, with fixed or variable time-step, can be applied to the fast and/or slow subsystems. The subsystems exchange coupling variables at a communication interval that can be fixed or varied dynamically depending upon the state of the system variables. The proposed multirate method is applied to two example power systems that include power-electronic subsystems. Increases in simulation speed of 183-281% over established single-rate integration algorithms are demonstrated.
48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition | 2010
Eric Walters; Steve Iden; Wright-Patterson Afb; Kevin McCarthy; Marco Amrhein; Brian Raczkowski; Jason Wells; Peter Lamm; Mitch Wolff; Kirk Yerkes; Blane Wampler; William Borger
In this paper, a new subsystem-based approach to solve aerospace vehicle energy management issues is described. The goal of this approach is to create an “Energy Optimized Aircraft” that will maximize energy utilization for broad capabilities while minimizing complexity. To support this goal, an advanced modeling and simulation ICD process is established. This process addresses several of the current challenges facing modeling and simulation of large integrated systems.
SAE transactions | 2004
Juri Jatskevich; Eric Walters; Charles E. Lucas; Peter Lamm
In this paper, a parametric average-value modeling approach is applied to a high-frequency six-phase aircraft generation subsystem. This approach utilizes a detailed switch-level model of the system to numerically establish the averaged dynamic relationships between the ac inputs of the rectifier and the dc-link outputs. A comparison between the average-value and detailed models is presented, wherein, the average-value model is shown to accurately portray both the large-signal time-domain transients and the small-signal frequency-domain characteristics. Since the discontinuous switching events are not present in the average-value model, significant gains can be realized in the computational performance. For the study system, the developed average-value simulation executed more than two orders of magnitude faster than the detailed simulation.
international conference on control applications | 2000
Juri Jatskevich; Oleg Wasynczuk; Eric Walters; Charles E. Lucas
An automated state model generator (ASMG) is a tool for modeling and analysis of lumped-parameter power-electronic-based systems. In this modeling approach, the minimal state-space representation of the overall system is generated automatically and updated dynamically based upon the topological state of the system. However, due to the changing topology, simulation of a switched circuit using the ASMG requires the concatenation of solutions to the initial value problems (IVPs) corresponding to the time intervals between commutations. In this paper, a transformation of state variables is derived such that the states are continuous throughout the simulation process. This feature eliminates the need to re-initialize the ODE solver. The continuous state algorithm is verified on a high-pulse-count power supply and sets the stage for state-space averaging and system-level analysis of switched circuits.
SAE transactions | 2004
Charles E. Lucas; Eric Walters; Oleg Wasynczuk; Peter Lamm
An allocation algorithm for optimally assigning the various subsystem simulations, within a distributed heterogeneous simulation, to a specific set of computational resources has been developed. This algorithm uses a cost function that approximates the simulation execution time for each of the subsystems based upon the model complexity and the performance parameters of the available computer resources. The cost function is then evaluated to determine the optimal allocation that ensures the overall simulation execution time is minimized. In this paper, the allocation algorithm is applied to a large-scale power-electronic-based aircraft electrical power system. This study system is comprised of ten component simulations that together are modeled by 85 state variables and include 74 switching devices. Both optimal and sub-optimal allocations are considered and the predicted simulation run times are verified experimentally.
43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit | 2007
Michael Corbett; Peter Lamm; Kyle Miller; J. Wolff; Eric Walters
Aircraft power demands continue to increase with the increase in electrical subsystems. These subsystems directly affect the behavior of the power and propulsion systems and can no longer be neglected in system analyses. The performance of the whole aircraft must also be considered with the combined interactions between the power and propulsion systems. The larger loading demands placed on the power and propulsion subsystems result in thrust, speed, and altitude transients that affect the whole aircraft. This results in different operating parameters for the engine. The complex models designed to integrate new capabilities have a high computational cost. This paper investigates the possibility of using a hardware-in-the-loop (HIL) analysis with real time integration of the aircraft/propulsion system. Using this method, a significant reduction in computational runtime is observed, and the airframe/turbine engine model is usable in a HIL environment. This also allows for a more complete analysis of the interactions between engine loading and aircraft performance by including some real hardware components. The dynamic interactions between aircraft subsystems highlight the need for system-level modeling using a combination of high-fidelity computer models and hardware in a real-time environment.
