Benchmark Case Research Articles

Fully coupled finite strain consolidation analysis for soft clay treated by prefabricated horizontal drain with vacuum and heat preloading

This paper established a one-dimensional fully coupled finite strain consolidation analysis for soft clay treated by thermal prefabricated horizontal drain under vacuum and heat preloading with the consideration of thermal elastic viscoplastic (TEVP) behaviors of soft clay. Firstly, the governing equations for finite strain consolidation, heat conduction, and heat convention process have been carefully derived based on the well-established Gibson’s large strain consolidation theory and TEVP constitutive model. Secondly, the accuracy and reliability of the numerical method have been verified with benchmark cases and three physical model tests. Then, improvement of consolidation efficiency rate (IER) was defined and used to study the effect of three variables on consolidation efficiency. Last but not least, a graphical user interface (GUI) was provided to support researchers and engineers in utilizing the proposed numerical method for conducting consolidation analysis of soft soil treated in the same or similar fashion.

Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

A fourth-order kernel for improving numerical accuracy and stability in Eulerian SPH for fluids and total Lagrangian SPH for solids

The error of smoothed particle hydrodynamics (SPH) using a kernel for particle-based approximation mainly arises from smoothing and integration errors. The choice of the kernel significantly impacts numerical accuracy, stability and computational efficiency. Currently, the most popular kernels, such as B-spline, truncated Gaussian (for compact support), and Wendland kernels, have 2nd-order smoothing errors, and the Wendland kernel has become mainstream in the SPH community due to its stability and accuracy. Since the particle distribution after relaxation can achieve fast convergence of integration error with respect to support radius, it is logical to choose kernels with higher-order smoothing error to improve the numerical accuracy. In this paper, the error of the 4th-order Laguerre-Wendland kernel proposed by Litvinov et al. [1] is revisited, and another 4th-order truncated Laguerre-Gauss kernel is further analyzed and considered to replace the widely used Wendland kernel. The proposed kernel has the following three properties: One is that it avoids the pair-instability problem during the relaxation process, unlike the original truncated Gaussian kernel, and achieves much less relaxation residue than the Wendland and Laguerre-Wendland kernels; One is the truncated compact support size is the same as the non-truncated one of the Wendland kernel, which leads to both kernels' comparably computational efficiency; Another is that the truncation error of this kernel is much less than that of the Wendland kernel. Furthermore, a comprehensive set of 2D and 3D benchmark cases on Eulerian SPH for fluid dynamics and total Lagrangian SPH for solid dynamics validate the considerably improved numerical accuracy of the truncated Laguerre-Gauss kernel without introducing extra computational effort.

A family of TENOA-THINC-MOOD schemes based on diffuse-interface method for compressible multiphase flows

In this work, we develop a family of hybrid algorithms to simulate compressible multi-phase flows by solving the quasi-conservative five-equation model. The general numerical framework utilizes targeted essentially non-oscillatory schemes with adaptive dissipation (TENOA) to boost the resolution of high-frequency waves, whereas the Tangent of Hyperbola for INterface Capturing (THINC) scheme is activated near the shock and contact discontinuities as well as material interfaces to maintain their sharpness, based on a novel indicator. Also, the newly derived THINC reconstruction scheme is both simpler and better at preserving the flow symmetry. The robustness of our numerical approach is further fortified through the adaptation of a modified a posteriori multidimensional optimal order detection (MOOD) technique. Moreover, to mitigate carbuncle phenomenon encountered in two-dimensional simulations, a hybrid Riemann solver is also proposed based on the low Mach modification around shock for the Harten-Lax-van Leer contact (HLLC) approximate Riemann solver. Numerical results of the challenging one- and two-dimensional benchmark cases demonstrate the superiority of the proposed methods regarding oscillation-free, interface-sharpening, low-dissipation and robustness properties.

Three-dimensional continuum point cloud method for large deformation and its verification

This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses on the study of St. Venant Kirchhoff and compressible neo-Hookean materials. Results for various initial boundary value problems are presented, including benchmark cases involving unidirectional tension and simple shear. Subsequently, the study concludes with an analysis of a displacement-controlled simulation of a compressible neo-Hookean material, specifically a bar that is pulled to 50% of its original length and rotated 90°. The pure tension case yields a 1.5% error in displacement between computed and expected values and a combined tension and torsion loading case provides further insight into material behavior under complex loading conditions. The resulting normal axial and transverse stress-strain curves are also presented. Finally, the consistency and robustness of the proposed nonlinear numerical schemes are successfully demonstrated through various numerical experiments.

