Benchmark Problems Research Articles

Modeling Hydraulic Fracture Entering Stress Barrier: Theory and Practical Recommendations

Numerical modeling of hydraulic fracturing is complicated when a fracture reaches a stress barrier. For high barriers, it may require changing the computational scheme. In view of the strong influence of stress barriers on the final footprint and opening of a hydraulic fracture, for decades, their modeling has been the subject of special investigations. Actually, classical models of propagation within a pay layer with impenetrable boundaries referred to the case of extremely high-stress contrast in neighbor layers. Further improvements tended to account for the fracture growth in these layers by including stress contrasts as input parameters of a model. This tendency resulted in the suggestion and successive enhancements of pseudo-three-dimensional models. All of them have used stress intensity factors (SIFs) to characterize the combined resistance caused by stress contrasts, material strength, and fluid viscosity. Specifically, the SIFs served to formulate the conditions that control the front penetration into a neighbor layer. This key concept presents the background of our research. Despite examples of modeling propagation through barriers, there is no general theory clarifying when and why conventional schemes may become inefficient and how to overcome computational difficulties. This paper presents the theory and practical recommendations following it. We start with the definition of the barrier intensity, which exposes that the barrier strength may change from zero for contrast-free propagation to infinity for channelized propagation. The analysis reveals two types of computational difficulties caused by spatial discretization as follows: (i) general, arising for fine grids and aggravated by a barrier, and (ii) specific, caused entirely by a strong barrier. The asymptotic approach, which avoids spatial discretization, is suggested. It is illustrated by solving benchmark problems for barriers of arbitrary intensity. The analysis distinguishes three typical stages of the fracture penetration into a barrier and provides theoretical values of the Nolte–Smith slope parameter and arrest time as functions of the barrier intensity. Special analysis establishes the accuracy and bounds of the asymptotic approach. It appears that the approach provides physically significant and accurate results for fracture penetration into high, intermediate, and even weak stress barriers. On this basis, simple practical recommendations are given for modeling hydraulic fractures in rocks with stress barriers. The recommendations may be promptly implemented in any program using spatial discretization to model fracture propagation.

A novel reinforcement learning-based method for structure optimization

With the rapid development of deep learning technology, Reinforcement Learning (RL) has garnered considerable acclaim within the realm of structural optimization owing to its excellent exploration mechanism. However, the widespread application of RL in this field is limited owing to the excessive number of iterations required to converge and the expensive computational cost it brings. To address these challenges, this article presents a novel RL framework for structural optimization, combining Monte Carlo tree search with the proximal policy optimization method, called LMPOM. The key contributions of LMPOM encompass: (1) an enhanced Monte Carlo tree search strategy for partitioning the hybrid design space; (2) a strategy for adaptively updating surrogate models to reduce simulation costs; and (3) the introduction of a novel termination condition for the RL algorithms. Through tests on three benchmark problems, compared with previous RL algorithms, LMPOM consistently shows fewer iterations and better optimization results.

Advances in ArborX to support exascale applications

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.

Stochastic Black-Box Optimization using Multi-Fidelity Score Function Estimator

Abstract Optimizing parameters of physics-based simulators is crucial in the design pro-
cess of engineering and scientific systems. This becomes particularly challenging
when the simulator is stochastic, computationally expensive, black-box and when a
high-dimensional vector of parameters needs to be optimized, as e.g. is the case
in complex climate models that involve numerous interdependent variables and
uncertain parameters. Many traditional optimization methods rely on gradient
information, which is frequently unavailable in legacy black-box codes. To address
these challenges, we present SCOUT-Nd (Stochastic Constrained Optimization for
N dimensions), a gradient-based algorithm that can be used on non-differentiable
objectives. It can be combined with natural gradients in order to further enhance
convergence properties. and it also incorporates multi-fidelity schemes and an
adaptive selection of samples in order to minimize computational effort. We vali-
date our approach using standard, benchmark problems, demonstrating its superior
performance in parameter optimization compared to existing methods. Addition-
ally, we showcase the algorithm’s efficacy in a complex real-world application, i.e.
the optimization of a wind farm layout.