applied power electronics conference | 2008
Jon K. Engelsman; Jason Wells; Eric Walters; Peter Lamm; Malcolm Daniels
A new approach to the development of a simulation model for an aircraft electrical main generator is presented. In this approach, a recently developed physical voltage behind reactance modeling technique is used to efficiently and accurately separate the stator-rotor interactions of a synchronous generator in order to increase computational efficiency. This study describes the method used to develop a representative simulation model of a synchronous generator from hardware frequency-response testing, with additional generator controls analogous to built-in systems on an actual aircraft. Comparisons are presented between results from simulation tests and hardware testing using basic step load conditions.
SAE transactions | 2004
Scott Graham; Ivan H. Wong; Won-Zon Chen; Alex Lazarevic; Keith J. Cleek; Eric Walters; Charles E. Lucas; Oleg Wasynczuk; Peter Lamm
Future Air Force intelligence, surveillance, and reconnaissance (ISR) platforms, such as high-altitude Uninhabited Aerial Vehicles (UAV), may drastically change the requirements of aircraft power systems. For example, there are potential interactions between large pulsed-power payloads and the turbine engine that could compromise the operation of the power system within certain flight envelopes. Until now, the development of large-scale, multi-disciplinary (propulsion, electrical, mechanical, hydraulic, thermal, etc.) simulations to investigate such interactions has been prohibitive due to the size of the system and the computational power required. Moreover, the subsystem simulations that are developed separately often are written in different commercial-off-the-shelf simulation programs. In this paper, a new technique useful for the numerical simulation of large-scale systems to overcome these obstacles, known as Distributed Heterogeneous Simulation (DH
Enabling technologies for simulation science. Conference | 2003
Charles E. Lucas; Eric Walters; Juri Jatskevich; Oleg Wasynczuk; Peter Lamm
), is utilized to form a dynamic system-level simulation of a high-altitude, long-endurance UAV-type of power system. This system includes detailed dynamic models of a turbine engine, high- and low-spool generators, and payloads. Although not necessary, all of the component models for this system were developed within the same simulation environment, specifically with MATLAB/Simulink. This enabled a single-computer integrated system model and a distributed computer system simulation to be formed thereby allowing for a direct comparison of simulation accuracy and computational performance for the two simulation approaches. From this comparison, it was determined that by distributing the system simulation across three computers, a 21-fold increase in simulation speed could be realized while producing nearly identical results.
canadian conference on electrical and computer engineering | 2000
Juri Jatskevich; Oleg Wasynczuk; Eric Walters; Charles E. Lucas
In this paper, a new technique useful for the numerical simulation of large-scale systems is presented. This approach enables the overall system simulation to be formed by the dynamic interconnection of the various interdependent simulations, each representing a specific component or subsystem such as control, electrical, mechanical, hydraulic, or thermal. Each simulation may be developed separately using possibly different commercial-off-the-shelf simulation programs thereby allowing the most suitable language or tool to be used based on the design/analysis needs. These subsystems communicate the required interface variables at specific time intervals. A discussion concerning the selection of appropriate communication intervals is presented herein. For the purpose of demonstration, this technique is applied to a detailed simulation of a representative aircraft power system, such as that found on the Joint Strike Fighter (JSF). This system is comprised of ten component models each developed using MATLAB/Simulink, EASY5, or ACSL. When the ten component simulations were distributed across just four personal computers (PCs), a greater than 15-fold improvement in simulation speed (compared to the single-computer implementation) was achieved.