Search results for "Runge–Kutta method"

showing 10 items of 14 documents

Modelling phase transition kinetics of chenodeoxycholic acid with the Runge–Kutta method

2009

Abstract The phase transition kinetics of two chenodeoxycholic acid polymorphic modifications— form I (stable at high temperature), form III (stable at low temperature) and the amorphous phase has been examined under various conditions of temperature and relative humidity. Form III conversion to form I was examined at high temperature conditions and was found to be non-spontaneous, requiring seed crystals for initiation. The formation kinetic model of form I was created incorporating the three-dimensional seed crystal growth, the phase transition rate proportion to the surface area of form I crystals, and the influence of the amorphous phase surface area changes with an empirical stage poin…

Phase transitionDifferential Thermal AnalysisSpectrophotometry InfraredDifferential equationClinical BiochemistryPharmaceutical ScienceThermodynamicsChenodeoxycholic AcidKinetic energyPhase TransitionAnalytical ChemistryReaction rate constantDrug StabilityX-Ray DiffractionDrug DiscoverySample preparationSpectroscopySeed crystalModels StatisticalCalorimetry Differential ScanningChemistryTemperatureKineticsRunge–Kutta methodsCrystallographyX-ray crystallographyCrystallizationJournal of Pharmaceutical and Biomedical Analysis
researchProduct

NUMERICAL ALGORITHMS

2013

For many systems of differential equations modeling problems in science and engineering, there are natural splittings of the right hand side into two parts, one non-stiff or mildly stiff, and the other one stiff. For such systems implicit-explicit (IMEX) integration combines an explicit scheme for the non-stiff part with an implicit scheme for the stiff part. In a recent series of papers two of the authors (Sandu and Zhang) have developed IMEX GLMs, a family of implicit-explicit schemes based on general linear methods. It has been shown that, due to their high stage order, IMEX GLMs require no additional coupling order conditions, and are not marred by order reduction. This work develops a …

General linear methodsMathematical optimizationIMEX methods; general linear methods; error analysis; order conditions; stability analysisIMEX methodsDifferential equationSCHEMESorder conditionsMathematics AppliedExtrapolationStability (learning theory)QUADRATIC STABILITYstability analysisPARABOLIC EQUATIONSSYSTEMSNORDSIECK METHODSFOS: MathematicsApplied mathematicsMathematics - Numerical AnalysisRUNGE-KUTTA METHODSMULTISTEP METHODSerror analysisMathematicsCONSTRUCTIONSeries (mathematics)Applied MathematicsNumerical analysisComputer Science - Numerical AnalysisStability analysisORDEROrder conditionsNumerical Analysis (math.NA)Computer Science::Numerical AnalysisRunge–Kutta methodsGeneral linear methodsError analysisORDINARY DIFFERENTIAL-EQUATIONSOrdinary differential equationgeneral linear methodsMathematics
researchProduct

A Magnetohydrodynamic Generator for Marine Energy Harvesting

2018

In this article we present an approach to the description of Magneto-hydrodynamic Marine Energy Harvesting (MHMEH) system. Preliminarly, a general discussion on the principle of operation is presented. Successively, in order to move beyond the analytical model, a 3-D MHD modeling tool and a Runge Kutta method based solver are presented and they are used to investigate an alternative MHD solutions. Some numerical analyses are given.

Magnetohydrodynamic generatorComputer science020209 energyMagnetohydrodinamic Propulsion Systems02 engineering and technologySolverDesalinationFinite element methodlaw.inventionRunge–Kutta methodslawMarine energy0202 electrical engineering electronic engineering information engineeringApplied mathematicsMagnetohydrodynamicsOCEANS 2018 MTS/IEEE Charleston
researchProduct

Equilibrium real gas computations using Marquina's scheme

2003

Marquina's approximate Riemann solver for the compressible Euler equations for gas dynamics is generalized to an arbitrary equilibrium equation of state. Applications of this solver to some test problems in one and two space dimensions show the desired accuracy and robustness

Real gasApplied MathematicsMechanical EngineeringMathematical analysisMathematicsofComputing_NUMERICALANALYSISComputational MechanicsSolverSpace (mathematics)Compressible flowRiemann solverComputer Science ApplicationsEuler equationsRunge–Kutta methodssymbols.namesakeMechanics of MaterialsComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONCompressibilitysymbolsMathematicsInternational Journal for Numerical Methods in Fluids
researchProduct

High-order Runge–Kutta–Nyström geometric methods with processing

2001

Abstract We present new families of sixth- and eighth-order Runge–Kutta–Nystrom geometric integrators with processing for ordinary differential equations. Both the processor and the kernel are composed of explicitly computable flows associated with non trivial elements belonging to the Lie algebra involved in the problem. Their efficiency is found to be superior to other previously known algorithms of equivalent order, in some case up to four orders of magnitude.

