Benchmark Case Research Articles

Social Learning with Actor–Critic for dynamic grasping of underwater robots via digital twins

Dynamic grasping is a crucial technology in the field of underwater robotics, playing an essential role in executing complex tasks. However, traditional control methods encounter challenges in achieving an efficient and stable dynamic grasping process due to the complexities and uncertainties of underwater environments. This paper introduces a novel black-box intelligent optimization algorithm named Social Learning with Actor–Critic (SLAC) for the dynamic grasping of underwater robots. The core architecture of SLAC is based on the integration of two key algorithms: Intelligent Social Learning (ISL) for intelligent optimization and Soft Actor–Critic (SAC) for reinforcement learning. ISL enhances SAC by supplying a larger number of transitions, whereas SAC improves ISL with more effective strategies. These algorithms interact synergistically, augmenting their respective strengths throughout the learning process. To evaluate SLAC’s performance, a comparison is made with six state-of-the-art methods across eight continuous control benchmark cases. The results highlight SLAC’s exceptional learning capability and performance benefits. Furthermore, virtual reality software for the underwater robot and a corresponding digital twin system have been developed. The SLAC algorithm is trained in the digital twin environment before its application in the actual underwater setting. Through interactive training and iterative learning, both simulated and experimental results demonstrate the robot’s proficiency in achieving efficient and stable dynamic grasping, effectively adapting to various underwater environments’ variations and complexities.

Essentially non-hourglass SPH elastic dynamics

More than two decades ago, the numerical instabilities of particle clustering and non-physical fractures encountered in the simulations of typical elastic dynamics problems using updated Lagrangian smoothed particle hydrodynamics (ULSPH) was identified as tensile instability. Despite continuous efforts in the past, a satisfactory resolution for the simulations of these problems has remained elusive. In this paper, the concept of hourglass modes, other than tensile instability, is first explored for the discretization of shear force, arguing that the former may actually lead to the numerical instabilities in these simulations. Based on such concept, we present an essentially non-hourglass formulation by utilizing the Laplacian operator which is widely used in fluid simulations. Together with the dual-criteria time stepping, adopted into the simulation of solids for the first time to significantly enhance computational efficiency, a comprehensive set of challenging benchmark cases is used to showcase that our method achieves accurate and stable SPH elastic dynamics.

Modelling urban canopy with object-based porosity model

We present a novel approach to modelling urban canopies based on the porosity model. The impact of objects in the canopy layer is implicitly accounted for by applying a high-value drag force in the parts of the canopy layer that objects would occupy. This effectively halts fluid flow in the defined space of the objects, mimicking the impact of solid objects in the canopy layer. The model addresses two common issues in urban canopy modelling: the definition of the drag coefficient and the loss of canopy morphology, which significantly affect flow and turbulence fields. The model is assessed for three cases: two idealised urban-like canopies with realistic urban flow conditions and one benchmark case based on the measurements of Hilderman and Chong (2007). The benchmark results, where the explicitly modelled objects are reduced from 109 to 9, show that the new model exhibits a level of accuracy similar to explicit modelling. We also computed the commonly used zone-based porosity and roughness models for the two idealised urban-like canopies. The new model produced results comparable to the results of explicit modelling and superior to those of the alternative approaches. All cases were computed in unsteady mode using a hybrid RANS/LES model based on elliptic-relaxation eddy-viscosity RANS and the dynamic Smagorinsky model. The simulations were performed using the open-source computational fluid dynamics code T-Flows, available on Github.

Knowledge-Dominated and Data-Driven Rigid-Flexible Coupling Dynamics for Rotating Flexible Structure

Solving a rigid–flexible coupling nonlinear dynamic equation necessitates updating dynamic quantities in each time step, which is the main factor that hampers computational efficiency. Therefore, this study develops an innovative framework that combines knowledge-based and data-driven solutions, for which a data-driven numerical integration (DDNI) method is established to update the mass matrix and load vector. The DDNI model is obtained from offline training based on a deep neural network model and is embedded in the floating frame of reference formulation for online simulation of rigid–flexible coupling problems. As benchmark cases, planar and curved shell structures are analyzed, and the results show that the DDNI method exhibits excellent performance in terms of accuracy and adaptability. In particular, compared to a fully numeric integration (FNI) scheme, the DDNI method shows significantly improved efficiency, surpassing that of commercial software. These noteworthy characteristics aid the investigation of intricate structures in engineering problems. We utilize the developed DDNI method in a rigid–flexible coupling analysis of a manipulator, which shows that it is highly time-efficient, approximately 20 times faster than the FNI scheme, while producing results that are consistent with those of experiments.

Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates

This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy–Riemann equations into machine learning architectures. By incorporating Cauchy integrals and boundary conditions, the neural network successfully learns to numerically produce matrix kernel factorization for Wiener–Hopf analytical models. To validate and demonstrate the approach, a benchmark case of wave scattering from parallel hard–soft plates is studied by comparing the machine learning results with the available analytical solutions. The proposed MINN approach could provide a new route to extensively enhance the theoretical modeling capability for several wave scattering and fluid mechanics problems. The code can be found at https://github.com/lscapku/MINN.

A mid-range approximation method assisted by trust region strategy for aerodynamic shape optimization

This paper presents an efficient solution for high-fidelity large-scale aerodynamic shape optimization problems based on several developments in the mid-range approximation method within a trust region optimization framework. The mid-range approximation method is an iterative optimization technique that utilizes mid-range approximations to replace the physical experiments or simulations during the optimization based on the selected trust region strategy. It could transform the original optimization problem into a sequence of approximate sub-optimization problems. In this work, an improved trust region strategy is proposed to contain more optimization states with a flexible and controllable performance to suit different types of problems. A metamodel assembly technique and its gradient-enhanced version are developed to further relax the requirements of computational costs in the mid-range approximation method. Its performance is discussed through a detailed comparison of metamodel performance using a mathematical benchmark case named Vanderplaats scalable beam. The wing only of the Common Research Model is offered to the proposed method for aerodynamic shape optimization. The optimization has 1 design objective, 232 design variables, and 135 design constraints. With all constraints satisfied, the optimized configuration has a 4.85% improvement in wing drag performance. The shock region is greatly reduced and the wing pressure distribution is smooth and nearly parallel. These results show that the proposed method could achieve the design goal successfully within a reasonable computational cost for large-scale problems.

Numerical investigation on thermal-hydraulic performance of leeward cut annular finned tubes in crossflow heat exchangers: An insight on fin material saving

The objective of the current study is to analyse the hydro-thermal characteristics of leeward cut fins with various trimming values and finalise a specific fin that benefits by maximum material saving and negligible compromise in heat transfer. The leeward section of a fin is cut in two ways: angular and chordwise. For angular cutting, four different angles (15°, 30°, 60°, and 90°) are considered, while for chordwise cutting, a fin is trimmed along a chord present at D/3, 2D/3, and D mm from the vertical diameter. The spacing between fins is varied (2–4 mm). For turbulence modelling, the transition SST model is implemented. Streamlines reveal that the intensity of a vortex formed downstream of the tube depends on the fin-trimming value. For a higher fin cutoff, a lower pressure drop is obtained. The heat transfer rate analysis reveals that angular cutoffs of 15° (8.3 % material saving) and 30° (16.6 % material saving) have 2.5 %–3.1 % less heat transfer than the standard case. Chordwise cut at D gives 1.1 % less heat transfer but saves only 2.7 % fin material than the standard case of no cutoff. Z/E factor, a ratio of heat transfer for a unit temperature drop to power provided, is used to determine hydro-thermal performance. The value of the Z/E ratio is the highest for the benchmark case. However, for angular cutoffs of 15° and 30°, the Z/E factor is less by 4 and 6.7 %, respectively, than the standard case. Examining the outcomes of this study, we recommend using an angular cutoff of 30° or lower to get significant material savings for an equivalent heat transfer.

Application of 1D model for overland flow simulations on 2D complex domains

High computational demand limits the applications of two-dimensional (2D) shallow water equation models for high resolution overland flow simulations, while one-dimensional (1D) models can achieve higher computing efficiency. This study applied a 1D hydrodynamic model to surrogate 2D models for overland flow simulations on complex 2D domains. With one dimension reduced, the surrogate model simulation would have acceptable accuracy with much higher computing efficiency. The surrogating is fulfilled through mimicking 2D models in mesh generations, so that the 1D channel network is generated in such a way that it geometrically covers the whole domain without overlapping and intersection, and hydrologically follows the steepest slopes. Several benchmark cases on 2D domains in both laboratory and field scales with complex geometry, where no 1D models have been ever applied, are used to compare the 1D and 2D model simulations. The comparisons demonstrate that the 1D model does have potentials to efficiently simulate overland flow on 2D complex domains with accuracy comparable to 2D models.

