Development and Validation of a Thermometallurgical Model for Furnace-Based Austenitization During Hot Stamping

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
M. Verma ◽  
H. Yan ◽  
J. R. Culham ◽  
M. Di Ciano ◽  
K. J. Daun
Keyword(s):  
Hot Stamping ◽  
Austenite Phase ◽  
Phase Fraction ◽  
Manganese Steel ◽  
Model Parameters ◽  
Order Model ◽  
Hearth Furnace ◽  
Detailed Model ◽  
First Order ◽  
Roller Hearth

In hot-forming die-quenching (HFDQ) boron manganese steel blanks are heated within a roller hearth furnace, and then simultaneously quenched and formed into fully martensitic body-in-white components. Industry needs models that can predict the instantaneous temperature and austenite phase fraction within the roller furnace to diagnose problems (e.g., incomplete austenitization), forecast costs, and optimize process settings. This paper introduces a thermometallurgical model for Al–Si coated 22MnB5, consisting of a coupled heat transfer and austenitization submodels. Two candidate austenitization submodels are considered: an empirical first-order model and a model based on the detailed austenitization kinetics. In the case of the first-order model, a detailed Monte Carlo procedure is used to construct 95% credibility intervals for the blank temperature and austenite phase fraction that accounts for uncertainties in the furnace temperature and model parameters. The models are first assessed using temperature and austenite phase fractions from Al–Si coated 22MnB5 coupons heated in a laboratory-scale muffle furnace, and then used to simulate austenitization of patched blanks within an industrial roller hearth furnace. The results show that the empirical first-order model provides a more robust estimate of austenite phase fraction compared to the detailed model.

An Empirical Model for a Variable Speed Refrigeration System

2011 ◽  
Vol 383-390 ◽  
pp. 7563-7568
Author(s):  
Hua Li ◽  
Ji You Fei
Keyword(s):  
Decoupling Control ◽  
Opening Angle ◽  
Model Parameters ◽  
Variable Speed ◽  
Refrigeration System ◽  
Order Model ◽  
Indoor Temperature ◽  
First Order ◽  
Proposed Model ◽  
Expansion Valve

This paper deals with an empirical dynamic model for decoupling control of the variable speed refrigeration system (VSRS). To cope with inherent complexity and nonlinearity in system dynamics, the model parameters are first obtained based on experimental data. In the study, the dynamic characteristics of indoor temperature and superheat are assumed to be first-order model with time delay. While the compressor frequency and opening angle of electronic expansion valve are varying, the indoor temperature and the superheat exhibit interfering characteristics each other in the VSRS. Thus, each decoupling model has been proposed to eliminate such interference. Finally, the experiment and simulation results indicate that the proposed model offers more tractable means for describing the actual VSRS comparing to other models currently available.

scholarly journals Control System Design Based on a Universal First Order Model with Time Delays

10.14311/258 ◽  
2001 ◽  
Vol 41 (4-5) ◽  
Author(s):  
T. Vyhlídal ◽  
P. Zítek
Keyword(s):  
Control Algorithm ◽  
Internal Model Control ◽  
Model Parameters ◽  
Order Model ◽  
Relay Feedback ◽  
First Order ◽  
Control Scheme ◽  
Model Control ◽  
Siso Systems ◽  
Modelling Approach

An original modelling approach for SISO systems is presented, based on a first order model with more than one delay in its structure. By means of this model it is possible truly to hit off the properties of systems which are conventionally described by higher order models. The identification method making use of a relay feedback test combined with transient responses of the system has proved to be suitable for assessing the model parameters. With respect to its plain structure the model is well suited to be applied in the framework of an internal model control scheme (IMC). The resultant control algorithm with only one optional parameter is very simple and can easily be implemented, for example by means of a programmable controller (PLC).

Development of a Thermo-Metallurgical Model to Predict Heating and Austenitization of 22MnB5 for Hot Forming Die Quenching

2017 ◽  
Author(s):  
M. Verma ◽  
J. R. Culham ◽  
M. Di Ciano ◽  
K. J. Daun
Keyword(s):  
Spot Welding ◽  
Present Model ◽  
Hot Forming ◽  
Austenite Formation ◽  
Hearth Furnace ◽  
First Order ◽  
Ultrahigh Strength Steel ◽  
Die Quenching ◽  
Body In White ◽  
Roller Hearth