Physics-informed neural networks for weakly compressible flows using Galerkin–Boltzmann formulation

In this work, we study the Galerkin–Boltzmann formulation within a physics-informed neural network (PINN) framework to solve flow problems in weakly compressible regimes. The Galerkin–Boltzmann equations are discretized with second-order Hermite polynomials in microscopic velocity space, which leads to a first-order conservation law with six equations. Reducing the output dimension makes this equation system particularly well suited for PINNs compared with the widely used D2Q9 lattice Boltzmann velocity space discretizations. We created two distinct neural networks to overcome the scale disparity between the equilibrium and non-equilibrium states in collision terms of the equations. We test the accuracy and performance of the formulation with benchmark problems and solutions for forward and inverse problems with limited data. We compared our approach with the incompressible Navier–Stokes equation and the D2Q9 formulation. We show that the Galerkin–Boltzmann formulation results in similar L2 errors in velocity predictions in a comparable training time with the Navier–Stokes equation and lower training time than the D2Q9 formulation. We also solve forward and inverse problems for a flow over a square, try to capture an accurate boundary layer, and infer the relaxation time parameter using available data from a high-fidelity solver. Our findings show the potential of utilizing the Galerkin–Boltzmann formulation in PINN for weakly compressible flow problems.

A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems

Many evolutionary multi-modal multi-objective algorithms (MMEAs) have been proposed to solve multi-modal multi-objective optimization problems (MMOPs). Unfortunately, the environmental selection process causes many algorithms to place too much emphasis on solution variety in the decision space, which leads to solutions with low convergence quality. As a result, not only are all local Pareto fronts reversed, but objective values are also far lower than the global Pareto Fronts. To tackle these tasks, this paper proposes a hierarchical clustering-based MMOEA_DC_HR model that uses decision space clustering methods to group neighborhood solutions into several local clusters, preserving local Pareto Sets. And secondary clustering is performed in the objective space to select temporary populations from these local clusters to maintain the diversity of the objective space. Additionally, a hierarchical ranking method is introduced to update the convergence archive aiding in maintaining the convergence of the algorithm and controlling the quality of the Pareto Front. The test results show that this novel algorithm exhibits competitive performance in solving selected benchmark problems when compared to other cutting-edge MMEAs.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Reliability-based topology optimization of imperfect structures considering uncertainty of load position

In this paper, a novel optimization technique is implemented to explore the effects of considering uncertain load positions. Therefore, the integration of reliability-based design into structural topology optimization, while considering imperfect geometrically nonlinear analysis, is proposed. By comparing the results obtained from perfect and imperfect geometrically and materially nonlinear analyses, this study examines the impact of nonlinearity on probabilistic and deterministic analyses. Concerning probabilistic analysis, the originality of this research lies in its incorporation of the position of the applied load as a stochastic variable. This distinctive approach complements the consideration of other relevant parameters, including volume fraction, material properties, and geometrical imperfections, with the overarching goal of capturing the variability arising from real-world conditions. For the assessment of uncertainties, normal distribution is assumed for all these parameters. Normal distributions are chosen due to their advantages in terms of simplicity, ease of implementation, and computational efficiency. These characteristics are particularly beneficial when dealing with complex optimization algorithms and extensive analyses, as is the case in our research. The proposed algorithm is validated according to the results of benchmark problems. Structural examples like cantilever beam, pinned-shell, and L-shaped beam problems are further explored within the context of imperfect geometrically nonlinear reliability-based topology optimization, with specific regard to the probabilistic aspect of the location of the externally applied loads. Moreover, the results of the suggested approach suggest that the inclusion of a probabilistic design strategy has influenced topology optimization. The reliability index acts as a controlling constraint for the resulting optimized configurations, including the mean stress values associated with the resulting topologies.

Measurement of intelligent computing via Levenberg Marquardt algorithm (LMA) for accurate prediction of fluid forces in a transient non-Newtonian thermal flow

Predicting precise results for the quantities of interest in time-dependent Computational Fluid Dynamics (CFD) simulations requires a significant investment of computational resources and time. To get around these issues, CFD simulations have been joined with Artificial Neural Networks (ANN). An optimally configured artificial neural network (ANN) is given the training and validation data sets produced by computational fluid dynamics (CFD). The flow around a cylinder, which is a well-known benchmark problem for incompressible flows, has been taken into consideration by the hybrid-CFD system. The mathematical model is based on the non-stationary Navier-Stokes and energy equations with viscosity. The basic architecture of the ANN model consists of 10 hidden layers, three output levels, and five input layers. Fast second-order LMA, a top-tier approach, was used to train the network. Both the Mean Square Error (MSE) and the coefficient of determination (R) provide statistical evidence that the ANN projected values for the drag and lift coefficients and average Nusselt number obtained from the finite element analysis are accurate. This analysis suggests that ANNs have the potential to significantly cut down on the amount of time and energy needed to run time-dependent simulations.