Evaluation of a Coupled CFD and Multi-Body Motion Model for Ice-Structure Interaction Simulation

The interaction of water flow, ice, and structures is common in fluvial ice processes, particularly around Ice Control Structures (ICSs) that are used to manage and prevent ice jam floods. To evaluate the effectiveness of ICSs, it is essential to understand the complex interaction between water flow, ice and the structure. Numerical modeling is a valuable tool that can facilitate such understanding. Until now, classical Eulerian mesh-based methods have not been evaluated for the simulation of ice interaction with ICS. In this paper we evaluate the capability, accuracy, and efficiency of a coupled Computational Fluid Dynamic (CFD) and multi-body motion numerical model, based on the mesh-based FLOW-3D V.2023 R1 software for simulation of ice-structure interactions in several benchmark cases. The model’s performance was compared with results from meshless-based models (performed by others) for the same laboratory test cases that were used as a reference for the comparison. To this end, simulation results from a range of dam break laboratory experiments were analyzed, encompassing varying numbers of floating objects with distinct characteristics, both in the presence and absence of ICS, and under different downstream water levels. The results show that the overall accuracy of the FLOW-3D model under various experimental conditions resulted in a RMSE of 0.0534 as opposed to an overall RMSE of 0.0599 for the meshless methods. Instabilities were observed in the FLOW-3D model for more complex phenomena that involve open boundaries and a larger number of blocks. Although the FLOW-3D model exhibited a similar computational time to the GPU-accelerated meshless-based models, constraints on the processors speed and the number of cores available for use by the processors could limit the computational time.

Comparative study of kernel- and deep learning-based proxy models for nonlinearly constrained life-cycle production optimization

This study presents a comparative study for the application of deep learning–based and kernel-based proxy models in hydrocarbon production optimization with nonlinear constraints, comparing their performance against high-fidelity simulators (HFS) in terms of computational efficiency and the quality of the results achieved. One of the proxy models employed is referred to as embed to control and observe (E2CO), a deep learning–based model. The other model is a kernel-based proxy model known as least-squares support-vector regression (LS-SVR). Both proxy models can predict production/injection outputs for each well. We use the sequential quadratic programming (SQP) method for nonlinearly constrained production optimization. Our optimization objective revolves around the net present value (NPV), while the nonlinear state constraints involve field liquid production rate (FLPR) and field water production rate (FWPR). NPV, FLPR, and FWPR are estimated using two different types of proxy models. The gradient of the objective function and the Jacobian matrix of constraints are computed analytically for LS-SVR. In contrast, the method of stochastic simplex approximated gradient (StoSAG) is employed for optimization with E2CO and HFS. The reservoir model considered in this study is a two-phase, three-dimensional reservoir characterized by channelized and heterogeneous permeability, sourced from the SPE10 benchmark case. We optimize well controls to maximize NPV in an oil-water waterflooding scenario. All proxy models can achieve optimal NPV results like those obtained through HFS but with significantly lower computational resources. Both proxy models are found to be approximately 15-fold more efficient than using HFS when the number of simulation runs is compared. Forward prediction results show that both proxy models outperform numerical simulator, and the E2CO can predict up to 2400 times faster than HFS whereas speedups gained over HFS go up to 5000-fold with LS-SVR. The training time required for E2CO is at least an order of magnitude longer than that for LS-SVR. The main contributions of the paper are (i) development of a novel methodology that uses an E2CO proxy model within a gradient-based iterative retraining proxy approach for life-cycle production optimization with nonlinear state constraints, (ii) comparison of this methodology in terms of computational cost and accuracy with an LS-SVR proxy model, and (iii) new insights into the accuracy and prediction performances of these machine learning-based proxy models for 3D oil-water systems as well as their efficiency in nonlinearly constrained production optimization for waterflooding applications.