Hot forming die quenching (HFDQ) is used to transform ultrahigh strength steel blanks into martensitic body-in-white components that are lighter than parts made from traditional mild steels, without sacrificing crash performance. The part is sometimes locally reinforced by spot-welding patches to the blanks, but the increased thickness of the patched blanks sometimes results in incomplete austenitization, which can compromise the strength of as-formed parts. This paper presents an integrated thermo-metallurgical model of the austenitization of Al-Si coated 22MnB5 within a roller hearth furnace. While previous models account for the latent heat of austenitization by heuristically adjusting the specific heat, the present model explicitly simulates austenite formation using a first-order metallurgy submodel derived from dilatometry measurements. The model is validated by comparing predicted temperatures to measurements carried out on coupons heated within a lab-scale muffle furnace and full-sized blanks heated in an industrial-scale roller hearth furnace. Finally, the model is used to optimize roller speed based on zone temperatures.

scholarly journals Resistance in a non-linear autoregressive model of pulmonary mechanics

2016 ◽  
Vol 2 (1) ◽  
pp. 623-627
Author(s):  
Ruby Langdon ◽  
Paul D. Docherty ◽  
Bernhard Laufer ◽  
Knut Möller
Keyword(s):  
Patient Specific ◽  
Pulmonary Mechanics ◽  
Model Parameters ◽  
System Modelling ◽  
Order Model ◽  
Mechanically Ventilated ◽  
First Order ◽  
Ventilator Induced Lung Injury ◽  
Non Linear ◽  
Narx Model

AbstractRespiratory system modelling can enable patient-specific mechanical ventilator settings to be found, and can thus reduce the incidence of ventilator induced lung injury in the intensive care unit. The resistance of a simple first order model (FOM) of pulmonary mechanics was compared with a flow dependent term of a non-linear autoregressive (NARX) model. Model parameters were identified for consecutive non-overlapping windows of length 20 breaths. The analysis was performed over recruitment manoeuvres for 25 sedated mechanically ventilated patients. The NARX model term, b1, consistently decreased as positive end expiratory pressure (PEEP) increased, while the FOM resistance behaviour varied. Overall the NARX b1 behaviour is more in-line with expected trends in airway resistance as PEEP increases. This work has further verified the physiologically descriptive capability of the NARX model.

Fuel Cell Frequency Response Function Modeling for Control Applications

2010 ◽  
Author(s):  
Thomas C. H. Roberts ◽  
Patrick J. Cunningham
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Transfer Function ◽  
Frequency Response ◽  
Response Function ◽  
Time Constant ◽  
Frequency Response Function ◽  
Model Parameters ◽  
Order Model ◽  
First Order

This paper provides the framework for first-order transfer function modeling of a fuel cell for controls use. It is shown that under specific conditions a fuel cell can be modeled as a first-order system. With a first order model, it is possible to determine how the fuel cell responds dynamically on a systems level before incorporating it into a larger more complex system. Current data sheets for fuel cells provide limited information of the output of the fuel cell, and a polarization curve based on static operation. This is vital information, but gives no insight into how the fuel cell responds under dynamic conditions. Dynamic responses are important when incorporating fuel cells as a power source in larger systems, such as automobiles, as loads and conditions are constantly changing. The modeling technique used in this research is the frequency response function. In this approach an experimental frequency response, or Bode plot, is computed from a frequency rich input signal and corresponding output signal. Here the controlled input is the Hydrogen flow and the output is the fuel cell voltage. During these tests, the fuel cell was connected to a constant resistance load. Using the frequency response function approach, a family of first-order transfer function models was created for a fuel cell at different operating temperatures and reactant relative humidity. These models are validated through comparison to experimental step responses. From this family of models the variations in the first-order model parameters of static gain and time constant are quantified. Static gain varied from 0.675 to 0.961 and the time constant ranged between 4.5 seconds and 10.5 seconds.

A first-order model of thermal lensing of laser propagation in the eye and implications for laser safety

2005 ◽  
Author(s):  
Robert J. Thomas ◽  
Rebecca L. Vincelette ◽  
Gavin D. Buffington ◽  
Amber D. Strunk ◽  
Michael A. Edwards ◽  
...  
Keyword(s):  
Thermal Lensing ◽  
Order Model ◽  
Laser Safety ◽  
First Order ◽  
Laser Propagation

scholarly journals Fate of false vacua in holographic first-order phase transitions

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Francesco Bigazzi ◽  
Alessio Caddeo ◽  
Aldo L. Cotrone ◽  
Angel Paredes
Keyword(s):  
Phase Transitions ◽  
Chiral Symmetry ◽  
Metastable Phase ◽  
Chiral Symmetry Breaking ◽  
Model Parameters ◽  
Temperature Phase ◽  
Thin Wall ◽  
Critical Temperatures ◽  
First Order ◽  
First Order Phase

Abstract Using the holographic correspondence as a tool, we study the dynamics of first-order phase transitions in strongly coupled gauge theories at finite temperature. Considering an evolution from the large to the small temperature phase, we compute the nucleation rate of bubbles of true vacuum in the metastable phase. For this purpose, we find the relevant configurations (bounces) interpolating between the vacua and we compute the related effective actions. We start by revisiting the compact Randall-Sundrum model at high temperature. Using holographic renormalization, we compute the derivative term in the effective bounce action, that was missing in the literature. Then, we address the full problem within the top-down Witten-Sakai-Sugimoto model. It displays both a confinement/deconfinement and a chiral symmetry breaking/restoration phase transition which, depending on the model parameters, can happen at different critical temperatures. For the confinement/deconfinement case we perform the numerical analysis of an effective description of the transition and also provide analytic expressions using thick and thin wall approximations. For the chiral symmetry transition, we implement a variational approach that allows us to address the challenging non-linear problem stemming from the Dirac-Born-Infeld action.

