Standard Test Cases Research Articles

Flexibility analysis of typical roof diaphragms in steel structures using the equivalent connectors method

Steel structures are invariably analyzed and designed excluding the global stiffening effect of the roof cladding. Including this effect historically required complex design methods or deprecated standards. This paper presents a simplified numerical modeling method which allows this stiffening effect to be included. The method is an extension of the already established Component Method but utilizes only elements commonly available in typical design and analysis software. The results of the models were favorably compared to standardized test cases; an average difference of 0.24 mm maximum displacement was found, corresponding to a 3.4% discrepancy. The consistency of these results implies that the proposed model can be considered to be used for scientific and practical purposes when assessing a building’s most realistic behavior is necessary.

Optimal control strategy of photovoltaic system based autonomous micro grid for power quality improvement

<span>Due to the increasing number of distributed generation units being used in remote regions, the need for reliable and flexible operation of these systems has led to the development of microgrid technology. When a microgrid operates in an autonomous mode, it can experience unpredictable loads and other factors that can affect its operation. An optimal control mechanism is required to maintain the system's stability. Hence, in this paper optimal control strategy of autonomous microgrid for power quality improvement is presented. A test case of single phase 3.5 kW photovoltaic (PV) system based autonomous micro grid is considered. Particle swarm optimization (PSO) and Harris hawks optimization (HHO) optimal control strategies are implemented under standard test case and variable test cases. In all the cases </span><span lang="EN-IN">PV mean voltage-</span><span>Vmpv (V), </span><span>PV mean current</span><span>-</span><span>Impv (A), </span><span>RMS voltage-</span><span>Vrms (V), </span><span>RMS current-</span><span>Irms (A), </span><span>mean PV power-Ppv </span><span>(W), </span><span>autonomous grid power-</span><span>Pg (W)</span><span>, efficiency (%), total harmonic distortion (THD) (%), inverter losses (%) are evaluated. In all the cases HHO optimal control strategy for autonomous microgrid exhibits the best performance in comparison with PSO optimal control strategy. The inverter efficiency is improved, inverter losses are reduced and the THD is improved.</span>

A semi‐hybrid‐mixed method for Stokes–Brinkman–Darcy flows with H(div)‐velocity fields

SummaryWe consider new semi‐hybrid‐mixed finite element formulations for Stokes–Brinkman problems. Using ‐conforming approximate velocity fields, the continuity of normal components over element interfaces is taken for granted, and pressure is searched in discontinuous spaces preserving the divergence compatibility property. Tangential continuity is weakly imposed by a Lagrange multiplier playing the role of tangential traction. The method is strongly mass‐conservative, leading to exact divergence‐free simulations of incompressible flows. The Lagrange multiplier space requires specific choices according to the velocity approximations implemented in each element geometry. In certain cases, classic divergence‐compatible pairs adopted for Darcy flows may require divergence‐free bubble enrichment to enforce tangential continuity in some extent, avoiding any extra stabilization technique. Considerable improvement in computational performance is achieved by the application of static condensation: the global system is solved only for a piecewise constant pressure variable, velocity normal trace and tangential traction over interfaces. The remaining solution components are recovered by solving independent local Neumann problems in each element. Numerical results are presented for a set of standard test cases in the field of Stokes–Brinkman–Darcy flows with known analytical solutions. The main convergence properties of the method are verified in the whole range of parameters, from Stokes to Darcy limits, as well as for the combined Stokes–Darcy scenario. The robustness of the method is also demonstrated for more challenging test problems with complex non‐conforming meshes with local refinement patterns as well as for curved geometry.

Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm

Accurate estimation of the model parameters of a fuel cell is extremely important to study its system behavior. This paper presents a parameter identification method based on improved artificial bee colony algorithm (IABC). Inspired by the cross operation of genetic algorithm and the cross and mutation operation of differential evolution algorithm, this study improves the position update formula of ABC in the parameter optimization process, thus the convergence accuracy and speed of IABC algorithm are improved. The algorithm was used to identify 3 typical standard test cases, namely 250W FC, BCS 500W and NedStack PS6 stack, and the minimum sum of squares (SSE) error between PEMFC measured voltage data and estimated voltage data was defined as the objective function. The SSE values of the three PEMFCs reached 0.29686, 0.011718 and 2.9848, respectively. The model I/V polarization curve estimated by IABC algorithm has a high fitting degree with the experimental measured curve, which indicates the accuracy of IABC algorithm. To verify the superiority of the proposed IABC algorithm, the identification results obtained by some optimization algorithms are compared and ANOVA (analysis of variance) is performed. Results show that the IABC algorithm has faster convergence speed and better robustness.