A General Formulation of the Resonance Spectrum Expansion Self-Shielding Method

The resonance spectrum expansion (RSE) self-shielding method was recently proposed by Nagoya and Osaka universities as a powerful alternative to existing approaches. First investigations of the RSE at Polytechnique Montreal show that it can effectively replace the actual subgroup method used for production calculations in DRAGON5. The Japanese implementation of the RSE method is limited to a solution of the Boltzmann transport equation (BTE) with the method of characteristics. We are proposing a new implementation of the RSE method compatible with various types of solutions for the BTE, including the collision probability and the interface current methods. We based our validation study on a subset made up of eight Rowlands pin cell benchmark cases. The absorption rates obtained after self-shielding are compared with exact values obtained using an elastic slowing-down calculation where each resonance is modeled individually in the resolved energy domain. Validation of Rowlands benchmark with effective multiplication factor calculations was also conducted with respect of the SERPENT2 Monte Carlo code. It is shown that the RSE method is compatible with both advanced and legacy energy meshes and performs slightly better than the production subgroup methods actually used.

Modeling the effect of material heterogeneity on the thermo-mechanical behavior of concrete using mesoscale and stochastic field approaches

The adverse impact of high temperatures on concrete is a well-recognized issue that can lead to significant mechanical deterioration and structural integrity loss. Factors such as aggregate type, cement composition, temperature, duration of exposure, and moisture content can substantially influence the fire resistance of concrete. To simulate and better understand the effects arising from the heterogeneity of concrete in a fire situation, a mesoscale approach is proposed, using the Mesh Fragmentation Technique (MFT) to assess the complex thermo-mechanical behavior of concrete. The MFT introduces high aspect ratio interface elements to model crack propagation and interfacial transition zones by means of an appropriated tensile damage constitutive law. In this extended framework, a fully-coupled thermo-mechanical model is proposed. The modeling approach includes considerations of both macroscopic and mesoscopic scales, in which the coarse aggregate, mortar matrix and interfacial transition zones are represented. Besides, a stochastic distribution is assumed for the material properties to account for the lower scale heterogeneity. The main novelty proposed in this study consists in the synergy of mesoscale and stochastic approaches that are herein combined to model the effect of heterogeneity on the concurrent macro-mesoscale thermo-mechanical behavior of concrete. To validate the numerical model’s capabilities in capturing thermally induced cracks, benchmark cases and a simulation of a bending beam exposed to elevated temperatures are presented. The results demonstrate the potential of the proposed approach in predicting the behavior of concrete subjected to thermal loading and the role played by heterogeneity in the thermally induced cracking.

Bio-methanol with negative CO2 emissions from residual forestry biomass gasification: Modelling and techno-economic assessment of different process configurations

The paper presents a techno-economic comparison among five alternative process configurations for bio-methanol production from the gasification of residual forestry biomass. Process design and simulations are performed in Aspen Plus for mass and energy balance calculation, followed by preliminary sizing and economic analysis. Process schemes include a gasification island (state-of-the-art low-pressure gasification compared against a high-pressure gasifier) with syngas conditioning and compression, heat recovery, syngas composition adjustment (by CO2 capture or addition of hydrogen produced by electrolysis), methanol synthesis and purification and a heat recovery cycle for power generation. CO2 capture is performed with conventional chemical absorption in the benchmark cases, while low-temperature partial condensation of CO2 is modeled in the advanced scenario. Methanol output is 14–15 kt/y in the CO2 capture cases and 36 kt/y in the H2 addition option.Configurations with a pressurized gasifier and phase-change-based CO2 separation are the most efficient ones, with a primary energy efficiency of 50 % and a Levelized Cost of Methanol (LCOM) of 700 €/tMeOH. In comparison, LCOM increases to 730 €/tMeOH in the case with conventional capture or between 792 €/tMeOH and 831 €/tMeOH (depending on the CCS technology) if the gasification pressure is conservatively reduced to 2.5 bar. In the H2 addition scenario, LCOM increases to 821 €/tMeOH due to the significant impact of the electricity consumption for H2 production, (only partly compensated by the increased methanol production). Scenarios with CO2 capture feature negative CO2 emissions, in the range −1.64 to −1.84 tCO2eq/tMeOH, as a result of the capture and storage of biogenic CO2 (BECCS approach).

