Receding horizon control of cocurrent first order hyperbolic partial differential equation systems

2004 ◽  
Vol 21 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Jinhoon Choi ◽  
Kwang Soon Lee
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Receding Horizon Control ◽  
Hyperbolic Partial Differential Equation ◽  
Receding Horizon ◽  
Partial Differential ◽  
First Order

scholarly journals Moving-Horizon Modulating Functions-Based Algorithm for Online Source Estimation in a First-Order Hyperbolic Partial Differential Equation

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Sharefa Asiri ◽  
Shahrazed Elmetennani ◽  
Taous-Meriem Laleg-Kirati
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Source Term ◽  
Environmental Changes ◽  
Estimation Methods ◽  
Hyperbolic Partial Differential Equation ◽  
Partial Differential ◽  
First Order ◽  
Source Estimation ◽  
Modulating Functions

In this paper, an online estimation algorithm of the source term in a first-order hyperbolic partial differential equation (PDE) is proposed. This equation describes heat transport dynamics in concentrated solar collectors where the source term represents the received energy. This energy depends on the solar irradiance intensity and the collector characteristics affected by the environmental changes. Control strategies are usually used to enhance the efficiency of heat production; however, these strategies often depend on the source term which is highly affected by the external working conditions. Hence, efficient source estimation methods are required. The proposed algorithm is based on modulating functions method (MFM) where a moving-horizon strategy is introduced. Numerical results are provided to illustrate the performance of the proposed estimator in open-and closed-loops.

ESTIMATES FOR APPROXIMATE SOLUTIONS TO A FUNCTIONAL DIFFERENTIAL EQUATION MODEL OF CELL DIVISION

2021 ◽  
pp. 1-20
Author(s):  
STEPHEN TAYLOR ◽  
XUESHAN YANG
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Cell Division ◽  
Nonnegative Function ◽  
Approximate Solutions ◽  
Equation Model ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
First Order ◽  
Functional Differential

Abstract The functional partial differential equation (FPDE) for cell division, $$ \begin{align*} &\frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t))\\ &\quad = -(b(x,t)+\mu(x,t))n(x,t)+b(\alpha x,t)\alpha n(\alpha x,t)+b(\beta x,t)\beta n(\beta x,t), \end{align*} $$ is not amenable to analytical solution techniques, despite being closely related to the first-order partial differential equation (PDE) $$ \begin{align*} \frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t)) = -(b(x,t)+\mu(x,t))n(x,t)+F(x,t), \end{align*} $$ which, with known $F(x,t)$ , can be solved by the method of characteristics. The difficulty is due to the advanced functional terms $n(\alpha x,t)$ and $n(\beta x,t)$ , where $\beta \ge 2 \ge \alpha \ge 1$ , which arise because cells of size x are created when cells of size $\alpha x$ and $\beta x$ divide. The nonnegative function, $n(x,t)$ , denotes the density of cells at time t with respect to cell size x. The functions $g(x,t)$ , $b(x,t)$ and $\mu (x,t)$ are, respectively, the growth rate, splitting rate and death rate of cells of size x. The total number of cells, $\int _{0}^{\infty }n(x,t)\,dx$ , coincides with the $L^1$ norm of n. The goal of this paper is to find estimates in $L^1$ (and, with some restrictions, $L^p$ for $p>1$ ) for a sequence of approximate solutions to the FPDE that are generated by solving the first-order PDE. Our goal is to provide a framework for the analysis and computation of such FPDEs, and we give examples of such computations at the end of the paper.

scholarly journals Interpretation of Frenkel’s theory of sintering considering evolution of activated pores: II. Model and reliability

2015 ◽  
Vol 47 (1) ◽  
pp. 89-94
Author(s):  
C.L. Yu ◽  
D.P. Gao ◽  
S.M. Chai ◽  
Q. Liu ◽  
H. Shi ◽  
...  
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Liquid Phase ◽  
Liquid Phase Sintering ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
First Order ◽  
Sintering Time ◽  
Sintering Mechanism ◽  
Cordierite Glass

Frenkel's liquid-phase sintering mechanism has essential influence on the sintering of materials, however, by which only the initial 10% during isothermal sintering can be well explained. To overcome this shortage, Nikolic et al. introduced a mathematical model of shrinkage vs. sintering time concerning the activated volume evolution. This article compares the model established by Nikolic et al. with that of the Frenkel's liquid-phase sintering mechanism. The model is verified reliable via training the height and diameter data of cordierite glass by Giess et al. and the first-order partial differential equation. It is verified that the higher the temperature, the more quickly the value of the first-order partial differential equation with time and the relative initial effective activated volume to that in the final equibrium state increases to zero, and the more reliable the model is.

scholarly journals Pemodelan Tsunami Sederhana dengan Menggunakan Persamaan Differensial Parsial

2018 ◽  
Vol 8 (1) ◽  
pp. 26
Author(s):  
Indriati Retno Palupi ◽  
Wiji Raharjo ◽  
Eko Wibowo ◽  
Hafiz Hamdalah
Keyword(s):  
Differential Equation ◽  
Fluid Dynamics ◽  
Partial Differential Equation ◽  
Wave Propagation ◽  
Taylor Expansion ◽  
Tsunami Wave ◽  
Time Function ◽  
Hyperbolic Partial Differential Equation ◽  
Partial Differential ◽  
Space And Time

One way to solve fluid dynamics problem is using partial differential equation. By using Taylor expansion, fluid dynamics can be applied simply. For the example is tsunami wave. It is include to hyperbolic partial differential equation, tsunami wave propagation can describe in space and time function by using Euler FTCS (Forward Time Central Space) formula.

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