Mixing-phase model for shear-induced contractive/dilative effects in unsteady water-sediment mixture flows

Among the geophysical surface processes, mud and debris flows show one of the most complex and challenging behaviour for scientists and modellers. These flows consist of highly-unsteady gravity-driven movements of water-sediment mixtures with non-Newtonian rheology where the solid concentration could be about 40%–80% of the flow volume and which occur along steep and irregular terrains. Furthermore, the appearance of dynamic pressures in the fluid filling the intergranular pores increases the complexity and dominates the behaviour of the fluidized water-sediment material, leading to the appearance of significant density gradients during the movement. The dynamic pressure in the pore-fluid changes the effective normal stress within the mobilized material, affecting the frictional shear stress between grains and leading to the solid phase dilation/contraction. This must be properly accounted for when developing realistic models for water-sediment surface flows. In this work, a novel physically-based approach for modelling multi-grain dense-packed water-sediment flows is presented. A novel closure formulation for the pressure distribution within the pore-fluid during the movement of dense-packed water-sediment materials has been derived. This closure allows to relate the appearance of shear-induced dynamic pore pressures to the contractive/dilative behaviour of the solid aggregate. The resultant system of depth-averaged conservation laws includes continuity of the density-variable water-sediment material and the different solid classes transported in the flow, as well as the linear momentum equation for the fluidized bulk material, and it is solved using a well-balanced fully-coupled Finite Volume (FV) method. The resultant simulation tool is faced to synthetic, laboratory and real-scale benchmark cases to test its robustness and accuracy. The presence of dynamic pore pressures within the pore-fluid leads to the appearance of a deviatoric contribution to the solid flux, which causes the shear-induced separation of the solid and liquid phases and sustains the flow mobility for long distances, as it has been observed in real mud and debris events.

Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.

Benchmark Outcomes in Deceased Donor Kidney Transplantation: A Multicenter Analysis of 80 996 Transplants From 126 Centers.

We defined clinically relevant benchmark values in deceased donor kidney transplantation (KT), to assess the best achievable results in low-risk patient cohorts from experienced centers. We identified the "ideal" cases from the United Network for Organ Sharing Standard Transplant Analysis and Research files from centers performing ≥50 KT per year between 2010 and 2018. Cases have been selected based on the kidney donor profile index values (<35%), a cold ischemia time (CIT) ≤18 h, a HLA mismatch ≤4, and excluding blood group (ABO) incompatible, dual and combined transplants. The outcomes of the benchmark cohort have been compared with a group of patients excluded from the benchmark cohort because but not meeting 1 or more of the abovementioned criteria. The 171 424 KT patients in the United Network for Organ Sharing Standard Transplant Analysis and Research files were screened and 8694 benchmark cases of a total of 80 996 KT (10.7%) from 126 centers meeting the selection criteria were identified. The benchmarks for 1-, 3-, and 5-y patient survival are ≥97%, ≥92.5%, and ≥86.7%, and ≥95.4%, ≥87.8%, and ≥79.6% for graft survival. Benchmark cutoff for hospital length of stay is ≤5 d, ≤23.6% for delayed graft function, and ≤7.5% and ≤9.1% for 6-mo and 1-y incidence of acute rejection. Overall 1-, 3-, and 5-y actuarial graft survivals were 96.6%, 91.1%, and 84.2% versus 93.5%, 85.4%, and 75.5% in the benchmark and comparison groups, respectively (P < 0.001). Overall 1-, 3-, and 5-y actuarial patient survivals were 98.1%, 94.8%, and 90.0% versus 96.6%, 91.1%, and 83.0% in the benchmark and comparison groups, respectively (P < 0.001). For the first time, we quantified the best achievable postoperative results in an ideal scenario in deceased donor KT, aimed at improving the clinical practice guided by the comparison of center performances with the ideal outcomes defined.