Collapse analysis of a masonry arch bridge using the applied element method

Masonry arch bridges constitute a fundamental part of the European transport network. Given their historical relevance and ongoing functional role, often under significantly higher load conditions than originally designed for, a reliable assessment of their load-bearing capacity is essential to understand whether they can guarantee adequate structural performance. To address this need, research efforts have focused on the development of computational methods capable of providing realistic simulations of the structural and collapse behavior of this kind of structures. In this context, the present paper aims to evaluate the application of the recently developed Applied Element Method (AEM) to masonry arch bridges, using the well-known Prestwood bridge (Staffordshire, UK) as a benchmark case study. The bridge was modeled using AEM and loaded until collapse simulating the actual conditions of the in situ test carried out in 1986. Results show consistency, in terms of bearing capacity and collapse mechanism, with the experimental data and previous studies that used other numerical approaches, proving the ability of the Applied Element Method to provide an accurate estimate of the collapse behavior of this kind of structures. AEM’s ability to represent collapse mechanisms involving large displacements, at a reduced computational cost, is especially useful for the design of alert and monitoring systems for structures in a damaged or pre-collapse state.

A metaheuristic algorithmic framework for solving the hybrid flow shop scheduling problem with unrelated parallel machines

The hybrid flow shop scheduling problem (HFSP), as a realistic extension of the classical flow shop scheduling problem, widely exists in real-world industrial production systems. In practice, the fact that machines are unrelated is important and cannot be neglected. This study focuses on the HFSP with unrelated parallel machines (HFSP-UPM) to minimize the makespan. To address this problem, a hybrid representation that combines single-sequence coding and full-sequence coding is developed to search for solution space that is not covered by common encoding methods. An initialization block integrating random heuristic strategies is proposed for improving the quality of the initial sparrow swarm. To improve the diversity of a sparrow swarm further, a perturbation block embedded with a set of historical best positions and an enhancement strategy are developed. A critical set based local search block is designed for high-intensity local exploitation of promising regions when necessary. Several benchmark cases from the literature as well as some randomly generated instances characterized by the distribution of real data from factory studies are employed to participate in the test. The test results reveal the effectiveness of the proposed perturbation block and local search block. Compared to state-of-the-art metaheuristic algorithms, the enhanced sparrow search algorithm (ESSA) demonstrates higher convergence accuracy when handling instances. The value of the gap between a solution found by the ESSA and a feasible solution found by the mixed-integer programming (MIP) model can reach 0.6%.

A finite volume formulation for one-dimensional Stefan problems

This paper presents a numerical formulation that combines the finite-volume method with a time-dependent boundary-fitted coordinate system to tackle the moving boundary problems for the one-dimensional (1-D) heat equation. Following the formulation process, an algorithm was created with the help of the linear algebra package Eigen to generate the data for the numerical solution. Four benchmark cases of heat conduction were investigated by this approach and compared to the analytical solution: a fixed boundary case, the liquid phase of a melting process, the solid phase of a melting process, and an ablating process. The results demonstrate the capability of the presented numerical formulation in efficiently solving the general 1-D heat problems, especially the one-phase Stefan problems, as well as the consistency of the numerical and analytical solutions. Future works can strive to extend the formulations to Stefan problems of two phases or higher dimensions. The work can then be extended to real-life applications, such as the spacecraft re-entry ablation process, and the process of iceberg melting, demonstrating its critical value in solving practical problems.

Optimal redistributive charity

AbstractThis paper analyzes optimal redistributive charitable giving in a Mirrleesian framework. The status motive behind charity, the stigma from receiving donations, and transaction costs all work to decrease the optimal marginal subsidies on redistributive charity. Because the existing empirical evidence of these effects (when available) is highly uncertain and context‐dependent, the range of possible outcomes is large, and even includes the possibility that charitable giving should be taxed rather than subsidized. In a natural benchmark case, the optimal tax treatment of charitable giving is consistent with a simple tax credit, whereas pure tax deductions are never part of the optimal policy.

