Standard Test Cases Research Articles

Virtual Generator-Storage Pairing for Robust Multistage Decisions in Power Systems

In this article, we present a robust optimization-based framework for day-ahead reliability assessment (RA) and real-time dispatch to ensure power system reliability under multistage renewable uncertainty. Our framework jointly considers fossil-fuel generators and energy storage in the system. To obtain near-optimal performance in grid reliability, we develop robust multistage decisions based on the concept of “safe-dispatch sets” in the literature. Although such safe-dispatch sets directly answer the RA and real-time dispatch problem, their computation often incurs high computational complexity. To develop low-complexity algorithms, we adopt a divide-and-conquer paradigm. First, we derive the conditions to describe the safe-dispatch sets for a pair of energy storage and generator. The results from this simple building block provide us with useful insights on how the grid reliability is related to the parameters, e.g., the generators’ ramp speeds and the storage capacity. To tackle the general multibus scenario, the uncertainty is split among virtual generator-storage pairs (VGSPs), where we can utilize the results from the one-bus building block. Numerical studies on a standard IEEE 30-bus test case illustrate that our proposed solution requires much lower storage capacity to ensure system reliability compared to state-of-the-art approaches, without renewable curtailment.

Generic Modal Design Variables for Efficient Aerodynamic Optimization

Orthogonal modes are an effective method for aerodynamic shape optimization due to their excellent design space compactness; however, all existing methods are generated from a database of representative geometry. Moreover, application to high-fidelity design spaces is not possible because high-frequency shape components are insufficiently bounded, leading to nonsmooth and oscillatory geometries. In this work, a new generic methodology for generating orthogonal shape modes is presented based on a purely geometric derivation, eliminating the need for geometric training data. The new method is a further development of the gradient-limiting method developed previously for constraining the design space in a geometrically meaningful way to reduce the effective degrees of freedom and improve optimization convergence rate and final result. Here, the gradient-limiting methodology is reformulated by transforming the constraints directly onto design variables to produce orthogonal shape modes with equivalent constraints for ensuring smooth and valid iterates. The new generic methodology requires no training data, can be applied to arbitrary topologies using different boundary conditions, and naturally includes translational modes as part of the orthogonal basis. When applied to two standard aerodynamic test cases, the new method has superior performance compared to library-derived modes. Importantly, the optimization convergence rate is independent of the number of design variables, and the optimized objective at high design fidelities is greatly improved by avoiding local minima corresponding to spurious geometries. A nonstandard test case is demonstrated, for which traditional library modes are not useable due to nontrivial topology, and it is shown to benefit from the high-fidelity design space and translational mode made possible with the novel methodology.

Simulation of interfacial deformations for 2D axisymmetric multi-material flows

A numerical method is developed to model the deformation of interfaces between various materials in a multi-material system. The method involves a new and straightforward interface reconstruction scheme integrated with the volume-of-fluid (VOF) technique capable of addressing triple-point configurations. The developed method can track various interfacial structures between liquids in liquid-liquid-gas systems. First, a few standard test cases are used to demonstrate the capabilities of the numerical scheme and examine its performance. Next, the impact of a water drop onto a pool of silicone oil is simulated. Experiments were also performed to obtain time-resolved images of this phenomenon for comparison and validation purposes. Computer simulations well agree with those of the experiments.

High-Order Semi-Lagrangian Schemes for the Transport Equation on Icosahedron Spherical Grids

The transport process is an important part of the research of fluid dynamics, especially when it comes to tracer advection in the atmosphere or ocean dynamics. In this paper, a series of high-order semi-Lagrangian methods for the transport process on the sphere are considered. The methods are formulated entirely in three-dimensional Cartesian coordinates, thus avoiding any apparent artificial singularities associated with surface-based coordinate systems. The underlying idea of the semi-Lagrangian method is to find the value of the field/tracer at the departure point through interpolating the values of its surrounding grid points to the departure point. The implementation of the semi-Lagrangian method is divided into the following two main procedures: finding the departure point by integrating the characteristic equation backward and then interpolate on the departure point. In the first procedure, three methods are utilized to solve the characteristic equation for the locations of departure points, including the commonly used midpoint-rule method and two explicit high-order Runge–Kutta (RK) methods. In the second one, for interpolation, four new methods are presented, including (1) linear interpolation; (2) polynomial fitting based on the least square method; (3) global radial basis function stencils (RBFs), and (4) local RBFs. For the latter two interpolation methods, we find that it is crucial to select an optimal value for the shape parameter of the basis function. A Gauss hill advection case is used to compare and contrast the methods in terms of their accuracy, and conservation properties. In addition, the proposed method is applied to standard test cases, which include solid body rotation, shear deformation of twin slotted cylinders, and the evolution of a moving vortex. It demonstrates that the proposed method could simulate all test cases with reasonable accuracy and efficiency.