Implementation of a VoF solver with phase change for the simulation of internal cavitation and droplet breakup in injectors

This study presents the implementation and derivation of a Volume of Fluid (VoF) solver capable of taking into account pressure driven phase change and handle three fluids. The implemented code is designed to investigate internal nozzle cavitation and subsequent injection and jet breakup. An extended cavitation model is also implemented that can handle phase change in the presence of a third phase. Detailed description and derivation of the mathematical system is presented. The solver is validated on a one-dimensional benchmark case from the literature and verified on an experimentally well-documented geometry. The verification shows the developing cavitation regime is well captured by comparing velocity profiles and flow images with experimental measurements and visualisation.

Research on Genetic Algorithm Optimization with Fusion Tabu Search Strategy and Its Application in Solving Three-Dimensional Packing Problems

Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packing efficiency and utilization.The three-dimensional packing problem is an NP-hard combinatorial optimization problem in the field of modern logistics, with high computational complexity. This paper proposes an improved genetic algorithm by incorporating a fusion tabu search strategy to address this problem. The algorithm employs a three-dimensional loading mathematical model and utilizes a wall-building method under residual space constraints for stacking goods. Furthermore, adaptation of fitness variation strategy, chromosome adjustment, and tabu search algorithm are introduced to balance the algorithm’s global and local search capabilities, as well as to enhance population diversity and convergence speed. Through testing on benchmark cases such as Bischoff and Ratcliff, the improved algorithm demonstrates an average increase of over 3% in packing space utilization compared to traditional genetic algorithms and other heuristic algorithms, validating its feasibility and effectiveness. The proposed improved genetic algorithm provides new insights for solving three-dimensional packing problems and optimizing logistics loading schedules, offering promising prospects for various applications.

Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy.

Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.

A strategy of mixed tube bundle to improve anti-deposition performance and heat transfer

Ash deposition is a key issue that limits the energy utilization and safety operation of high-energy-consuming equipment in industrial applications, and the clarification of the heat and mass transfer process under ash deposition is vital. To improve the anti-deposition performance of tube bundles and mitigate heat transfer deterioration, a mixed strategy with attached small cylinders arranged behind the tube has been proposed, which overcomes the difficulties faced by irregular and extended surface tube bundles in manufacturing, cleaning, maintenance, and energy consumption. A CFD model incorporating multiple deposition mechanisms is established to investigate the dynamic deposition behavior, heat transfer characteristics, and comprehensive performance of the tube bundles. In particular, the dynamic growth process of the deposition layer on heat transfer surface in this work is achieved through a dynamic mesh smoothing method based on inverse distance weighting interpolation. Parametric studies are conducted on different arrangements and angles (α = 15° - 65°) of the attached cylinders to obtain the optimal comprehensive performance design. The results show that the attached cylinders suppress the formation and development of the wake vortex caused by the primary tube bundle, significantly enhancing the anti-deposition performance of the tube bundles. The length of vortex evolution increases and the separation point of the boundary layer extends with the angles, leading to a transition of the vortex shedding regime between tube bundles. Compared to the benchmark case, Case 1 exhibits a higher anti-deposition and heat transfer performance, where the deposition mass decreases by 43.98% and the PEC reaches the maximum value of 1.11 at α = 45°, which delivers the best comprehensive performance. The results can provide more realistic predictions for the practical design, operation, and maintenance of thermal equipment.

Optimizing integrated lot sizing and production scheduling in flexible flow line systems with energy scheme: A two level approach based on reinforcement learning

Many production environments are faced with the need to simultaneously determine the planning of lot sizing and the scheduling of production sequences while ensuring cost minimization. This issue becomes even more complex when integrating multiple energy sources with the goal of a low-carbon economy. To address this challenge, this paper proposes an integrated lot sizing and flexible flow line production scheduling model under a time-of-use pricing scheme. In addition, the model takes into account conventional grid power, on-site renewable energy sources, and an energy storage system. The associated objective function is solved adopting a two-level approach to optimize energy costs while maintaining production throughput and meeting customer demand. The implementation relies on reinforcement learning capabilities to tackle complexity issues. The proposed approach is evaluated on a benchmark case and its results are compared with those obtained with First-In-First-Out heuristic, genetic algorithm and CPLEX. These results highlight the promising aspect of the proposed approach in terms of performance.