Efficient trust region filter modeling strategies for computationally expensive black-box optimization

The study and application of contemporary optimization techniques considerably enhance the efficiency of chemical research and manufacturing. With the dynamic progression of modern manufacturing technologies, the emergence of numerous black-box models characterized by inaccessible mathematical formulations and high evaluation costs poses new challenges to traditional optimization methods, leading to difficulty in programming and solving. Hence, based on the trust region filter (TRF) method, we define a new framework to elevate optimization efficiency in this study for chemical systems involving computationally expensive black-box functions. Sampling size per iteration is reduced, and sampling efficiency is improved by incorporating the known data beyond the trust region to assist in constructing reduced models, which is achieved by the application of the Gaussian process. Through comparison and validation of benchmark tests and case studies, this approach demonstrates that using the Gaussian process as the reduced model can lower the number of calls to black-box functions by more than half compared to common linear and quadratic models, and convergence to first-order critical points can still be guaranteed.

An active learning approach for reliability assessment of passive systems combining polynomial chaos expansion and adaptive sampling

Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system.

Mesh antenna cable net form finding optimization by an improved NSGA-II

The tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic Algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution.

Robust statistical industrial fault monitoring: A machine learning-based distributed CCA and low frequency control charts

Over the past two decades, there has been a notable increase in the complexity and dynamism of industrial and manufacturing systems. Traditional fault detection strategies exhibit limited robustness in terms of detection accuracy, inefficiency, and poor detection outcomes. This paper presents an innovative, robust machine learning (ML) based distributed canonical correlation analysis (DCCA) and low-frequency generated univariate control charts CUSUM and EWMA using wavelet transforms (WT) to monitor industrial and manufacturing systems, specifically focusing on abnormalities and fault detection. The framework’s performance is tested using previous algorithms such as multiscale PCA and PLS, and the (CSTR) is utilized as the benchmark case application. The results illustrate that the ML-based multiscale low-frequency (DCCA) monitoring charts for fault cases exhibited an (MDR) of 0 %, (FAR) of 0.2 %, (FDR) of 100 %, precision of 99.8 %, and an F1-score of 99.9 %. This method enables robust detection and ensures complete monitoring of aberrant circumstances.

A high-order fully Lagrangian particle level-set method for dynamic surfaces

We present a fully Lagrangian particle level-set method based on high-order polynomial regression. This enables meshfree simulations of dynamic surfaces, relaxing the need for particle-mesh interpolation. Instead, we perform level-set redistancing directly on irregularly distributed particles by polynomial regression in a Newton-Lagrange basis on a set of unisolvent nodes. We demonstrate that the resulting particle closest-point (PCP) redistancing achieves high-order accuracy for 2D and 3D geometries discretized on irregular particle distributions and has better robustness against particle distortion than regression in a monomial basis. Further, we show convergence in classic level-set benchmark cases involving ill-conditioned particle distributions, and we present an example application to multi-phase flow problems involving oscillating and dividing droplets.

A coupled fully implicit method for solving drag-adjoint equations of steady incompressible flows

Numerical instability is one of the main concerns when applying adjoint-based gradient computation in aerodynamic design optimization for a variety of engineering problems. An adjoint pressure-adjoint velocity-coupled fully implicit method for solving the adjoint equations of steady incompressible flows is developed in the present work, taking the aerodynamic drag as the optimization target. The finite volume method for cell-centered, collocated variables on unstructured grids is used for spatial discretization. A stabilization term based on pressure-weighted interpolation (PWI) of the adjoint velocity is added to the adjoint continuity equation to avoid spurious oscillations of adjoint pressure. All the terms of the continuous adjoint equations are discretized implicitly, including the adjoint transpose convection (ATC) term, which is regarded as one of the major sources of numerical instability. The resulting discretized equations are integrated into one linear system, and the adjoint fields can be obtained simultaneously. To prevent the diagonal element of the coefficient matrix of the linear system from getting too small, a singular value decomposition is applied to the diagonal block of the coefficient matrix, and a limiter is applied to the diagonal elements. The condition number of the coefficient matrix may be quite large, so a parallel sparse linear solver that effectively combines direct and iterative methods through a Schur complement approach is applied to solve the linear system. Compared to the staggered iterative solver, no damping or masking is needed for the ATC term in the proposed fully implicit method. There is no need to sacrifice numerical accuracy for numerical stability. This method has been tested in several benchmark cases, and the numerical results show good numerical stability and accuracy. The present method provides a sound basis for industrial applications of adjoint-based aerodynamic optimization methods.