Partial Differential Research Articles

Concept image framework for derivatives from contour graphs

Physics experts and students commonly use a variety of representations when working with partial derivatives, including symbols, graphs, and words. One especially powerful representation is the contour graph. In open-ended problem-solving interviews with nine upper-division physics students, we asked students to determine derivatives from contour graphs with electrostatic and thermodynamic contexts. Students undertook three overarching actions: (i) orienting to the given graph, (ii) finding one or more derivatives, and (iii) reflecting on the task. In general, students tended to have more difficulty with the thermodynamic task than the electrostatic one. Students made use of a variety of representational features throughout the interviews, namely by annotating the provided contour graphs in ways that highlighted what is held constant. We use these results to generalize the existing concept image framework for ordinary derivatives to create a framework useful for understanding student ideas about multivariable derivatives. In particular, we add a new layer to the framework—the layer—that calls attention to how students do or do not attend to which variables are changing (or remaining constant) when finding a derivative. We view the change layer as a powerful central idea for both further research and the development of instructional tasks for physics students. Published by the American Physical Society 2024

Cancer-associated fibroblasts shape early myeloid cell response to chemotherapy-induced immunogenic signals in next generation tumor organoid cultures

BackgroundPatient-derived colorectal cancer (CRC) organoids (PDOs) solely consisting of malignant cells led to major advances in the understanding of cancer treatments. Yet, a major limitation is the absence of cells...

A high-order generalised differential quadrature element method for simulating 2D and 3D incompressible flows on unstructured meshes

In this paper, a high-order generalised differential quadrature element method (GDQE) is proposed to simulate two-dimensional (2D) and three-dimensional (3D) incompressible flows on unstructured meshes. In this method, the computational domain is decomposed into unstructured elements. In each element, the high-order generalised differential quadrature (GDQ) discretisation is applied. Specifically, the GDQ method is utilised to approximate the partial derivatives of flow variables and fluxes with high-order accuracy inside each element. At the shared interfaces between different GDQ elements, the common flux is computed to account for the information exchange, which is achieved by the lattice Boltzmann flux solver (LBFS) in the present work. Since the solution in each GDQ element solely relies on information from itself and its direct neighbouring element, the developed method is authentically compact, and it is naturally suitable for parallel computing. Furthermore, by selecting the order of elemental GDQ discretisation, arbitrary accuracy orders can be achieved with ease. Representative incompressible flow problems, including 2D laminar flows as well as 3D turbulent simulations, are considered to evaluate the accuracy, efficiency, and robustness of the present method. Successful numerical simulations, especially for scale-resolving 3D turbulent flow problems, confirm that the present method is efficient and high-order accurate.

Improving transonic performance with adjoint-based NACA 0012 airfoil design optimization

This study delves into the discrete adjoint method, a powerful tool in high-fidelity aerodynamic shape optimization that efficiently computes derivatives of a target function for different design variables. It offers a theoretical exploration of its implementation as an innovative tool for calculating partial derivatives (sensitivities) related to objective functions and design variables. It is implemented to a NACA0012 airfoil at a transonic speed. This designated test case is qualitatively evaluated, considering specified Mach number and Reynolds number values of 0.7 and 15.96 million, respectively. The standard Spalart-Allmaras turbulence model is adapted to improve computational cost efficiency. The results validate the efficiency of Discrete Adjoint with OpenFOAM, showcasing its capability to generate optimal configurations. The achieved optimized performance is evidenced by minimizing the drag coefficient value (Cd) to an impressive value of 0.00871, 62.2 % lower than the previous studies, thus securing a novelty. While this research does not consider the post-processing of sensitivity calculations, it enables the potential for future research. The primary target of this paper is to assess the educational and training needs of researchers, engineers, and graduate students, offering a comprehensive introduction to discrete adjoint aerodynamic design optimization, presenting a novel methodology, and contributing to future advancements in the design optimization field.

Sobolev regularity of bivariate isogeometric finite element spaces in case of a geometry map with degenerate corner

We investigate Sobolev regularity of bivariate functions obtained in Isogeometric Analysis when using geometry maps that are degenerate in the sense that the first partial derivatives vanish at isolated points. In particular, we show how the known C1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C^1$$\\end{document}-conditions for D-patches have to be tightened to guarantee square integrability of second partial derivatives, as required when computing finite element approximations of elliptic fourth order PDEs like the biharmonic equation.

