35,328 research outputs found
Feedback methods for inverse simulation of dynamic models for engineering systems applications
Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed
applications. This paper includes a review of established methods of inverse simulation,giving some emphasis to iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach which is based on feedback principles. This feedback method is suitable for a wide range of linear and nonlinear dynamic models and involves two distinct stages. The first stage involves
design of a feedback loop around the given simulation model and, in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within
closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications
of this feedback approach to inverse simulation are described through case studies that put particular emphasis on nonlinear and multi-input multi-output models
Dynamic stability of space vehicles. Volume 3 - Torsional vibration modes
Torsional model development and model calculations for cylindrical space vehicle systems and systems employing clustered tank
Longitudinal dynamics of liquid filled elastic shells
Longitudinal dynamics of liquid filled elastic shells - interaction liquid and elastic tank, liquid surface instability, and bubble dynamic
Qualitative System Identification from Imperfect Data
Experience in the physical sciences suggests that the only realistic means of
understanding complex systems is through the use of mathematical models.
Typically, this has come to mean the identification of quantitative models
expressed as differential equations. Quantitative modelling works best when the
structure of the model (i.e., the form of the equations) is known; and the
primary concern is one of estimating the values of the parameters in the model.
For complex biological systems, the model-structure is rarely known and the
modeler has to deal with both model-identification and parameter-estimation. In
this paper we are concerned with providing automated assistance to the first of
these problems. Specifically, we examine the identification by machine of the
structural relationships between experimentally observed variables. These
relationship will be expressed in the form of qualitative abstractions of a
quantitative model. Such qualitative models may not only provide clues to the
precise quantitative model, but also assist in understanding the essence of
that model. Our position in this paper is that background knowledge
incorporating system modelling principles can be used to constrain effectively
the set of good qualitative models. Utilising the model-identification
framework provided by Inductive Logic Programming (ILP) we present empirical
support for this position using a series of increasingly complex artificial
datasets. The results are obtained with qualitative and quantitative data
subject to varying amounts of noise and different degrees of sparsity. The
results also point to the presence of a set of qualitative states, which we
term kernel subsets, that may be necessary for a qualitative model-learner to
learn correct models. We demonstrate scalability of the method to biological
system modelling by identification of the glycolysis metabolic pathway from
data
The middeck 0-gravity dynamics experiment
The Middeck 0-Gravity Dynamics Experiment (MODE), flown onboard the Shuttle STS-48 Mission, consists of three major elements: the Experiment Support Module, a dynamics test bed providing computer experiment control, analog signal conditioning, power conditioning, an operator interface consisting of a keypad and display, experiment electrical and thermal control, and archival data storage: the Fluid Test Article assembly, used to investigate the dynamics of fluid-structure interaction in 0-gravity; and the Structural Test Article for investigating the open-loop dynamics of structures in 0-gravity. Deployable, erectable, and rotary modules were assembled to form three one- and two-dimensional structures, in which variations in bracing wire and rotary joint preload could be introduced. Change in linear modal parameters as well as the change in nonlinear nature of the response is examined. Trends in modal parameters are presented as a function of force amplitude, joint preload, and ambient gravity. An experimental study of the lateral slosh behavior of contained fluids is also presented. A comparison of the measured earth and space results identifies and highlights the effects of gravity on the linear and nonlinear slosh behavior of these fluids
The COLD-SAT Experiment for Cryogenic Fluid Management Technology
Future national space transportation missions will depend on the use of cryogenic fluid management technology development needs for these missions. In-space testing will be conducted in order to show low gravity cryogenic fluid management concepts and to acquire a technical data base. Liquid H2 is the preferred test fluid due to its propellant use. The design of COLD-SAT (Cryogenic On-orbit Liquid Depot Storage, Acquisition, and Transfer Satellite), an Expendable Launch Vehicle (ELV) launched orbital spacecraft that will perform subcritical liquid H2 storage and transfer experiments under low gravity conditions is studied. An Atlas launch vehicle will place COLD-SAT into a circular orbit, and the 3-axis controlled spacecraft bus will provide electric power, experiment control, and data management, attitude control, and propulsive accelerations for the experiments. Low levels of acceleration will provide data on the effects that low gravity might have on the heat and mass transfer processes used. The experiment module will contain 3 liquid H2 tanks; fluid transfer, pressurization and venting equipment; and instrumentation
Dynamic stability and parametric resonance in cylindrical propellant tanks Final report
Dynamic stability and parametric resonance of longitudinally excited liquid propellant tank mode
Matrix Holzer analyses for fully-coupled vibrations of clustered launch-vehicle configurations including applications to the Titan IIIC and uncoupled Saturn I cases
Matrix-Holzer analyses for predicting free vibration modes of clustered launch vehicle configurations including Titan IIIC and uncoupled Saturn I case
Grey-box model identification via evolutionary computing
This paper presents an evolutionary grey-box model identification methodology that makes the best use of a priori knowledge on
a clear-box model with a global structural representation of the physical system under study, whilst incorporating accurate blackbox
models for immeasurable and local nonlinearities of a practical system. The evolutionary technique is applied to building
dominant structural identification with local parametric tuning without the need of a differentiable performance index in the
presence of noisy data. It is shown that the evolutionary technique provides an excellent fitting performance and is capable of
accommodating multiple objectives such as to examine the relationships between model complexity and fitting accuracy during the
model building process. Validation results show that the proposed method offers robust, uncluttered and accurate models for two
practical systems. It is expected that this type of grey-box models will accommodate many practical engineering systems for a better
modelling accuracy
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