Lung SBRT credentialing in the Canadian OCOG-LUSTRE randomized trial

PurposeTo report on the Stereotactic Body Radiation Therapy (SBRT) credentialing experience during the Phase III Ontario Clinical Oncology Group (OCOG) LUSTRE trial for stage I non-small cell lung cancer. MethodsThree credentialing requirements were required in this process: (a) An institutional technical survey; (b) IROC (Imaging and Radiation Oncology Core) thoracic phantom end-to-end test; and (c) Contouring and completion of standardized test cases using SBRT for one central and one peripheral lung cancer, compared against the host institution as the standard. The main hypotheses were that unacceptable variation would exist particularly in OAR definition across all centres, and that institutions with limited experience in SBRT would be more likely to violate per-protocol guidelines. ResultsFifteen Canadian centres participated of which 8 were new, and 7 were previously established (≥2 years SBRT experience), and all successfully completed surveys and IROC phantom testing. Of 30 SBRT test plans, 10 required replanning due to major deviations, with no differences in violations between new and established centres (p = 0.61). Mean contouring errors were highest for brachial plexus in the central (C) case (12.55 ± 6.62 mm), and vessels in the peripheral (P) case (13.01 ± 12.55 mm), with the proximal bronchial tree (PBT) (2.82 ± 0.78 C, 3.27 ± 1.06 P) as another variable structure. Mean dice coefficients were lowest for plexus (0.37 ± 0.2 C, 0.37 ± 0.14 P), PBT (0.77 ± 0.06 C, 0.75 ± 0.09 P), vessels (0.69 ± 0.29 C, 0.64 ± 0.31 P), and esophagus (0.74 ± 0.04 C, 0.76 ± 0.04 P). All plans passed per-protocol planning target volume (PTV) coverage and maximum/volumetric organs-at-risk constraints, although variations existed in dose gradients within and outside the target. ConclusionsClear differences exist in both contouring and planning with lung SBRT, regardless of centre experience. Such an exercise is important for studies that rely on high precision radiotherapy, and to ensure that implications on trial quality and outcomes are as optimal as possible.

Ensembles of realistic power distribution networks

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

An all-speed formulation using a modified γ-model for the prediction of boundary layer transition and heat transfer

• Effect of Mach number and temperature is added in the modified γ -model to find the transition onset for all speed flows. • Algebraic correlation is derived for the scaling constant of F onset1 based on the Mach number and temperature ratio. • Compressibility correction is added to the R e θ c correlation. • Effect of inlet viscosity ratio on transition and an existing correlation for it is identified to use with modified γ -model . Menter’s one-equation transition model, known to predict all types of boundary layer transition in low-speed flows, does not include the effect of Mach number and temperature, predominant at high speeds. This work uses an algebraic correlation based on a self-similar solution of compressible boundary layer equations to predict the transition onset location at all speed regimes ranging from incompressible to hypersonic speeds at different temperature ratios. Since Menter’s model is highly susceptible to inflow conditions (viscosity ratio and turbulence intensity), the modified model uses correlations related to the experimental conditions for inlet turbulence quantities. A two-dimensional in-house solver based on the finite volume method is developed for this study and validated with standard test cases (flat plate, ramp) in different speed regimes for the turbulence model and a low-speed test case for Menter’s transition model. Transition location is predicted from the heat transfer rate for high-speed flows and skin friction co-efficient for low-speed flows. Numerical results obtained from Menter’s and modified model for various test cases are compared against the experimental and DNS data. Computational results show that modified γ -model could predict the transition onset location more accurately than Menter’s model in all speed regimes.