A lower bound estimate of solutions to the Cauchy problems for a hyperbolic Monge–Ampère equation

This paper investigates the Cauchy problems for a hyperbolic Monge–Ampère equation which can be reduced to a quasilinear hyperbolic system via Riemannian invariants. Based on the first a priori estimate of the solutions, the second a priori estimate of the derivation of the solutions, and the third a priori estimate of the continuous mould of the first‐order partial derivatives of the solutions to quasilinear hyperbolic system, a lower bound estimate of classical solutions to the Cauchy problems for a hyperbolic Monge–Ampère equation is derived.

Modelling pressure dynamics in a gas pipeline with an in-line sampling

Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure duringstart-up or shutdown and at specific extraction points. This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point. The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

Automatic differential kinematics of serial manipulator robots through dual numbers

Dual Numbers are an extension of real numbers known for its capability of performing exact automatic differentiation of one-valued functions theoretically without error approximation. Also, Differential Kinematics of robots involves the computation of the Jacobian, which is a matrix of partial derivatives of the Forward Kinematic equations with respect to the robot’s joints. Thus, to perform the automatic calculation of the Jacobian matrix, this paper presents an extension of dual numbers composed of a scalar real part and a vector dual part, where the real part represents the angular value of the robot joint, and the dual part represents the direction of the corresponding partial derivative for each joint. The presented method was implemented in Matlab through Object Orientes Programming (OOP), and the results for calculating the Jacobian of the KUKA KR 500 robot model for 1000 random postures were subsequently compared in terms of execution time and Mean Squared Error (MSE) with other conventional methods: the geometric method, the symbolic method, and the finite difference method. The results showed a significant improvement in the computing time for calculating the Jacobian of the robotic model compared to the other methods, as well as a minimum MSE having as reference the numerical value of the symbolic calculations.

On differentiability and mass distributions of typical bivariate copulas

Despite the fact that copulas are commonly considered as analytically smooth/regular objects, derivatives of copulas have to be handled with care. Triggered by a recently published result characterizing multivariate copulas via (d−1)-increasingness of their partial derivative we study the bivariate setting in detail and show that the set of non-differentiability points of a copula may be quite large. We first construct examples of copulas C whose first partial derivative ∂1C(x,y) is pathological in the sense that for almost every x∈(0,1) it does not exist on a dense subset of y∈(0,1), and then show that the family of these copulas is dense. Since in commonly considered subfamilies more regularity might be typical, we then focus on bivariate Extreme Value copulas (EVCs) and show that a topologically typical EVC is not absolutely continuous but has degenerated discrete component, implying that in this class typically ∂1C(x,y) exists in full (0,1)2.Considering that regularity of copulas is closely related to their mass distributions we then study mass distributions of topologically typical copulas and prove the surprising fact that topologically typical bivariate copulas are mutually completely dependent with full support. Furthermore, we use the characterization of EVCs in terms of their associated Pickands dependence measures ϑ on [0,1], show that regularity of ϑ carries over to the corresponding EVC and prove that the subfamily of all EVCs whose absolutely continuous, discrete and singular component has full support is dense in the class of all EVCs.

Resolution‐Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

ABSTRACTThis article proposes the design of the resolution‐dependent variance‐constrained state estimation (RDVCSE) algorithm for a class of time‐varying nonlinear coupled complex networks (TVNCCNs) with stochastic communication and correlated noises. Specifically, a continuous‐differentiable nonlinear function with bounded first partial derivative is considered during the exchange among different coupled units and a resolution‐limited model is taken into account to embody the limited data‐processing capabilities of sensors. In order to describe the principle of random allocation in engineering, a stochastic strategy is employed in the sensor/estimator shared channel. An augmented RDVCSE method is developed such that the error covariance upper bound of state estimation (ECUBSE) can be guaranteed and obtained first. Then, the estimator parameter can be concretized via optimizing the trace of ECUBSE. In addition, a sufficient criterion is provided to verify the uniform boundedness of the presented RDVCSE algorithm. Finally, a comparative simulation is carried out to illustrate the validity of the introduced RDVCSE algorithm.

Parameterization of a Fluctuating Charge Model for Complexes Containing 3d Transition Metals.