Unified LSPG Model Reduction Framework and Assessment for Hypersonic Computational Fluid Dynamics

A unified least-squares Petrov–Galerkin (LSPG) framework for projection-based model order reduction featuring three different approximation manifolds [affine manifold, quadratic manifold, and nonlinear manifold built using a deep artificial neural network (ANN)] is presented. Its performance was assessed for a variable-speed version of the double-cone hypersonic benchmark problem. First, a high-dimensional viscous computational fluid dynamics model (HDM) was constructed, verified, and validated. The dimensionality of the HDM was then reduced using LSPG, each of the aforementioned approximation manifolds, and a global right reduced-order basis trained in the range 8≤M∞≤13. Each resulting global projection-based reduced-order model (PROM) was hyper-reduced and transformed into a hyper-reduced PROM (HPROM). The accuracy of each constructed HPROM was assessed for various quantities of interest and contrasted with that of snapshot interpolation. For this purpose, three different error measures were considered and discussed in the context of shock-dominated problems. Wall-clock and CPU time speedup factors are reported. Overall, it was shown that using a relatively small set of training data, all constructed LSPG HPROMs were nonlinearly stable, real-time capable, and highly predictive. The LSPG HPROM constructed using a nonlinear approximation manifold and an ANN was the most computationally efficient.

Triple junction benchmark for multiphase-field models combining capillary and bulk driving forces

Abstract A benchmark problem is formulated which is well suited for the validation of mesoscopic phase-field models for grain-boundary migration in polycrystals. First, an analytical steady-state solution of the sharp moving boundary problem is derived for a symmetric lamellar structure, which is valid for arbitrary bulk driving forces and triple junction angles. Characteristic quantities are identified to reduce the parameter space which in turn allows a systematic comparison of simulations and analytical results. Various multiphase-field (MPF) formulations are compared which approximate the sharp interface problem in terms of a diffuse regularization. An interfacial thickness convergence study reveals that the model error is largely dependent on the ratio of bulk to interfacial stabilizing force as well as the underlying model formulation. An additional grid convergence study highlights the efficiency of a more advanced discretization scheme. The results can be used to guide the selection of appropriate models and to estimate the interface thickness and spatial resolution required to achieve a given accuracy target. The post-processing framework consists of a fully automated determination of well-defined metrics from the phase field simulation data, eliminating human bias and facilitating reproducibility. The corresponding code is made openly available to assist the materials science and engineering community in validating MPF, multi-order parameter and similar model developments. We believe that this work provides a reliable benchmark procedure to better understand the potentials and limitations of current multiphase-field models as well as alternative approaches.

Design and implementation of HDF5-format neutron nuclear database in RMC code

This study developed a new structured nuclear database to improve the readability and extensibility of the ACE (A Compact ENDF) nuclear database, save disk space, reduce memory usage, and enhance the computational efficiency of the Reactor Monte Carlo (RMC) code. A Python package was developed to store nuclear data in HDF5 format. Compared to the ACE database, the HDF5-format database shows significant improvements: an 80% reduction in disk space for continuous energy neutron data and a 60% reduction for neutron thermal scattering data. The HDF5-format database was implemented in RMC’s criticality calculation mode and validated through VERA benchmark problem 2B, demonstrating perfect agreement with the ACE results in keff and neutron flux counts. The Computational results indicate a 7.7% reduction in memory usage and a 20.1% improvement in computational efficiency with the HDF5-format database. Additional tests show that using the database at a single temperature point reduces memory usage by 5.4% and running time by 13.2%. At two temperature points, memory usage decreases by 35.2% and running time by 18.0%. The new data structure reduces temperature-independent redundant data and improves indexing efficiency, leading to greater savings with more temperature points. This development enhances performance of criticality calculation of RMC and addresses ACE database limitations.