Development of electrical power transmission system linear hybrid state estimator based on circuit analysis techniques

Electrical power systems are in rapid evolution, necessitating advanced monitoring, control, and protection aspects connected to an accurate knowledge of the state of the grid. State estimation is one-way such knowledge could be achieved by estimating voltage magnitudes and phase angles at all buses in electrical networks. Electric utilities are deploying advanced Phasor Measurement Units (PMUs) in grids with conventional Remote Terminal Units (RTUs). Integrating measurements from the two types of devices for state estimation is inevitable. In the past, hybrid state estimators have been based on nonlinear models associated with various challenges, such as using nonlinear state estimation algorithms with high computational time, slow convergence, and initialization problems. For this reason, there is a need to develop hybrid linear mathematical models that eliminate issues posed by nonlinearity models. This paper presents a novel mathematical formulation that gives a linear measurement model for hybrid state estimation based on the standard steady-state branch model. The developed model is then tested in MATLAB using three standard transmission test cases (IEEE 14-BUS, IEEE 30-BUS, and IEEE 57-BUS) with available measurements subjected to different errors. Notably, the performance of the developed model based on two different state estimation algorithms, Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV), are compared. The simulation results obtained for voltage magnitudes and phase angles for all buses in the three scenarios compare well with reference values. Using Normalized Cumulative Error (NCE) as a performance index, the developed linear measurement model gives less cumulative error than the conventional nonlinear model. With measurements subjected to bad data (such as negative values for measured variables), the developed model performs better using WLAV than the WLS estimation algorithm. The computational time for all three networks is notably less when using the WLAV algorithm than the WLS algorithm.

Consistent and flexible thermodynamics in atmospheric models using internal energy as a thermodynamic potential. Part I: Equilibrium regime

Approximations in the moist thermodynamics of atmospheric models can often be inconsistent; different parts of numerical models may handle the thermodynamics in different ways, or the approximations may disagree with the laws of thermodynamics. To address these problems, all relevant thermodynamic quantities may be derived from a defined thermodynamic potential; approximations are instead made to the potential itself—this guarantees self‐consistency, as well as flexibility. Previous work showed that this concept is viable for vapour and liquid water mixtures in a moist atmospheric system using the Gibbs potential. However, on extension to include the ice phase, an ambiguity is encountered at the triple point. To resolve this ambiguity, here the internal energy is used instead. Constrained maximisation methods on the entropy can then be used to solve for the system equilibrium state. Nevertheless, a further extension is necessary for realistic atmospheric systems, where many importantnon‐equilibriumprocesses take place; for example, freezing of supercooled water and evaporation into subsaturated air. To capture processes such as these fully, the equilibrium method must be reformulated to involve finite rates of approach towards equilibrium. Here the principles of non‐equilibrium thermodynamics are used, beginning with a set of phenomenological equations, to show how non‐equilibrium moist processes may be coupled to a semi‐implicit semi‐Lagrangian dynamical core. Standard bubble test cases and simulations of idealised cloudy thermals are presented to demonstrate the viability of the approach for the equilibrium regime. Further details and results for non‐equilibrium regimes are presented in Part II.

Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation

We present a multilevel quantum–classical intermediate representation (IR) that enables an optimizing, retargetable compiler for available quantum languages. Our work builds upon the multilevel intermediate representation (MLIR) framework and leverages its unique progressive lowering capabilities to map quantum languages to the low-level virtual machine (LLVM) machine-level IR. We provide both quantum and classical optimizations via the MLIR pattern rewriting subsystem and standard LLVM optimization passes, and demonstrate the programmability, compilation, and execution of our approach via standard benchmarks and test cases. In comparison to other standalone language and compiler efforts available today, our work results in compile times that are 1,000× faster than standard Pythonic approaches, and 5–10× faster than comparative standalone quantum language compilers. Our compiler provides quantum resource optimizations via standard programming patterns that result in a 10× reduction in entangling operations, a common source of program noise. We see this work as a vehicle for rapid quantum compiler prototyping.