Metalloproteins widely exist in biology, playing pivotal roles in diverse life processes. Meanwhile, molecular dynamics (MD) simulations based on classical force fields has emerged as an important tool in scientific research. Partial charges are critical parameters within classical force fields and usually derived from quantum mechanical (QM) calculations. However, QM calculations are often time-consuming and prone to basis set dependence. Alternatively, fluctuating charge (FQ) models offer another avenue for partial charge derivation, which has significant speed advantages and can be used for large-scale screening. Building upon our previous work, which introduced an FQ model for zinc-containing complexes, herein we extend this model to include additional 3d transition metals which are important to the life sciences, namely chromium, manganese, iron, cobalt, and nickel. Employing CM5 charges as target for parametrization, our FQ model accurately reproduces partial charges for 3d metal complexes featuring biologically relevant ligands. Furthermore, by using atomic charges derived by our FQ model, MD simulations have been performed. These charges showed excellent performance in simulating proteomic metal sites housing multiple metal ions, specifically, a metalloprotein containing an iron-sulfur cluster and another containing a dimanganese metal site, showcasing comparable performance to those of RESP charges. We anticipate that our study can accelerate the parametrization of atomic charges for metalloproteins featuring 3d transition metals, thereby facilitating simulations of relevant systems.

Local minima in Newton's aerodynamic problem and inequalities between norms of partial derivatives

The problem considered first by Newton (1687) [1] consists in finding a surface of the minimal frontal resistance in a parallel flow of non-interacting point particles. The standard formulation assumes that the surface is convex with a given convex base Ω and a bounded altitude. Newton found the solution for surfaces of revolution. Without this assumption the problem is still unsolved, although many important results have been obtained in the last decades. We consider the problem to characterize the domains Ω for which the flat surface gives a local minimum. We show that this problem can be reduced to an inequality between L2-norms of partial derivatives for bivariate concave functions on a convex domain that vanish on the boundary. Can the ratio between those norms be arbitrarily large? The answer depends on the geometry of the domain. A complete criterion is derived, which also solves the local minimality problem.

Physics-informed neural networks for efficient aeroacoustic computational based on two time sequence prediction models

Physics-informed neural networks (PINNs) have been proven powerful for solving partial differential equations (PDEs). However, conventional PINNs fail handling time/space variables in the training data distinctly and overlook the temporal relationships among data points, which is critical for accurately modeling dynamic systems. To address this issue, this work proposes two novel PINN models based on the transformer and the convolutional long short-term memory (ConvLSTM) architectures, respectively. An encoder-decoder neural network structure is adopted to enhance handling of time-space variables and a high-order finite-difference method is used to compute partial derivatives. These computations are implemented using the convolution within the PyTorch framework that incorporates both physical and mathematical information into the neural networks. The efficacy of the proposed methods has been evaluated for the wave equation and the Reynolds-Averaged Navier-Stokes equations. Results suggest that the transformer-based PINN outperforms both the conventional and ConvLSTM-based PINN in terms of accuracy, efficiency and generalizability.

Vectorial spatial differentiation of optical beams with metal–dielectric multilayers enabled by spin Hall effect of light and resonant reflection zero

We theoretically describe and numerically investigate the operation of “vectorial” optical differentiation of a three-dimensional light beam, which consists in simultaneous computation of two partial derivatives of the incident beam profile with respect to two spatial coordinates in different transverse electric field components. It is implemented upon reflection of the beam from a layered structure by simultaneously utilizing the effect of optical resonance and the spin Hall effect of light. As an example of a layered structure performing this operation, we propose a three-layer metal–dielectric–metal (MDM) structure. We show that by choosing the parameters of the MDM structure, it is possible to achieve the so-called isotropic vectorial differentiation, for which the intensity of the reflected optical beam (squared electric field magnitude) is proportional to the squared absolute value of the gradient of the incident linearly polarized beam. The presented numerical simulation results demonstrate high-quality vectorial differentiation and confirm the developed theoretical description.

A new approach to the directional derivative of fractional order

The fractional derivative approximations offer many approaches to understanding real‐world problems. The conformable fractional derivative operator that is one of the fractional derivative operators has recently attracted a lot of interesting. In this paper, a new approach to the directional derivative obtained with the help of the conformal fractional derivative is presented. Considering this approach, the definition of the fractional partial derivative is reformulated. In addition, a new definition of fractional gradient, fractional curl, and fractional divergence is given, and the properties of these new concepts are examined.