Evaluating the performance of various Heuristic Algorithms to Solve Flow Shop Scheduling Problem with Fuzzy Processing Time

This paper evaluates the performance of selected heuristic algorithms, namely Palmer’s and Campbell, Dudek and Smith (CDS) algorithm, Nawaz-Enscore-Ham (NEH), and SAI algorithms for minimizing the makespan in fuzzy flow shop scheduling problem (FSSP) with triangular fuzzy processing times. The algorithms are tested on sixteen benchmark problems with size up to 10 machines and 20 jobs using a MATLAB program. Computational results show that, despite its simplicity, the FNEH algorithm can provide the best results compared to other algorithms, regardless of the size of the problem.

Democratizing uncertainty quantification

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale.In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.

Benchmark verification of PIC-DSMC programs

We examine a number of common verification and benchmark problems for Particle-in-Cell and Direct Simulation Monte Carlo codes. Since results, including convergence rates, comparison to analytic solutions, and code-to-code comparisons, for these problems are often used as evidence of correctness for simulation codes, it is necessary to understand what successful verification using one or more of these problems implies about the correctness of the simulation code. To that end, a series of benchmark problems is performed in Aleph, a PIC-DSMC code developed at Sandia National Laboratories, including both at the canonical numerical parameters and others where verification should fail. The results presented suggest that improvements and extensions to current benchmark problems and additional problem specifications would benefit existing and future codes thereby providing greater confidence in predictive results.

Analytical evaluation of the elastic stresses in a multilayer spherical pressure vessel

An explicit-form solution is constructed for a problem on elastic deformation of a multilayer spherical vessel due to uniform internal pressure. The vessel is assembled of perfectly connected elastic layers whose material properties may arbitrarily vary across their thickness. The solution is constructed through the use of the single solid approach along with the modified scheme of the direct integration method which allow for the consideration of the multilayer vessel as a composite sphere with piecewise-variable profiles of the material properties. As a result, the original problem is reduced to solving a governing integral equation of the second kind whose solution is constructed in the form of the explicit dependence on the internal pressure. The solution is verified by the comparison with exact solutions derived by making use the method of tailored solutions for specific benchmark problems. By comparing our with the one for a functionally-graded sphere, whose material properties are evaluated through the use of the micromodular homogenization technique, the specific features of the circumferential stress are analyzed.

Differential quadrature lattice Boltzmann method for simulating fluid flows

In this study, a novel method for simulating incompressible flows using the lattice Boltzmann method is presented, aiming to improve robustness. This method is enhanced by the Qadyan numerical method. In the Qadyan method, Partial differential equations are solved using semi-discrete schemes combined with the differential quadrature method. To discretize the spatial derivatives of the lattice Boltzmann equation, the upwind differential quadrature method is employed, while the first-order forward difference scheme and/or fourth-order Runge-Kutta method handle the temporal term. The decision to exclude more complex methods stems from their requirement for refined computational grids. This work focuses on achievable results with similar types of uniform grids. The proposed scheme introduces and evaluates a novel method for solving both steady and unsteady problems. The present numerical method is validated by solving five benchmark problems, including heat diffusion on a slab, Stokes’ first problem, Taylor-Green vortex flow, flow around a flat plate, and flow in a lid-driven cavity. Reasonable agreements are obtained between the solutions obtained using this method and those from analytical/other numerical approaches. The Qadyan formulation delivers more accurate results compared to the finite-difference lattice Boltzmann method, (FDLBM), without requiring an increase in the number of grids. Notably, the novel Qadyan method achieves this increased accuracy without introducing additional complexity to the standard lattice Boltzmann method or resorting to non-uniform grids. This is possible due to the nature of the differential quadrature method, which utilizes more combinations of candidate stencils. The Qadyan method has a higher computational cost in comparison with the FDLBM.