Numerical AnalysisDifferential equationApplied MathematicsMathematical analysisMathematicsofComputing_NUMERICALANALYSISLie groupMathematics::Numerical AnalysisComputational MathematicsRunge–Kutta methodsKernel methodKernel (image processing)Ordinary differential equationLie algebraInitial value problemApplied mathematicsMathematicsApplied Numerical Mathematics
researchProduct

The interrelation between stochastic differential inclusions and set-valued stochastic differential equations

2013

Abstract In this paper we connect the well established theory of stochastic differential inclusions with a new theory of set-valued stochastic differential equations. Solutions to the latter equations are understood as continuous mappings taking on their values in the hyperspace of nonempty, bounded, convex and closed subsets of the space L 2 consisting of square integrable random vectors. We show that for the solution X to a set-valued stochastic differential equation corresponding to a stochastic differential inclusion, there exists a solution x for this inclusion that is a ‖ ⋅ ‖ L 2 -continuous selection of X . This result enables us to draw inferences about the reachable sets of solutio…

Continuous-time stochastic processApplied MathematicsMathematical analysisStochastic calculusMalliavin calculusStochastic partial differential equationsymbols.namesakeStochastic differential equationDifferential inclusionRunge–Kutta methodsymbolsApplied mathematicsAnalysisMathematicsAlgebraic differential equationJournal of Mathematical Analysis and Applications
researchProduct

Partially Implicit Runge-Kutta Methods for Wave-Like Equations

2014

Runge-Kutta methods are used to integrate in time systems of differential equations. Implicit methods are designed to overcome numerical instabilities appearing during the evolution of a system of equations. We will present partially implicit Runge-Kutta methods for a particular structure of equations, generalization of a wave equation; the partially implicit term refers to this structure, where the implicit term appears only in a subset of the system of equations. These methods do not require any inversion of operators and the computational costs are similar to those of explicit Runge-Kutta methods. Partially implicit Runge-Kutta methods are derived up to third-order of convergence. We ana…

Physics::Computational Physics010308 nuclear & particles physicsDifferential equationMathematical analysisInversion (meteorology)010103 numerical & computational mathematicsSystem of linear equationsComputer Science::Numerical Analysis01 natural sciencesMathematics::Numerical AnalysisRunge–Kutta methods0103 physical sciences0101 mathematicsMathematics
researchProduct

Minimally implicit Runge-Kutta methods for Resistive Relativistic MHD

2016

The Relativistic Resistive Magnetohydrodynamic (RRMHD) equations are a hyperbolic system of partial differential equations used to describe the dynamics of relativistic magnetized fluids with a finite conductivity. Close to the ideal magnetohydrodynamic regime, the source term proportional to the conductivity becomes potentially stiff and cannot be handled with standard explicit time integration methods. We propose a new class of methods to deal with the stiffness fo the system, which we name Minimally Implicit Runge-Kutta methods. These methods avoid the development of numerical instabilities without increasing the computational costs in comparison with explicit methods, need no iterative …

AstrofísicaHistoryResistive touchscreenPartial differential equation010308 nuclear & particles physicsExplicit and implicit methodsNumerical methods for ordinary differential equationsStiffnessMagnetohidrodinàmica01 natural sciencesComputer Science ApplicationsEducationRunge–Kutta methods0103 physical sciencesmedicineCalculusApplied mathematicsMagnetohydrodynamic driveMagnetohydrodynamicsmedicine.symptom010303 astronomy & astrophysicsMathematics
researchProduct

Generalized singly-implicit Runge-Kutta methods with arbitrary knots

1985

The aim of this paper is to derive Butcher's generalization of singly-implicit methods without restrictions on the knots. Our analysis yields explicit computable expressions for the similarity transformations involved which allow the efficient implementation of the first phase of the method, i.e. the solution of the nonlinear equations. Furthermore, simple formulas for the second phase of the method, i.e. computation of the approximations at the next nodal point, are established. Finally, the matrix which governs the stability of the method is studied.

Similarity (geometry)Computer Networks and CommunicationsGeneralizationApplied MathematicsComputationMathematical analysisStability (learning theory)Computational MathematicsMatrix (mathematics)Runge–Kutta methodsNonlinear systemSimple (abstract algebra)SoftwareMathematicsBIT
researchProduct

A computational magnetohydrodynamic model of a marine propulsion system

2016

In this article we present an approach to the description of Magnetohydrodynamic Propulsion. Preliminarly, an analytical model which includes an electromagnetic model and a thermal model is presented. Successively, in order to move beyond the analytical model, a 3-D MHD modeling tool and a Runge Kutta method based solver are presented and they are used to investigate an alternative MHD solutions. Some numerical analysis are given.

PhysicsNumerical analysisMechanicsSettore ING-IND/32 - Convertitori Macchine E Azionamenti ElettriciSolverPropulsionOceanographyFinite element methodMagnetohydrodinamic Propulsion SystemRunge–Kutta methodsSettore ING-INF/04 - AutomaticaPhysics::Plasma PhysicsMarine propulsionAutomotive EngineeringPhysics::Space PhysicsMagnetohydrodynamic driveMagnetohydrodynamicsOCEANS 2016 - Shanghai
researchProduct