Application of two approaches to model chlorine residuals in Severn Trent Water Ltd (STW) distribution systems

1997 ◽  
Vol 36 (5) ◽  
pp. 317-324 ◽  
Author(s):  
M.J. Rodriguez ◽  
J.R. West ◽  
J. Powell ◽  
J.B. Sérodes
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Treatment Plant ◽  
Residual Chlorine ◽  
Ann Model ◽  
Order Model ◽  
First Order ◽  
Chlorine Decay ◽  
Artificial Neural Network Ann ◽  
Key Parameter

Increasingly, those who work in the field of drinking water have demonstrated an interest in developing models for evolution of water quality from the treatment plant to the consumer's tap. To date, most of the modelling efforts have been focused on residual chlorine as a key parameter of quality within distribution systems. This paper presents the application of a conventional approach, the first order model, and the application of an emergent modelling approach, an artificial neural network (ANN) model, to simulate residual chlorine in a Severn Trent Water Ltd (U.K.) distribution system. The application of the first order model depends on the adequate estimation of the chlorine decay coefficient and the travel time within the system. The success of an ANN model depends on the use of representative data about factors which affect chlorine evolution in the system. Results demonstrate that ANN has a promising capacity for learning the dynamics of chlorine decay. The development of an ANN appears to be justifiable for disinfection control purposes, in cases when parameter estimation within the first order model is imprecise or difficult to obtain.

scholarly journals Determination of exothermic batch reactor specific model parameters

2019 ◽  
Vol 292 ◽  
pp. 01063
Author(s):  
Lubomír Macků
Keyword(s):  
Rate Constant ◽  
Reaction Rate ◽  
Batch Reactor ◽  
Specific Model ◽  
Reaction Rate Constant ◽  
Order Reaction ◽  
First Order Reaction ◽  
Model Parameters ◽  
Reactor Model ◽  
First Order

An alternative method of determining exothermic reactor model parameters which include first order reaction rate constant is described in this paper. The method is based on known in reactor temperature development and is suitable for processes with changing quality of input substances. This method allows us to evaluate the reaction substances composition change and is also capable of the reaction rate constant (parameters of the Arrhenius equation) determination. Method can be used in exothermic batch or semi- batch reactors running processes based on the first order reaction. An example of such process is given here and the problem is shown on its mathematical model with the help of simulations.

scholarly journals New Mathematical Modeling Approach for Predicting Microbial Inactivation by High Hydrostatic Pressure

2007 ◽  
Vol 73 (8) ◽  
pp. 2468-2478 ◽  
Author(s):  
Bernadette Klotz ◽  
D. Leo Pyle ◽  
Bernard M. Mackey
Keyword(s):  
Microbial Inactivation ◽  
Inactivation Kinetics ◽  
Published Data ◽  
Model Parameters ◽  
Order Kinetic ◽  
Square Root ◽  
Additional Time ◽  
Decimal Reduction Time ◽  
First Order ◽  
Primary Model

ABSTRACT A new primary model based on a thermodynamically consistent first-order kinetic approach was constructed to describe non-log-linear inactivation kinetics of pressure-treated bacteria. The model assumes a first-order process in which the specific inactivation rate changes inversely with the square root of time. The model gave reasonable fits to experimental data over six to seven orders of magnitude. It was also tested on 138 published data sets and provided good fits in about 70% of cases in which the shape of the curve followed the typical convex upward form. In the remainder of published examples, curves contained additional shoulder regions or extended tail regions. Curves with shoulders could be accommodated by including an additional time delay parameter and curves with tails shoulders could be accommodated by omitting points in the tail beyond the point at which survival levels remained more or less constant. The model parameters varied regularly with pressure, which may reflect a genuine mechanistic basis for the model. This property also allowed the calculation of (a) parameters analogous to the decimal reduction time D and z, the temperature increase needed to change the D value by a factor of 10, in thermal processing, and hence the processing conditions needed to attain a desired level of inactivation; and (b) the apparent thermodynamic volumes of activation associated with the lethal events. The hypothesis that inactivation rates changed as a function of the square root of time would be consistent with a diffusion-limited process.

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