Transport Equation Transition Modeling in Cerans For Hypersonic Flows

Scope of present work covers implementation to validation of the Langtry-Menter two equation γ-Reθt Local Correlation based Transition Model (LCTM) in CERANS code for modeling subsonic to hypersonic flow transition. The LCTM is coupled with SST k-ω turbulence model. The governing equations of γ-Reθt model is discretized in finite volume framework similar to the RANS model and implicitized using the point Jacobi method. Transition correlations based on freestream as well as local turbulence intensity and critical momentum thickness Reynolds number were integrated with the model, and the code is validated for several standard transition test cases involving low subsonic to hypersonic high enthalpy flows covering a wide range of turbulent intensities.

Developlment of a Thermo-Chemical Non-Equilibrium Solver for Weakly Ionized Hypersonic Flowfields

A three dimensional solver for numerical simulation of weakly ionized hypersonic reactive air flows past high speed flight vehicles had been indigenously developed. The two-temperature, seven species, eighteen reactions, thermo-chemical non equilibrium, ionizing, air-chemistry model of Park is implemented in a compressible viscous code CERANS and solved in the finite volume framework. The energy relaxation is addressed by a conservation equation for the vibrational-electron-electronic energy of the gas mixture resulting in the evaluation of its vibrational temperature. The contribution of vibrational-electron-electronic energy of the reactive mixture is accounted in total energy equation as well as in the source terms of the relaxation model. The AUSM-PW+ numerical flux function has been used for modeling the convective fluxes and a central differencing approximation is used for modeling the diffusive fluxes. The code has been characterized for standard test cases involving weakly ionized hypersonic reactive thermo-chemical non equilibrium flows and results obtained are in good qualitative agreement with results available in open literature.

Grid Method Based Store Separation Suite Using Paranam Mesh Free Solver

A store separation suite based on Point Prediction methodology was developed at CSIR-National Aerospace Laboratories (NAL) using PARANAM (Parallel NAL MCIR) mesh free CFD solver along with 6-DOF code and dynamic point cloud pre-processor [7,8]. This store separation suite is well validated and tested and is being used widely for various strategic store separation projects at CSIR-NAL [7,8]. However, in some scenarios point prediction based method is difficult to employ owing to either large number of trajectory studies or time and resource requirements. In such scenarios a quick but sufficiently accurate method is needed. The Grid method which is an approximate method based on aerodynamic database has been used to develop a store separation suite to achieve the same. In this work we have exploited mesh free nature of PARANAM solver to generate the aerodynamic database required for Grid method efficiently. The suite has been applied to standard Eglin test case for carrying out validation. Further we have demonstrated the utility of such a tool in parametric studies for assessing sensitivity.

Application of Weighted Least Squares Meshless Method to Incompressible Flows

Meshless methods have been successfully applied to solve a large number of inviscid and viscous compressible flows in both two and three dimensional flow fields. However, the conventional methods of solution for compressible flow equations cannot be applied to incompressible flow equations. This is due to the basic difference in the nature of the pressure variable in the compressible and incompressible flows. Pressure acts as a thermodynamic variable in case of compressible flows and can be easily updated by using the equation of state. In contrast, in case of incompressible flows, the pressure is merely responsible for enforcing the incompressibility constraint and hence cannot be updated by the equation of state. This has led to the development of numerical methods based on Helmholtz Decomposition to solve for the fluid flows in the incompressible flow regimes. Here, we have applied weighted least squares meshless method to solve for the flows in the incompressible flow regime using the projection method based on Helmholtz Decomposition. The standard test cases of two dimensional lid driven cavity flow at Reynolds numbers of 100, 400 and 1000 have been solved using the weighted least squares meshless method. A good match between the results of the test case using the meshless methods and conventional CFD methods for the solution of the incompressible flows has been obtained.

A Modular Algorithm Based on the Minimum-Cost-Path Problem for Optimizing LTC Operations in Photovoltaic Integrated Distribution Systems

This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system’s load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer’s operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods.