Direct numerical simulation of three-dimensional particle-laden thermal convection using the Lattice Boltzmann Method

In thermal multiphase flows, the modulation in the heat transfer rate due to the presence of finite-size solid particles attract growing interest in recent years. This study focuses on developing a robust and efficient simulation method for particle-laden Rayleigh-Bénard convection. The present work utilizes the double distribution function-based Thermal Lattice-Boltzmann method (TLBM), which enables successful simulations of multiphase fluid-thermal interactions. The no-slip boundary of the moving solid particles is handled by the interpolated bounce-back scheme. Additionally, Galilean invariant momentum exchange and heat exchange approaches are employed for hydrodynamic force and heat transfer calculation at the solid boundaries. The accuracy of the current method is verified with several benchmark cases, including three-dimensional single-phase Rayleigh-Bénard convection, as well as the settling of hot and cold spherical particles in a three-dimensional enclosure. Furthermore, we explore the modulation of Rayleigh-Bénard flows due to the presence of freely moving finite-size particles. The simulations are conducted on a distributed memory cluster with a 3D domain decomposition technique facilitated by the MPI library. A brief discussion on the parallel performance of the simulations is provided for both particle-laden and single-phase flow scenarios. Finally, the effects of finite-size solid particles on the overall heat transfer efficiency and modulation to the flow field in three-dimensional particle-laden turbulent Rayleigh-Bénard convection are discussed. The results show that addition of solid particles results in a moderate increase in the overall Nusselt number, and this enhancement is mainly due to the increased heat flux transported by the particles.

A robust algorithm for computational floating body dynamics.

We present a non-iterative algorithm, FloatStepper, for coupling the motion of a rigid body and an incompressible fluid in computational fluid dynamics (CFD) simulations. The purpose of the algorithm is to remove the so-called added mass instability problem, which may arise when a light, floating body interacts with a heavy fluid. The idea underlying the presented coupling method is to precede every computational time step by a series of prescribed probe body motions in which the fluid response is determined, thus revealing the decomposition of the net force and torque into two components: (i) an added mass contribution proportional to the instantaneous body acceleration and (ii) all other forces and torques. The algorithm is implemented and released as an open-source extension module to the widely used CFD toolbox, OpenFOAM, as an alternative to the existing body motion solvers. The accuracy of the algorithm is investigated with several single-phase and two-phase flow benchmark cases. The benchmarks demonstrate excellent stability properties, allowing simulations even with massless bodies. They also highlight aspects of the implementation, such as the mesh motion method, where it can be improved to further enhance the flexibility and predictive capabilities of the code.

A minimalist approach to 3D photoemission orbital tomography: algorithms and data requirements

Photoemission orbital tomography provides direct access from laboratory measurements to the real-space molecular orbitals of well-ordered organic semiconductor layers. Specifically, the application of phase retrieval algorithms to photon-energy- and angle-resolved photoemission data enables the direct reconstruction of full 3D molecular orbitals without the need for simulations using density functional theory or the like. However, until now this procedure has remained challenging due to the need for densely-sampled, well-calibrated 3D photoemission patterns. Here, we present an iterative projection algorithm that completely eliminates this challenge: for the benchmark case of the pentacene frontier orbitals, we demonstrate the reconstruction of the full orbital based on a dataset containing only four simulated photoemission momentum measurements. We discuss the algorithm performance, sampling requirements with respect to the photon energy, optimal measurement strategies, and the accuracy of orbital images that can be achieved.

Gravitational wave probe of primordial black hole origin via superradiance

In this article we have used stochastic gravitational wave background as a unique probe to gain insight regarding the creation mechanism of primordial black holes. We have considered the cumulative gravitational wave background which consists of the primary part coming from the creation mechanism of the primordial black holes and the secondary part coming from the different mechanisms the primordial black holes go through. We have shown that in the presence of light or ultra light scalar bosons, superradiant instability generates the secondary part of the gravitational wave background which is the most detectable. In order to show the unique features of the cumulative background, we have consdiered the delayed vacuum decay during a first order phase transition as the origin of primordial black holes. We have shown the dependence of the features of the cumulative background, such as the mass of the relevant light scalars, peak frequencies, etc. on the transition parameters. We have also generated the cumulative background for a few benchmark cases to further illustrate our claim.