Michael Tiller

Feasibility of Detailed Vehicle Modeling

A feasibility study is presented concerning detailed vehicle modeling, including submodels for engine, transmission mechanics and hydraulics, as well as three-dimensional chassis behavior. The study was conducted jointly by Ford Motor Company, Dynasim AB and DLR. The results demonstrate that complex behavioral models of each subsystem can be developed, used and validated independently from each other, and finally assembled together to an overall model. Therefore, this approach could be the basis to establish modeling standards that allow collaboration between model developers throughout the automotive industry.

A Comparison of Different Methods for Battery and Supercapacitor Modeling

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling willbecome increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations. The developed models will be discussed and comparisons between the two modeling techniques (i), (ii) and measurement data will be made. This paper shows that using Modelica, the modeling language used by Dymola to describe physical components, leads to increased model flexibility and faster model development. Furthermore, a Dymola generated plant model runs faster than the equivalent Simulink model when exported into the Simulink environment and run in conjunction with a Simulink controller model.

Multi-Domain Modeling

This chapter presents several multi-domain system models. Multi-domain models are characterized by the fact that they have components belonging to different engineering domains. In this chapter, we will see models from the mechanical (both rotational and translational), electrical and thermodynamic domains. In addition, many of the examples contain block diagrams for some subsystems (e.g., for control systems).

Update on Small Modular Reactors Dynamic System Modeling Tool: Web Application

Previous reports focused on the development of component and system models as well as end-to-end system models using Modelica and Dymola for two advanced reactor architectures: (1) Advanced Liquid Metal Reactor and (2) fluoride high-temperature reactor (FHR). The focus of this report is the release of the first beta version of the web-based application for model use and collaboration, as well as an update on the FHR model. The web-based application allows novice users to configure end-to-end system models from preconfigured choices to investigate the instrumentation and controls implications of these designs and allows for the collaborative development of individual component models that can be benchmarked against test systems for potential inclusion in the model library. A description of this application is provided along with examples of its use and a listing and discussion of all the models that currently exist in the library.

Analytic Evaluation of Engine NVH Robustness Due to Manufacturing Variations

In an effort to improve quality, shorten engine development times, and reduce costly and time-consuming experimental work, analytic modeling is being used upstream in the product development process to evaluate engine robustness to noise factors. This paper describes a model-based method for evaluating engine NVH (Noise, Vibration, and Harshness) robustness due to manufacturing variations for a statistically significant engine population. A brief discussion of the cycle simulation model and its capabilities is included. The methodology consists of Monte Carlo simulations involving several noise factors to obtain the crank-angle resolved response of the combustion process and Fourier analysis of the resulting engine torque. Further analysis of the Fourier results leads to additional insights regarding the relative importance of and sensitivity to the individual noise factors. While the cost and resources required to experimentally evaluate a large engine population can be prohibitive, the analytical modeling proved to be a cost-effective way of analyzing the engine robustness taking into account manufacturing process capability.Copyright

Update on Small Modular Reactors Dynamics System Modeling Tool -- Molten Salt Cooled Architecture

The Small Modular Reactor (SMR) Dynamic System Modeling Tool project is in the third year of development. The project is designed to support collaborative modeling and study of various advanced SMR (non-light water cooled) concepts, including the use of multiple coupled reactors at a single site. The objective of the project is to provide a common simulation environment and baseline modeling resources to facilitate rapid development of dynamic advanced reactor SMR models, ensure consistency among research products within the Instrumentation, Controls, and Human-Machine Interface (ICHMI) technical area, and leverage cross-cutting capabilities while minimizing duplication of effort. The combined simulation environment and suite of models are identified as the Modular Dynamic SIMulation (MoDSIM) tool. The critical elements of this effort include (1) defining a standardized, common simulation environment that can be applied throughout the program, (2) developing a library of baseline component modules that can be assembled into full plant models using existing geometry and thermal-hydraulic data, (3) defining modeling conventions for interconnecting component models, and (4) establishing user interfaces and support tools to facilitate simulation development (i.e., configuration and parameterization), execution, and results display and capture.

Dynamic Simulation of Small Modular Reactors Using Modelica

Small modular reactors (SMRs) offer potential for addressing the nation’s long-term energy needs. However, the project design cycle for new reactor concepts is lengthy. As part of the Department of Energy’s Advanced SMR research and development program, Oak Ridge National Laboratory (ORNL) is developing a Dynamic System Modeling Tool (MoDSIM) to facilitate rapid instrumentation and controls studies of SMR concepts.Traditional nuclear reactor design makes use of verified and validated codes to meet the strict quality assurance requirements of the licensing process for the Nuclear Regulatory Commission. However, there are significant engineering analyses and high-level decisions required prior to the rigorous design phase. These analyses typically do not require high-fidelity codes. Different organizations and researchers may examine various plant configuration options prior to formal design activities. Engineers and managers must continuously make down-selection decisions regarding potential reactor architectures and subsystems. Traditionally, the modeling of these complex systems has been based on legacy models. Considerable time and effort are necessary to understand and manipulate these legacy models. For trade-space studies, two developments in the model-based systems engineering space represent a significant advancement in the ability of engineering tools to meet these demands. The first is Modelica: a nonproprietary, equation-based, object-oriented modeling language for cyber-physical systems. The second is the Functional Mockup Interface: a standardized, open interface for model exchange, simulation, and deployment.ORNL’s MoDSIM tool makes use of these developments and is intended to provide a flexible and robust dynamic system-modeling environment for SMRs. This includes single or multiple reactors, perhaps sharing common resources, or producing both electricity and process heat for local consumption or feeding a regional grid. MoDSIM uses the open-source modeling language (Modelica) and incorporates a user interface, coupled dynamic models, and analysis capabilities that will enable non-expert modelers to perform sophisticated end-to-end system simulations of both neutronic and thermal-hydraulic models. This approach enables open-source and crowd-source-type collaborations for model development of SMRs in an approach similar to open-source and open-design techniques currently used for software production and complex system design. As part of the tool development, an example SMR was chosen (advanced liquid metal reactor [ALMR]) and the ALMR models developed and interface tools demonstrated. For initial verification purposes, the results from these Modelica simulations are compared with the results documented for the earlier ALMR power-reactor innovative small-module concept. These results, as well as initial demonstrations of the tool for different control strategies, are presented in this paper.Copyright

Building and Connecting Components

While equations are an essential part of model development, it quickly becomes tedious to write out all the equations for the components in a system. In this chapter, we show how to reuse constitutive equations like Ohm’s law and automatically generate conservation equations for quantities like energy and mass. In doing so, it is possible to quickly build up large models of interacting components. Once again, examples will demonstrate various language features and the section at the end of the chapter will discuss these features in detail.

Exploring Nonlinear Behavior

The point at which modeling gets particularly interesting is when model behavior becomes increasingly nonlinear. It is no coincidence that this is the point where simulation tools start having trouble. Nonlinear behavior is hard to avoid in real world models. The examples in this chapter will introduce some of the approaches used to describe nonlinear behavior.

Block Diagrams vs. Acausal Modeling

In this section, we will discuss, in detail, the differences between the block diagram and acausal approaches introduced in Section 1.3.