A novel approach to radially global gyrokinetic simulation using the flux-tube code stella

A novel approach to global gyrokinetic simulation is implemented in the flux-tube code stella. This is done by using a subsidiary expansion of the gyrokinetic equation in the perpendicular scale length of the turbulence, originally derived by Parra and Barnes (2015) [23], which allows the use of Fourier basis functions while enabling the effect of radial profile variation to be included in a perturbative way. Radial variation of the magnetic geometry is included by utilizing a global extension of the Grad-Shafranov equation and the Miller equilibrium equations which is obtained through Taylor expansion. Radial boundary conditions that employ multiple flux-tube simulations are also developed, serving as a more physically motivated replacement to the conventional Dirichlet radial boundary conditions that are used in global simulation. It is shown that these new boundary conditions eliminate much of the numerical artefacts generated near the radial boundary when expressing a non-periodic function using a spectral basis. We then benchmark the new approach both linearly and non-linearly using a number of standard test cases.

Large deflection analysis of functionally graded beams based on geometrically exact three-dimensional beam theory and isogeometric analysis

The main aim of this study is to develop a numerical model based on Isogeometric Analysis to investigate the large deflection response of curved functionally graded beams. In this regard, an exact three-dimensional beam theory is employed to model the spatial behavior of the beams. The beam theory has been considered as an up-to-date one, which effectively fulfills the requirements large deflection representation, objectively, and geometrical exactness. The C1-continuity requirement of interpolation functions to develop the beam elements is treated naturally and efficiently by adopting the Isogeometric Analysis approach. To validate the accuracy and efficiency of the proposed approach, five standard benchmark test cases are conducted. In additions, various numerical examples are presented to study the influence of material variations on the behavior of curved functionally graded beam under different loading conditions.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioritized and two methods namely, graph and impact-based methods, are studied. The impact-based method requires extra initial computational efforts but thereafter provides better computational throughput than the graph-based method. The DOF is illustrated on a modified standard distribution test case with several DERs. The results from the test case demonstrate that the DOF based volt-var optimization results in consistently better performance than the state-of-the-art volt-var control.

A Posteriori Estimator for the Adaptive Solution of a Quasi-Static Fracture Phase-Field Model with Irreversibility Constraints

Within this article, we develop a residual type a posteriori error estimator for a time discrete quasi-static phase-field fracture model. Particular emphasize is given to the robustness of the error estimator for the variational inequality governing the phase-field evolution with respect to the phase-field regularization parameter $\epsilon$. The article concludes with numerical examples highlighting the performance of the proposed a posteriori error estimators on three standard test cases; the single edge notched tension and shear test as well as the L-shaped panel test.

Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games

Departure time choice models play a crucial role in determining the traffic load in transportation systems. Most studies that consider departure time user equilibrium (DTUE) problems make assumptions on the user characteristics (e.g., distribution of desired arrival time and trip length) or dynamic traffic model (e.g., classic bathtub or point queue models) in order to analyze the problem. This paper relaxes these assumptions and introduces a new framework to model and analyze the DTUE problem based on the so-called mean field games (MFGs) theory. MFGs allow us to define players at the microscopic level similar to classical game theory models, translating the effect of players’ decisions to macroscopic models. In this paper, we first present a continuous departure time choice model and investigate the equilibria of the system. Specifically, we demonstrate the existence of the equilibrium and characterize the DTUE. Then, a discrete approximation of the system is provided based on deterministic differential game models to numerically obtain the equilibrium of the system. To examine the efficiency of the proposed model, we compare it with the departure time choice models in the literature. We apply our framework to a standard test case and observe that the solutions obtained based on our model are 5.6% better in terms of relative cost compared with the solutions determined based on previous studies. Moreover, our proposed model converges with fewer iterations than the reference solution method in the literature. Finally, the model is scaled up to the real test case corresponding to the whole Lyon metropolis with a real demand pattern. The results show that the proposed framework is able to tackle a much larger test case than usual to include multiple preferred travel times and heterogeneous trip lengths more accurately than existing models.