Data-driven stochastic AC-OPF using Gaussian process regression

At present, electricity generation is responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating significant amounts of renewable energy sources into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly intermittent and thus bring a lot of uncertainty to power grid operation and challenge existing optimization and control policies. The chance-constrained (CC) alternating current optimal power flow (AC-OPF) framework finds the minimum cost of generation dispatch, maintaining the power grid operation within security limits with a prescribed probability. Unfortunately, the AC-OPF problem’s chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and also needs additional assumptions to be made about the behavior of renewable generation probability distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach for solving the stochastic AC-OPF problem, based on Gaussian process regression (GPR) to close this gap. The Gaussian process (GP) approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is validated and illustrated using a number of standard IEEE test cases.

Discrete exterior calculus discretization of two-phase incompressible Navier-Stokes equations with a conservative phase field method

We present a discrete exterior calculus (DEC) based discretization scheme for incompressible two-phase flows. Our physically-compatible exterior calculus discretization of single phase flow is extended to simulate immiscible two-phase flows with discontinuous changes in fluid properties such as density and viscosity across the interface. The two-phase incompressible Navier-Stokes equations and conservative phase field equation for interface capturing are first transformed into the exterior calculus framework. The discrete counterpart of these smooth equations is obtained by substituting with discrete differential forms and discrete exterior calculus operators. We prove the boundedness of the method for the forward Euler and predictor-corrector time integration schemes in the DEC framework. With a proper choice of two free parameters, the scheme remains phase field bounded without the requirement of any ad hoc mass redistribution. We verify the scheme against several standard test cases (for interface capturing) comprising not only the flat domains but also the curved domains, leveraging the advantage that DEC operators are independent of the coordinate system. The results show excellent properties of boundedness, mass conservation and convergence. Moreover, we demonstrate the ability of the scheme to handle large density and viscosity ratios as well as surface tension in the simulation of various two phase flow physical phenomena on flat or curved surfaces.

Efficient high-order gradient-based reconstruction for compressible flows

This paper extends the gradient-based reconstruction approach of Chamarthi [1] to genuine high-order accuracy for inviscid test cases involving smooth flows. A seventh-order accurate scheme is derived using the same stencil as of the explicit fourth-order scheme proposed in Ref. [1], which also has low dissipation properties. The proposed method is seventh-order accurate under the assumption that the variables at the cell centres are point values. A problem-independent discontinuity detector is used to obtain high-order accuracy. Accordingly, primitive or conservative variable reconstruction is performed around regions of discontinuities, whereas smooth solution regions apply flux reconstruction. The proposed approach can still share the derivatives between the inviscid and viscous fluxes, which is the main idea behind the gradient-based reconstruction. Several standard benchmark test cases are presented. The proposed method is more efficient than the seventh-order weighted compact nonlinear scheme (WCNS) for the test cases considered in this paper.

A Micro-Macro Markov Chain Monte Carlo Method for Molecular Dynamics using Reaction Coordinate Proposals

We introduce a new micro-macro Markov chain Monte Carlo method to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic (slow) dynamics of some low-dimensional set of reaction coordinates. The algorithm enhances exploration of the state space in the presence of metastability by allowing larger proposed moves at the macroscopic level, on which a conditional accept-reject procedure is applied. Only when the macroscopic proposal is accepted, is the full microscopic state reconstructed from the newly sampled reaction coordinate value and is subjected to a second accept/reject procedure. The computational gain stems from the fact that most proposals are rejected at the macroscopic level, at low computational cost, while microscopic states, once reconstructed, are almost always accepted. We analytically show convergence and discuss the rate of convergence of the proposed algorithm, and numerically illustrate its efficiency on two standard molecular test cases.

A mixed finite-element discretisation of the shallow-water equations

Abstract. This paper introduces a mixed finite-element shallow-water model on the sphere. The mixed finite-element approach is used as it has been shown to be both accurate and highly scalable for parallel architecture. Key features of the model are an iterated semi-implicit time-stepping scheme, a finite-volume transport scheme, and the cubed sphere grid. The model is tested on a number of standard spherical shallow-water test cases. Results show that the model produces similar results to other shallow-water models in the literature.

