Solver Configuration Research Articles

Analysis of Tesla CyberTruck Speed on the Velocity and Pressure Distribution Using SimFlow Software

This work presents a comprehensive Computational Fluid Dynamics (CFD) analysis of the external flow around the Tesla CyberTruck, focusing on its aerodynamic characteristics under varying conditions. The primary objectives are to study velocity and pressure distributions. The simulation considers two independent parameters: vehicle speed (20, 40, and 80 m/s) and the type of turbulent flow (k-ω SST and k-ε). The simulation provides insights into complex flow patterns through meticulous meshing, boundary condition setup, and solver configuration, highlighting areas of interest such as flow separation, recirculation, and turbulence. Parametric variations are analyzed to determine how turbulent flow type and speed affect critical parameters like pressure and velocity. The results of this CFD analysis offer valuable information about the vehicle's aerodynamic performance, contributing to design optimization, handling and stability enhancements, and improved fuel efficiency. The findings from this study are expected to enhance visualization and understanding of the aerodynamic aspects of the Tesla CyberTruck.

CFD based design gas rotameters: Dynamic mesh transient simulation

Rotameters are variable-area flowmeters widely used in various industries. In this paper, a comprehensive discussion has been presented on simulation of transient behavior of a rotameter using Computational Fluid Dynamics (CFD) technique. Various methods of dynamic meshing and the reasons behind choosing the diffusion-based model have been discussed. 2D and 3D simulations were compared, and in the 2D simulation, triangular and quadratic meshes were compared, and their orthogonal quality changes due to dynamic mesh methods are shown. It was illustrated that triangular mesh cells preserve their orthogonal quality far more effectively. Moreover, the CFD simulation results were validated by comparing them with experimental data obtained from a real rotameter and it was found that the 3D simulation results are more consistent with experimental data than the 2D simulation results. A difference of less than 4% has been observed between the simulation and experimental results, indicating a reasonable agreement. The CFD solver configuration and simulation steps presented in this paper can be used to optimize rotameter designs and improve their performance. Additionally, the simulation results can provide valuable insights into the behavior of the rotameter, allowing designers to identify the potential areas for improvement and optimization of the design, accordingly.

Automatic MILP solver configuration by learning problem similarities

Automatic MILP solver configuration by learning problem similarities

Accelerating finite element modeling of heat sinks with parallel processing using FEniCSx

When designing machine elements and electronics, a detailed heat conduction analysis gives valuable insight into volumetric strains, displacements, and stress because they depend on temperature. Thermal problems involving complex geometries and boundary conditions take a long time to solve due to the unavailability of high-performance computing due to license restrictions. This study demonstrates a complete open-source framework to reduce the computational time required to perform these steady-state analyses. The work illustrates steady heat conduction in two brackets and a circuit with a heat sink. The Python-based open-source package FEniCSx is used to formulate the problem. The open-source solvers used are from PETSc and Gmsh acts as the mesh generator. The program was executed in parallel across four processors using preconditioned Krylov solvers, reducing computation time and memory consumption by 92%. Higher-order function spaces give more precise solutions but are more computationally intensive and take more time. Parallel processing and Krylov solvers can reduce the time required for higher-order function spaces while providing accurate results. To demonstrate the efficacy of the suggested approach, the time it took to solve for each order of the function space for various geometries in different solver configurations was recorded and presented. In the case of order 5, the proposed framework is 137 times faster than the conventional approach using only direct solvers.

SMT solving for the validation of B and Event-B models

ProB provides a constraint solver for the B-method written in Prolog and can make use of different backends based on SAT and SMT solving. One such backend translates B and Event-B operators to SMT-LIB using the Z3 solver. This translation uses quantifiers to axiomatize some operators, which are not well-handled by Z3. Several relational constraints such as the transitive closure are not supported by this translation. In this article, we substantially improve the translation to SMT-LIB by employing a more constructive rather than axiomatized style using Z3’s lambda function. Thereby, we are able both to translate more B and Event-B operators to SMT-LIB and improve the overall performance. We further extend ProB’s interface to Z3 to run different solver configurations in parallel. In addition, we present a direct implementation of SMT solving in Prolog using ProB’s constraint solver as a theory solver. We hereby aim to combine the strengths of conflict-driven clause learning for identifying contradictions with ProB’s constraint solver for finding solutions. We deem this implementation to be worthwhile since ProB’s constraint solver is tailored toward solving B and Event-B constraints, and we herewith avoid the dependency on an external SMT solver. Empirical results show that the new integration of Z3 has improved performance of constraint solving and enables to solve several constraints which cannot be solved by ProB’s constraint solver. Furthermore, the direct implementation of SMT solving in ProB shows benefits compared to ProB’s constraint solver and the integration of Z3.

Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio

Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, mathrm {MIPVerify} and mathrm {Venus}, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively.

Blade-resolved numerical simulations of the NREL offshore 5 MW baseline wind turbine in full scale: A study of proper solver configuration and discretization strategies

This paper presents a blade-resolved numerical investigation of the NREL 5 MW baseline wind turbine for offshore applications including blade-tower interference, analyzing the solver configuration and its influence on the results accuracy and computational costs. The wind turbine was analyzed considering its full dimensions under the operating condition of optimal wind-power conversion efficiency for a wind speed of 10 m/s at hub height. The power production, generated thrust, and forces distribution along the blade span were estimated. The computational analyses were carried out using a Computational Fluid Dynamics (CFD) methodology employing the Finite Volume Method (FVM) implemented in the OpenFOAM software considering different approaches of the Pressure Implicit Split Operator (PISO) solver and different mesh refinement strategies for the spatial discretization process, which resulted in two different meshes being investigated. For one of the meshes, a temporal discretization analysis was performed for three different CFL numbers. The iterative form of the PISO algorithm was considered in its generic form and with an extra step to correct the pressure before the beginning of the iterative process in each time step. Both approaches can be accomplished in OpenFOAM through the PIMPLE solver facilities for the treatment of the pressure-velocity coupling in unsteady problems. The analysis of the transient incompressible turbulent flow was conducted considering the same turbulence model for all CFD investigations, the URANS k − ω SST. A numerical verification was conducted in each analysis by comparing the CFD results against values obtained using the blade element momentum theory, implemented in OpenFAST. To conclude each analysis, a computational cost investigation was carried out. Finally, for the spatial and temporal discretization investigation, detailed information regarding the flow characteristics is presented. According to the accuracy of the results obtained through the CFD simulations, the best numerical arrangement is given by the iterative PISO with face flux correction as pressure-based solver and a temporal discretization which employs lower values of CFL, such as 1 or 2.

Asynchronous task based Eulerian-Lagrangian parallel solver for combustion applications

Multiphysics applications often require the use of intimately coupled solvers. The application studied here makes use of an Eulerian solver to model fluid flow and combustion and a Lagrangian solver to model spray droplets. These are then implemented within one code to solve gas turbine combustion problems. However, large scale simulations where the flow and spray are within the same computational process can be expensive as the parallel solution does not scale well due to the poor load balancing of the spray particles. This is overcome by an asynchronous task-based Eulerian-Lagrangian (ATEL) approach where separate computational processes are used so that each solver can use an appropriate technique to partition the problem. Previously, this has been shown to overcome the load balancing problem but was restricted to a single computational node where shared memory could be used to transfer data. This work expands the methodology to work on large scale HPC facilities using a combination of shared memory and high speed interconnect to transfer data. The parallel methodology exploits one-sided shared memory communication when the corresponding processes are located within a computer node, otherwise it falls back to a conventional pair of send/receive. Also an hierarchical partitioning procedure is proposed that ensures that groups of parallel subdomains with high connectivity are placed on a compute node. Results are shown for two combustor cases: the DLR generic single sector combustor with an injection process that resembles a prefilming airblast atomiser which is found in many modern civil aircraft engines and a bluff-body swirl burner with a single source of fuel injection resembling a pressure atomiser. Both single sector and three sector combustor configurations have been used to carry out the performance studies. All performance cases have been tested with three different solver configurations: a) base-line Eulerian-Lagrangian solver b) ATEL and c) the baseline Eulerian solver without spray. The unstructured grids varied from 7M cells to 84M cells. In all cases the ATEL solution with flow, combustion and spray scaled identically to when solving flow and combustion alone. In fact, due to the memory bandwidth limitations of multicore processors, reducing the number of cores allocated to the flow and combustion to allocate some cores for the spray, hardly affected the computational speed of the flow solution, and due to the overlap of the spray calculation meant that the coupled Eulerian-Lagrangian solution could be achieved at almost no cost penalty to the Eulerian on its own. The choice of how to split the cores across the two solvers was considered by proposing a simple model to estimate the cost of each solver. Timing measurements show, that for the cases considered, the overall computational time is only weakly sensitive to this choice.

Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea

Abstract. This paper describes Nemo-Nordic 2.0, an operational marine model for the Baltic Sea. The model is used for both near-real-time forecasts and hindcast purposes. It provides estimates of sea surface height, water temperature, salinity, and velocity, as well as sea ice concentration and thickness. The model is based on the NEMO (Nucleus for European Modelling of the Ocean) circulation model and the previous Nemo-Nordic 1.0 configuration by Hordoir et al. (2019). The most notable updates include the switch from NEMO version 3.6 to 4.0, updated model bathymetry, and revised bottom friction formulation. The model domain covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. Vertical grid resolution has been increased from 3 to 1 m in the surface layer. In addition, the numerical solver configuration has been revised to reduce artificial mixing to improve the representation of inflow events. Sea ice is modeled with the SI3 model instead of LIM3. The model is validated against sea level, water temperature, and salinity observations, as well as Baltic Sea ice chart data for a 2-year hindcast simulation (October 2014 to September 2016). Sea level root mean square deviation (RMSD) is typically within 10 cm throughout the Baltic basin. Seasonal sea surface temperature variation is well captured, although the model exhibits a negative bias of approximately −0.5 ∘C. Salinity RMSD is typically below 1.5 g kg−1. The model captures the 2014 major Baltic inflow event and its propagation to the Gotland Deep. The model assessment demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.

Boosting evolutionary algorithm configuration

Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers.

On the unusual effectiveness of type-aware operator mutations for testing SMT solvers

We propose type-aware operator mutation, a simple, but unusually effective approach for testing SMT solvers. The key idea is to mutate operators of conforming types within the seed formulas to generate well-typed mutant formulas. These mutant formulas are then used as the test cases for SMT solvers. We realized type-aware operator mutation within the OpFuzz tool and used it to stress-test Z3 and CVC4, two state-of-the-art SMT solvers. Type-aware operator mutations are unusually effective: During one year of extensive testing with OpFuzz, we reported 1092 bugs on Z3’s and CVC4’s respective GitHub issue trackers, out of which 819 unique bugs were confirmed and 685 of the confirmed bugs were fixed by the developers. The detected bugs are highly diverse — we found bugs of many different types (soundness bugs, invalid model bugs, crashes, etc.), logics and solver configurations. We have further conducted an in-depth study of the bugs found by OpFuzz. The study results show that the bugs found by OpFuzz are of high quality. Many of them affect core components of the SMT solvers’ codebases, and some required major changes for the developers to fix. Among the 819 confirmed bugs found by OpFuzz,184 were soundness bugs, the most critical bugs in SMT solvers,and 489 were in the default modes of the solvers. Notably, OpFuzz found 27 critical soundness bugs in CVC4, which has proved to be a very stable SMT solver.

Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

Biological reactor retrofitting using CFD-ASM modelling

In recent years, the interest in modelling activated sludge (AS) systems by means of Computational Fluid Dynamics (CFD) techniques has significantly increased. This work shows a successful case study combining CFD hydrodynamics and biokinetic modelling. The hydrodynamics is analysed by using the Reynolds-averaged Navier-Stokes equation for incompressible non-Newtonian fluids and SST turbulence model. Biokinetics has been included in the CFD as transport equations with source and sink terms defined by the Activated Sludge Model n°1 (ASM1). Furthermore, a strategy for reducing the computational cost while maintaining accuracy of the results of these calculations has been proposed. This strategy is based on a two-step solver configuration and the definition of a variable timestep scheme. The resulting CFD-ASM approach permits a proper evaluation of denitrification in the anoxic tanks as well as the reproduction of nitrate and readily biodegradable substrate distributions. To demonstrate the strength of the proposed CFD-ASM, it has been used to evaluate the operation of a full-scale AS system and optimize its performance through changes in the biological reactor anoxic zone. The original configuration has been retrofitted and modified after detecting intrinsic defects in the fluid behaviour within the tank. This study has been assessed by analysing hydrodynamics in detail and validating the simulation results with tracer tests and flow velocity measurements. Substantial variations on the Residence Time Distribution have been confirmed when modifying the internal elements of the tank configuration: the wall-bushing and the stirrer positioning. As a result of this work, an influential short circuiting was corrected improving hydrodynamics and increasing mean residence time, all favouring denitrification efficiency. Outcomes of this study show the benefit of CFD when applied to AS tanks.