An evaluation of the hybrid Fokker–Planck-DSMC approach for high-speed rarefied gas flows

The Direct Simulation Monte Carlo (DSMC) method has been widely used for simulations of rarefied gas flows. It has been proven that the numerical solution of the DSMC method converges to the Boltzmann equation, providing a solid physical foundation for high Knudsen numbers. However, many aerospace engineering problems include both low and high-density regions in a single domain, making the DSMC method computationally expensive. Over the past decade, the particle Fokker–Planck (FP) method has been studied as a means to reduce computational costs near the continuum regime. While enhancing efficiency, the FP method loses physical accuracy at high Knudsen numbers. Aiming for universal accuracy and efficiency across the whole range of Knudsen numbers, the hybrid FP-DSMC method has been studied. Nevertheless, consistent comparisons among the DSMC, FP, and hybrid FP-DSMC methods have received limited attention so far. This paper presents a consistent comparative study of the DSMC, FP, and hybrid FP-DSMC methods to assess the accuracy and efficiency of the hybrid FP-DSMC method. The hybrid FP-DSMC solver is developed based on the open-source SPARTA framework. The benchmark problems include supersonic flow in a planar nozzle, hypersonic flow around a cylinder, and hypersonic flow around a THAAD-like missile. The results demonstrate that the hybrid FP-DSMC method can be more efficient than the DSMC method while being more accurate than the FP method. The speed-up achieved by the FP-DSMC method ranged from 1.5 to 14 times compared to the converged DSMC simulation.

An enhanced competitive swarm optimizer with strongly robust sparse operator for large-scale sparse multi-objective optimization problem

In the real world, the decision variables of large-scale sparse multi-objective problems are high-dimensional, and most Pareto optimal solutions are sparse. The balance of the algorithms is difficult to control, so it is challenging to deal with such problems in general. Therefore, An Enhanced Competitive Swarm Optimizer with Strongly Robust Sparse Operator (SR-ECSO) algorithm is proposed. Firstly, the strongly robust sparse functions which accelerate particles in the population better sparsity in decision space, are used in high-dimensional decision variables. Secondly, the diversity of sparse solutions is maintained, and the convergence balance of the algorithm is enhanced by the introduction of an adaptive random perturbation operator. Finally, the state of the particles is updated using a swarm optimizer to improve population competitiveness. To verify the proposed algorithm, we tested eight large-scale sparse benchmark problems, and the decision variables were set in three groups with 100, 500, and 1000 as examples. Experimental results show that the algorithm is promising for solving large-scale sparse optimization problems.

A numerical approach to overcome the very-low Reynolds number limitation of the artificial compressibility for incompressible flows

We propose a numerical approach to solve a long-standing challenge which is the applicability of the artificial compressibility (AC) formulation for solving the incompressible Navier—Stokes equations at very-low Reynolds numbers. A wide range of engineering applications involves very-low Reynolds number flows in Micro-ElectroMechanical Systems (MEMS) and in the fields of chemical-, agricultural- and biomedical engineering. It is known that the already existing numerical methods using the AC approach fail to provide physically correct results at very-low Reynolds numbers (Re ≤ 1). To overcome the limitation of the AC method for these engineering applications, we propose a higher-order Neumann-type pressure outflow boundary condition treatment along with their up to fourth-order numerical approximations. We found that the numerical treatment of the pressure at the outlet boundary plays the main role in overcoming the limitation of the AC method at very-low Reynolds numbers (Re << 1). Therefore, we provide numerical evidence on the accuracy of the AC method beyond its previously reported limitations, e.g., the low Reynolds number Oseen flow (Re << 1) is first presented in this work. A third-order explicit total-variation diminishing (TVD) Runge–Kutta scheme has been employed with standard finite difference spatial discretisation schemes for improving the accuracy of the numerical solution. For modelling strongly viscous flows, the Reynolds number ranges from 10−1 to 10−4. Overall, we found that the accuracy limitation of the AC method below Re < 1 can be overcome with an accurate numerical treatment of the outlet pressure boundary condition instead of using high-order schemes in the governing equations. For the investigated Reynolds number range (10−1 ≤ Re ≤ 10−4), the obtained results show that the relative errors were smaller than 1% for the numerical simulations performed on the configurations of both the two- and three-dimensional, straight microfluidic channels. The imposition of high-order derivative Neumann-type pressure outflow boundary conditions reduced the maximum relative errors of the numerical solutions from 85% and 95% to below than 1% at the outlet section of the two- and three-dimensional, straight microfluidic channel flows, respectively. Taking the advantage of the numerical approach proposed here, two- and three-dimensional benchmark problems employed in the current investigation in comparison with analytical solutions available in the literature, clearly demonstrate that the artificial compressibility can be used beyond its previously known constraints for very-low Reynolds number incompressible flows.