Investigation of Aeroelasticity Effect on Missile Separation from the Internal Bay

There is a strong aerodynamic interference when launching the missile in the embedded mode. During the separation process, the carrier aircraft safety may be threatened due to large slenderness ratio, low structural stiffness, and aeroelasticity effects of the missile. The present study simulates missile separation in the presence of the aeroelasticity effects based on the computational fluid dynamics (CFD), rigid body dynamics (RBD), and computational structure dynamics (CSD) coupling method. A hybrid dynamic grid method consisting of the mixed overset unstructured grid and deformation grid is utilized. In order to verify the accuracy of the coupled numerical method, store separation from a wing and AGARD 445.6 wing flutter are first simulated as two standard test cases. The verification results imply that the present coupled numerical method is reliable and capable in simulation of the aeroelastic effect in missile separation. The influence of aeroelasticity on the separation trajectory of a missile from the internal bay is systematically studied at different states. Numerical results show that aeroelasticity substantially affects the missile angular displacement, while it has a slight impact on the linear displacement of the center of mass. Mach number and flight altitude are two important flight parameters that characterize the aeroelasticity effect on missile separation from the internal bay.

A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem

The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t-test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem.

A Hybrid Reinforcement Learning Algorithm for 2D Irregular Packing Problems

Packing problems, also known as nesting problems or bin packing problems, are classic and popular NP-hard problems with high computational complexity. Inspired by classic reinforcement learning (RL), we established a mathematical model for two-dimensional (2D) irregular-piece packing combined with characteristics of 2D irregular pieces. An RL algorithm based on Monte Carlo learning (MC), Q-learning, and Sarsa-learning is proposed in this paper to solve a 2D irregular-piece packing problem. Additionally, mechanisms of reward–return and strategy-update based on piece packing were designed. Finally, the standard test case of irregular pieces was used for experimental testing to analyze the optimization effect of the algorithm. The experimental results show that the proposed algorithm can successfully realize packing of 2D irregular pieces. A similar or better optimization effect can be obtained compared to some classical heuristic algorithms. The proposed algorithm is an early attempt to use machine learning to solve 2D irregular packing problems. On the one hand, our hybrid RL algorithm can provide a basis for subsequent deep reinforcement learning (DRL) to solve packing problems, which has far-reaching theoretical significance. On the other hand, it has practical significance for improving the utilization rate of raw materials and broadening the application field of machine learning.

Программный комплекс NOISEtte–MCFL для расчета многокомпонентных реагирующих течений

The research software package NOISEtte–MCFL is designed to simulate multicomponent gas dynamic flows taking into account conjugate heat transfer. The NOISEtte–MCFL code is based on the developed original mathematical methodology based on the splitting algorithm for physical processes and an explicit iterative scheme based on Chebyshev polynomials. NOISEtte–MCFL is written in C++ and uses a hybrid three-level parallel structure, including MPI, OpenMP and CUDA technologies. The package is validated and verified on a set of model problems and standard test cases. Comparison with the results of calculations using the commercial ANSYS code and the open source OpenFOAM software is performed.

Flexural analysis of I-section beams functionally graded materials

This paper aims to present the flexural behavior of thin functionally graded (FG) I-beams. The characteristics of metal-ceramic materials are defined using a power-law function dependent on volume fraction. The bending-torsion equations for this problem are derived based on Vlasov's theory for thin-walled beams and the principle of minimum total potential energy. All geometric properties are expressed according to the functional graded power index law. A nonlinear algebraic system is obtained, and the deflection of the structure is numerically derived. To confirm the precision and effectiveness of the suggested method, a standard benchmark test case is implemented, that concerns the flexural analysis of an FGM I-section beam according to power index and aspect ratio parameters.

Improving the accuracy of discretisations of the vector transport equation on the lowest-order quadrilateral Raviart-Thomas finite elements

Within finite element models of fluids, vector-valued fields such as velocity or momentum variables are commonly discretised using the Raviart-Thomas elements. However, when using the lowest-order quadrilateral Raviart-Thomas elements, standard finite element discretisations of the vector transport equation typically have a low order of spatial accuracy. This paper describes two schemes that improve the accuracy of transporting such vector-valued fields on two-dimensional curved manifolds.The first scheme that is presented reconstructs the transported field in a higher-order function space, where the transport equation is then solved. The second scheme applies a mixed finite element formulation to the vector transport equation, simultaneously solving for the transported field and its vorticity. An approach to stabilising this mixed vector-vorticity formulation is presented that uses a Streamline Upwind Petrov-Galerkin (SUPG) method. These schemes are then demonstrated, along with their accuracy properties, through some numerical tests. Two new test cases are used to assess the transport of vector-valued fields on curved manifolds, solving the vector transport equation in isolation. The improvement of the schemes is also shown through two standard test cases for rotating shallow-water models.