Solver Composition Across the PDE/Linear Algebra Barrier

The efficient solution of discretisations of coupled systems of partial differential equations (PDEs) is at the core of much of numerical simulation. Significant effort has been expended on scalable algorithms to precondition Krylov iterations for the linear systems that arise. With few exceptions, the reported numerical implementation of such solution strategies is specific to a particular model setup, and intimately ties the solver strategy to the discretisation and PDE, especially when the preconditioner requires auxiliary operators. In this paper, we present recent improvements in the Firedrake finite element library that allow for straightforward development of the building blocks of extensible, composable preconditioners that decouple the solver from the model formulation. Our implementation extends the algebraic composability of linear solvers offered by the PETSc library by augmenting operators, and hence preconditioners, with the ability to provide any necessary auxiliary operators. Rather than specifying up front the full solver configuration, tied to the model, solvers can be developed independently of model formulation and configured at runtime. We illustrate with examples from incompressible fluids and temperature-driven convection.

On the impact of configuration on abstract argumentation automated reasoning

In this paper we consider the impact of configuration of abstract argumentation reasoners both when using a single solver and choosing combinations of framework representation–solver options; and also when composing portfolios of algorithms.To exemplify the impact of the framework–solver configuration we consider one of the most configurable solvers, namely ArgSemSAT—runner-up of the last competition on computational models of argumentation (ICCMA-15)—for enumerating preferred extensions. We discuss how to configure the representation of the argumentation framework in the input file and show how this coupled framework–solver configuration can have a remarkable impact on performance.As to the impact of configuring differently structured portfolios of abstract argumentation solvers, we consider the solvers submitted to ICCMA-15, which provided the community with a heterogeneous panorama of approaches for handling abstract argumentation frameworks. A superficial reading of the results of ICCMA-15 is that reduction-based systems (either SAT-based or ASP-based) are always the most efficient. Our investigation, concerning the enumeration of stable and preferred extensions, shows that this is not true in full generality and suggests the areas where the relatively under-developed non-reduction-based systems should focus more to improve their performance. Moreover, it also highlights that the state-of-the-art solvers are very complementary and can be successfully combined in portfolios.

Coupled Multiphysics Problems as Market Place for Competing Autonomous Software Agents

A novel approach is presented applying capabilities of software agent-based programming to numerical field computations. Distributed and autonomous calculation resources are used in analogy to a machine-to-machine system application for setting up and solving coupled simulations respecting the individual machine capabilities. Within the presented system, the involved machines interact cooperatively and comparatively. No central unit takes any influence to the solution process. Redundant resources do automatically and proactively add variance to the solution process by modifying their partial tasks based on negotiations between the involved resources. In that way, different numerical methods, solution strategies, and solver configurations are automatically evaluated as examples of the variety within coupled simulations. Partial results are dynamically integrated into an overall solver sequence, demonstrated on the coupled electric field computation of a patch antenna array. A quick provision of results is shown contemplating changes at calculation resource and vague user decisions for a nonlinear and bidirectionally coupled simulation of a 3-D multiphysics problem.

3D FINITE ELEMENT METHOD MODELING AND SIMULATION OF THE TEMPERATURE OF CRYSTALLINE PHOTOVOLTAIC MODULE

The temperature reached by solar cells/modules during operation has been simulated to have information about the temperature distribution in the solar cell/module. A finite element (FE) model of the solar cell/module was created in COMSOL Multiphysics environment. The simulations enable visualization of the temperature distribution in crystalline silicon solar cell. The effect of different encapsulating materials, Ethylene-vinyl Acetate (EVA) and silicone on cell temperature are also investigated. An unwanted side-effect of the encapsulation of solar cells into a PV module is that the encapsulation alters the heat flow into and out of the PV module, thereby increasing the operating temperature of the PV module. These increases in temperature have a major impact on the PV module by reducing its voltage, thereby lowering the output power. A finite element (FE) model representing a real crystalline silicon solar cell/module in terms of size, mechanical and material properties is created. An in-depth study of the FE software used, COMSOL Multiphysics is done in other to get the best output in terms of solver configuration and memory considerations. Four different geometries of the solar cell/module are created for these simulations; each representing a stage or variant in the solar module assembly. At first, the models are validated comparing results from simulations such as the effect of changes in solar irradiance, wind velocity and incident angle of solar irradiance on temperature with those in literature. The effect of the type of protective cover used in the encapsulation, either glass-glass or glass-Tedlar on cell temperature is analyzed. The thickness of the protective cover layers and its optical properties are also investigated based on their effects on cell/module temperature. All the simulations leading to the conclusions drawn about the cell/module temperature and its distribution were performed by varying wind velocity, irradiance, material sizes and incident angle among other model modifications. The color maps and graphs are presented in this study.