56,058 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
A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF
The future evolution of technological systems dedicated to improve energy
efficiency will strongly depend on effective and reliable Energy Storage
Systems, as key components for Smart Grids, microgrids and electric mobility.
Besides possible improvements in chemical materials and cells design, the
Battery Management System is the most important electronic device that improves
the reliability of a battery pack. In fact, a precise State of Charge (SoC)
estimation allows the energy flows controller to exploit better the full
capacity of each cell. In this paper, we propose an alternative definition for
the SoC, explaining the rationales by a mechanical analogy. We introduce a
novel cell model, conceived as a series of three electric dipoles, together
with a procedure for parameters estimation relying only on voltage measures and
a given current profile. The three dipoles represent the quasi-stationary, the
dynamics and the istantaneous components of voltage measures. An Extended
Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we
propose a multi-cell EKF system based on a round-robin approach to allow the
same processing block to keep track of many cells at the same time. Performance
tests with a prototype battery pack composed by 18 A123 cells connected in
series show encouraging results.Comment: 8 page, 12 figures, 1 tabl
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Predictive Control Applied to a Solar Desalination Plant Connected to a Greenhouse with Daily Variation of Irrigation Water Demand
The water deficit in the Mediterranean area is a known matter severely affecting agriculture. One way to avoid the aquifers’ exploitation is to supply water to crops by using thermal desalination processes. Moreover, in order to guarantee long-term sustainability, the required thermal energy for the desalination process can be provided by solar energy. This paper shows simulations for a case study in which a solar multi-effect distillation plant produces water for irrigation purposes. Detailed models of the involved systems are the base of a predictive controller to operate the desalination plant and fulfil the water demanded by the crops
Exploratory Research on MEMS Technology for Air-Conditioning and Heat-Pumps
This report details the efforts to exploit micro-electrical-mechanical-systems (MEMS)
and micro device technologies to improve control of multi-channel evaporators by
reducing maldistribution among channels, and increase capacity and efficiency of
current vapor-compression refrigeration chillers and heat-pumps. Besides
summarizing the market potential of MEMS technology for use in evaporators and
micro-heat-pumps, the report describes the accomplishments of an experimental
investigation of refrigerant-side maldistribution in multi-channel plate heat exchangers
(PHE's). A special test facility designed for the purpose of studying the
maldistribution of refrigerant in evaporators is described in the report. The facility
allows maldistribution caused by either normal superheat temperature control, or
induced by the user in controlled amounts, to be measured and quantified. Four
different techniques were used to detect the presence of liquid droplets in the stream of
superheated vapor at the evaporator exit, an indication of maldistributed flow. They
are: Helium-Neon laser, beaded thermocouple, static mixer and newly designed heated
MEMS sensor. Comparison of the four techniques shows that the MEMS sensor
designed and fabricated in this project has the highest potential for indicating
maldistribution, manifested by entrained liquid droplets, in multi-channel evaporators.
A complete set of test results in the time and frequency domain is show in graphical
form in the appendices. The design, fabrication, calibration, and testing of the MEMS
serpentine resistance sensor is also reported, along with a control scheme and strategy
for implementing the MEMS sensor in multi-channel evaporator systems
Modelica - A Language for Physical System Modeling, Visualization and Interaction
Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented
LABORATORY SIMULATION OF TURBULENT-LIKE FLOWS
Most turbulence studies up to the present are based on statistical modeling, however,
the spatio-temporal flow structure of the turbulence is still largely unexplored. Tur-
bulence has been established to have a multi-scale instantaneous streamline structure
which influences the energy spectrum and other properties such as dissipation and
mixing.
In an attempt to further understand the fundamental nature of turbulence and its
consequences for efficient mixing, a new class of flows, so called “turbulent-like”, is in-
troduced and its spatio-temporal structure of the flows characterised. These flows are
generated in the laboratory using a shallow layer of brine and controlled by multi-scale
electromagnetic forces resulting from a combination of electric current and a magnetic
field created by a fractal permanent magnet distribution. These flows are laminar, yet
turbulent-like, in that they have multi-scale streamline topology in the shape of “cat’s
eyes” within “cat’s eyes” (or 8’s within 8’s) similar to the known schematic streamline
structure of two-dimensional turbulence. Unsteadiness is introduced to the flows by
means of time-dependent electrical current.
Particle Tracking Velocimetry (PTV) measurements are performed. The technique
developed provides highly resolved Eulerian velocity fields in space and time. The
analysis focuses on the impact of the forcing frequency, mean intensity and amplitude
on various Eulerian and Lagrangian properties of the flows e.g. energy spectrum and
fluid element dispersion statistics. Other statistics such as the integral length and time
scales are also extracted to characterise the unsteady multi-scale flows.
The research outcome provides the analysis of laboratory generated unsteady multi-
scale flows which are a tool for the controlled study of complex flow properties related
to turbulence and mixing with potential applications as efficient mixers as well as in
geophysical, environmental and industrial fields
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TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution
The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a
satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A
ton-level liquid scintillator detector will be placed at about 30 m from a core
of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be
measured with sub-percent energy resolution, to provide a reference spectrum
for future reactor neutrino experiments, and to provide a benchmark measurement
to test nuclear databases. A spherical acrylic vessel containing 2.8 ton
gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon
Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full
coverage. The photoelectron yield is about 4500 per MeV, an order higher than
any existing large-scale liquid scintillator detectors. The detector operates
at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The
detector will measure about 2000 reactor antineutrinos per day, and is designed
to be well shielded from cosmogenic backgrounds and ambient radioactivities to
have about 10% background-to-signal ratio. The experiment is expected to start
operation in 2022
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