Analysis of a Combined Spherical Harmonics and Discontinuous Galerkin Discretization for the Boltzmann Transport Equation

Abstract In [L. Bourhrara, A new numerical method for solving the Boltzmann transport equation using the PN method and the discontinuous finite elements on unstructured and curved meshes, J. Comput. Phys. 397 2019, Article ID 108801], a numerical scheme based on a combined spherical harmonics and discontinuous Galerkin finite element method for the resolution of the Boltzmann transport equation is proposed. One of its features is that a streamline weight is added to the test function to obtain the variational formulation. In the present paper, restricting our attention to the advective part of the Boltzmann equation, we prove the convergence and provide error estimates of this numerical scheme. To this end, the original variational formulation is restated in a broken functional space. The use of broken functional spaces enables to build a conforming approximation, that is the finite element space is a subspace of the broken functional space. The setting of a conforming approximation simplifies the numerical analysis, in particular the error estimates, for which a Céa’s type lemma and standard interpolation estimates are sufficient for our analysis. For our numerical scheme, based on ℙ k {\mathbb{P}^{k}} discontinuous Galerkin finite elements (in space) on a mesh of size h and a spherical harmonics approximation of order N (in the angular variable), the convergence rate is of order 𝒪 ⁢ ( N - t + h k ) {\mathcal{O}(N^{-t}+h^{k})} for a smooth solution which admits partial derivatives of order k + 1 {k+1} and t with respect to the spatial and angular variables, respectively. For k = 0 {k=0} (piecewise constant finite elements) we also obtain a convergence result of order 𝒪 ⁢ ( N - t + h 1 2 ) {\mathcal{O}(N^{-t}+h^{\frac{1}{2}})} . Numerical experiments in one, two and three dimensions are provided, showing a better convergence behavior for the L 2 {L^{2}} -norm, typically of one more order, 𝒪 ⁢ ( N - t + h k + 1 ) {\mathcal{O}(N^{-t}+h^{k+1})} .

Multifingered Grasp Planning Based on Gaussian Process Implicit Surface and its Partial Differentials

Multifingered Grasp Planning Based on Gaussian Process Implicit Surface and its Partial Differentials

Unsteady-State Relative Permeability Curves Derived From Saturation Data and Partial Derivatives Using Magnetic Resonance Imaging

Unsteady-State Relative Permeability Curves Derived From Saturation Data and Partial Derivatives Using Magnetic Resonance Imaging

Optimized dead-zone inverse control using reinforcement learning and sliding-mode mechanism for a class of high-order nonlinear systems

An optimized control method is developed for a class of high-order nonlinear dynamic systems having controller dead-zone phenomenon. Dead-zone refers to the controller with zero behavior within a certain range, so it will inevitably affect system performance. In order to make the optimized control eliminate the effect of dead zone, the adaptive dead-zone inverse and reinforcement learning (RL) techniques are combined. The main idea is to find the desired optimized control using RL as the input of dead-zone inverse function and then to design the adaptive algorithm to estimate the unknown parameters of dead-zone inverse function, so that the competent system control can be yielded from the dead-zone function. However, most existing RL algorithms are difficult to apply in the dead zone inverse methods because of the algorithm complexity. The proposed RL greatly simplifies the algorithm because it derives the training rules from the negative gradient of a simple positive function yielded by the partial derivative of Hamilton–Jacobi-Bellman (HJB) equation. Meanwhile, the proposed dead-zone inverse method also requires fewer adaptive parameters. Finally, the proposed control is attested through theoretical proofs and simulation examples.

Математична модель оптимального планування виробничого процесу

In the first half of the last century, due to the increasing complexity of production processes, there was a need for their more efficient organization. During this period, the foundations of mathematical modeling of economic processes were laid. Modern mathematical models of optimal planning integrate artificial intelligence, machine learning methods and large databases, take into account uncertainty and risks in production processes using stochastic dependencies and probability theory methods in the models. This makes it possible to model even more complex systems and take into account more factors, such as fluctuations in demand, changes in production supply chains, etc. Today, optimal planning models are the basis of enterprise management systems (ERP) and are used in various industries: from manufacturing to logistics and energy. This article considers an economic-mathematical model for planning the optimal production process under certain assumptions about the economic state. That is, the profit of the enterprise, which can produce different types of products, for each type of these products depends on the economic state of the country. It is determined what share in the total production of the enterprise will be occupied by a certain type of product in order to obtain maximum profit. The profit of the enterprise depends on the state of the economy, therefore the expected profit is characterized by the mathematical expectation of profit. For an optimal production plan, you need to strive for the best ratio between expected profit and risk (mean square deviation), that is, it is necessary to find the maximum of the function that characterizes this ratio and is a function of n unknowns. To find the extremum of this function, we find partial derivatives and obtain n nonlinear equations with n unknowns. Performing some transformations, we reduce this system to a system of n linear equations. If the non-negativity conditions are not imposed on the variables, then when solving the system, it may happen that some variables will take negative values. This means that in order to obtain optimal profit, it is not recommended to manufacture the corresponding type of products