K-kL TÜRBÜLANS MODELİNİN UYGULAMASI, DOĞRULAMASI VE GiRDAP YAKALAMA YETENEKLERİNİN DEĞERLENDİRİLMESİ

This study presents the first results of a new turbulence model implementation in our compressible finite volume CFD solver. The k - kL turbulence model is one of the newest two-equation models, and it is based on the ideas of Rotta’s two-equation model. Various research groups progressively develop the model, and it is maturing rapidly. Reports suggest that the k - kL turbulence model provides superior results compared to the other two-equation turbulence models in specific problems. The improved solutions are observed mainly for the flows with high adverse pressure gradients, the blunt-body wakes and jet interactions. We have implemented the k - kL model (with the standard designation of k-kL-MEAH2015) in our solver, and we are testing it rigorously. This paper presents our results on standard turbulence test cases: subsonic flat plate and subsonic wall-mounted bump. The results compare well with the reference study previously presented and published by model developers. The design of the k - kL model prevents excessive production of turbulence and dissipation; hence it preserves vortices significantly better than the other two-equation models. The implemented model is also tested with a transonic fin trailing vortex case to support this statement. Results show that the k-kL model yields considerably better results than the SST turbulence model in cases including vortices.

A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization

The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with various industrial applications. This paper proposes a discrete cuckoo search algorithm based on random walk and cluster analysis to solve the traveling salesman problem (TSP). Although the original cuckoo search algorithm is not suitable to solve the TSP, this algorithm is modified to solve the TSP. Lévy flights and random preference walk in the original cuckoo search algorithm are replaced with novel tools, including the local adjustment operator and discrete random walk. The former is utilized to preserve superior solutions found by the algorithm, while the latter is employed to maintain the diversity of the population. For large-scale TSP problems, the k-means algorithm is first utilized to divide cities into k categories for optimization, while random algorithms are adopted to combine them. A simple 2-opt operator is utilized as the local optimization operator to accelerate the algorithm's convergence rate. Multiple groups of standard TSPLIB datasets are chosen to compare the proposed method with state-of-the-art optimization algorithms that also use the 2-opt/k-opt optimization operator. According to the experimental results, the stability and accuracy of the proposed algorithm are superior to the state-of-the-art optimization algorithms. Regarding the solution accuracy, the algorithm improves 0.39%, 0.29%, 0.84%, 0.4%, 0.07% and 2.22% in the optimal solution over discrete cuckoo search (DCS), discrete bat algorithm (DBA), discrete sine–cosine algorithm (DSCA), random-key cuckoo search (RKCS) and discrete spider monkey optimization (DSMO) on the standard test cases, and 0.58%, 0.19%, 0.85%, 0.43%, 0.36% and 5.83% in the average optimal solution. Regarding stability, the variance of the optimal solutions of RKCS, DSMO, and novel discrete differential evolution (NDEE) for 30 independent runs is 38.5, 52.6, and 59.4 times higher than that of the algorithm in this paper. Finally, the proposed algorithm optimizes air knife migration during glass cutting. Accordingly, the path for air knife migration is reduced by 8556 mm.

Restoring the conservativity of characteristic-based segregated models: Application to the hybrid lattice Boltzmann method

A general methodology is introduced to build conservative numerical models for fluid simulations based on segregated schemes, where mass, momentum, and energy equations are solved by different methods. It is especially designed here for developing new numerical discretizations of the total energy equation and adapted to a thermal coupling with the lattice Boltzmann method (LBM). The proposed methodology is based on a linear equivalence with standard discretizations of the entropy equation, which, as a characteristic variable of the Euler system, allows efficiently decoupling the energy equation with the LBM. To this extent, any LBM scheme is equivalently written under a finite-volume formulation involving fluxes, which are further included in the total energy equation as numerical corrections. The viscous heat production is implicitly considered thanks to the knowledge of the LBM momentum flux. Three models are subsequently derived: a first-order upwind, a Lax–Wendroff, and a third-order Godunov-type schemes. They are assessed on standard academic test cases: a Couette flow, entropy spot and vortex convections, a Sod shock tube, several two-dimensional Riemann problems, and a shock–vortex interaction. Three key features are then exhibited: (1) the models are conservative by construction, recovering correct jump relations across shock waves; (2) the stability and accuracy of entropy modes can be explicitly controlled; and (3) the low dissipation of the LBM for isentropic phenomena is preserved.

Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer

In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes.