Efficient Simulation Research Articles

Harmonic Oscillator Staging Coordinates for Efficient Path Integral Simulations of Quantum Oscillators and Crystals.

Imaginary-time path integral (PI) is a rigorous quantum mechanical tool to compute static properties at finite temperatures. However, the stiff nature of the internal PI modes poses a sampling challenge. This is commonly tackled using staging coordinates, in which the free particle (FP) contribution of the PI action is diagonalized. We introduce novel and simple staging coordinates that diagonalize the entire action of the harmonic oscillator (HO) model, rendering it efficiently applicable to (exclusively) systems with harmonic character, such as quantum oscillators and crystals. The method is not applicable to fluids or systems with imaginary modes. Unlike FP staging, the HO staging provides a unique treatment of the centroid mode. We provide implementation schemes for PIMC and PIMD simulations in NVT ensemble. Sampling efficiency is assessed in terms of the precision and accuracy of estimating the energy and heat capacity of a one-dimensional HO and an asymmetric anharmonic oscillator (AO). In PIMC, the HO coordinates propose collective moves that perfectly sample the HO contribution, then (for AO) the residual anharmonic term is sampled using standard Metropolis method. This results in a high acceptance rate and, hence, high precision, in comparison to the FP staging. In PIMD, the HO coordinates naturally prescribe definitions for the fictitious masses, yielding equal frequencies of all modes when applied to the HO model. This allows for a substantially larger time step sizes relative to standard staging, without affecting accuracy or integrator stability. For completeness, we also present results using normal mode (NM) coordinates, based on both HO and FP models. While staging and NM coordinates show similar performance (for FP or HO), staging is computationally preferable due to its cheaper scaling with the number of beads. The simplicity and the enhanced sampling gained by the HO coordinates open avenues for efficient estimation of nuclear quantum effects in more complex systems with harmonic character, such as real molecular bonds and quantum crystals.

Kylin-V: An open-source package calculating the dynamic and spectroscopic properties of large systems.

Quantum dynamics simulation and computational spectroscopy serve as indispensable tools for the theoretical understanding of various fundamental physical and chemical processes, ranging from charge transfer to photochemical reactions. When simulating realistic systems, the primary challenge stems from the overwhelming number of degrees of freedom and the pronounced many-body correlations. Here, we present Kylin-V, an innovative quantum dynamics package designed for accurate and efficient simulations of dynamics and spectroscopic properties of vibronic Hamiltonians for molecular systems and their aggregates. Kylin-V supports various quantum dynamics and computational spectroscopy methods, such as time-dependent density matrix renormalization group and our recently proposed single-site and hierarchical mapping approaches, as well as vibrational heat-bath configuration interaction. In this paper, we introduce the methodologies implemented in Kylin-V and illustrate their performances through a diverse collection of numerical examples.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

Objective. Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently. Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation. Main results. According to the NEMA IQ phantom simulation, FAST-PET’s simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures. Significance. FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

A multiblock (MIB) finite element method for accurate and efficient blood flow simulation

We present an efficient and accurate immersed boundary (IB) finite element (FE) method for internal flow problems with complex geometries (e.g. blood flow in the vascular system). In this study, we use a voxelized flow domain (discretized with hexahedral and tetrahedral elements) instead of a box domain, which is frequently used in IB methods. We highlight the applicability and efficiency of the proposed method in generating the flow domain mesh and removing unnecessary portions of the domain, compared to the standard IB methods that rely on a full Cartesian mesh. Our approach is attractive for internal flow problems that use IB methods. The proposed method accurately satisfies the boundary conditions on the immersed object using velocity and pressure correction procedures. The velocity correction applies implicitly such that the velocity on the immersed boundary (Lagrangian points) interpolated from the corrected velocity values computed on the mesh nodes (Eulerian nodes) accurately satisfies the prescribed velocity boundary conditions. The proposed method utilizes the well-established incremental pressure correction scheme (IPCS) FE solver, and the boundary condition-enforced IB (BCE-IB) method to numerically solve the transient, incompressible Navier-Stokes (NS) flow equations. We verify the accuracy of our numerical method using the analytical solution for the Poiseuille flow in a cylinder, and the available experimental data (laser Doppler velocimetry) for the flow in a three-dimensional 90° angle tube bend. We further examine the accuracy and applicability of the proposed method by considering flow within complex geometries, such as blood flow in aneurysmal vessels and the aorta, flow configurations that would otherwise be difficult to solve by most IB methods. Our method offers high accuracy, as demonstrated by the verification examples, and high applicability, as demonstrated through the solution of blood flow within complex geometry. The proposed method is efficient, since it is as fast as the traditional finite element method used to solve the Navier–Stokes flow equations, with a small overhead (not more than 5%) due to the numerical solution of a linear system formulated for the IB method.

Projeto e implementação de um sensor de radiação solar com arduino uno e célula de referência

Our work is pointed toward planning a device for estimating all out solar radiation in light of a reference photovoltaic cell. The rule of this device is chiefly founded qualities of a photovoltaic cell where sun oriented radiation is corresponding to the short circuit current, wich is estimated by a resistor.The voltage difference at these connections is measured by acquisition using a functional amplifier and then recorded into an analogue-to-digital converter for further processing. The Arduino Uno platform was chosen to process and display in real-time the solar radiation received from the reference cell surface. The affordable device has a wide range of uses, including monitoring solar radiation in both stand-alone and grid-connected photovoltaic power plants. The decision to utilize the Proteus design software environment has proven to be a key factor in the successful testing and validation of the device's performance. This integrated platform has enabled the efficient implementation and simulation of the hardware and software components, ultimately leading to the development of a cost-effective and reliable solar radiation estimation solution.

Development of an ammonia/diesel combustion mechanism for high ammonia energy ratio: Validation of the mechanisms in kinetic simulation and RCCI optical engine simulation

Ammonia blending in diesel is an efficient combustion strategy that overcomes the combustion resistance of ammonia while maintaining high engine adaptability. A smaller and accurate chemical kinetic mechanism is crucial for exploring the application of ammonia/diesel in engine. This study, based on the stepwise validation and optimization mechanisms, developed a novel ammonia/diesel mechanism comprising 130 species and 772 reactions. The mechanism was validated through kinetic verification for ammonia, n-dodecane, diesel and ammonia/diesel fuel. For profound understanding of the low-temperature autoignition chemistry of ammonia/diesel, applying this mechanism construct precise CFD models in RCCI optical engine. The results indicate that this mechanism can accurately capture ignition under high AER, wide working conditions, and low-temperature conditions. In the kinetic analysis, the C–N interaction reaction, particularly C2H4+NH2=C2H3+NH3, plays a crucial role in predicting the IDT of ammonia/diesel. In addition, NH2 undergoes deoxygenation reaction with HO2, converting inactive radicals HO2 into OH active radicals, enhances the reactivity of ammonia/diesel under low-temperature conditions. Applying the mechanism to CFD models, the model accurately predicts the pressure and heat release rate in RCCI optical engine, capturing the phenomenon of the high-temperature flame rapidly spreading towards the low-temperature regions in the cylinder. The research on this mechanism can construct accurate CFD model for optimizing efficient and clean combustion simulations of ammonia/diesel.

Simulation of Carrier Injection Efficiency in AlGaN-Based UV-Light-Emitting Diodes

Simulation of Carrier Injection Efficiency in AlGaN-Based UV-Light-Emitting Diodes

Efficient Ray-Tracing Simulation for Near-Field Spatial Non-Stationary mmWave Massive MIMO Channel and Its Experimental Validation

Efficient Ray-Tracing Simulation for Near-Field Spatial Non-Stationary mmWave Massive MIMO Channel and Its Experimental Validation

A stochastic Galerkin lattice Boltzmann method for incompressible fluid flows with uncertainties

Efficient modeling and simulation of uncertainties in computational fluid dynamics (CFD) remains a crucial challenge. In this paper, we present the first stochastic Galerkin (SG) lattice Boltzmann method (LBM) built upon the generalized polynomial chaos (gPC). The proposed method offers an efficient and accurate approach that depicts the propagation of uncertainties in stochastic incompressible flows. Formal analysis shows that the SG LBM preserves the correct Chapman–Enskog asymptotics and recovers the corresponding macroscopic fluid equations. Numerical experiments, including the Taylor–Green vortex flow, lid-driven cavity flow, and isentropic vortex convection, are presented to validate the solution algorithm. The results demonstrate that the SG LBM achieves the expected spectral convergence and the computational cost is significantly reduced compared to the sampling-based non-intrusive approaches, e.g., the routinely used Monte Carlo method. We obtain a speedup factor of 5.72 compared to Monte Carlo sampling in a randomized two-dimensional Taylor–Green vortex flow test case. By leveraging the accuracy and flexibility of LBM and the efficiency of gPC-based SG, the proposed SG LBM provides a powerful framework for uncertainty quantification in CFD practice.

Structure-preserving and efficient numerical simulation for diffuse interface model of two-phase magnetohydrodynamics

In this paper, we propose a novel diffuse interface model of two-phase magnetohydrodynamics (MHD) based on a magnetic vector potential formulation in the three-dimensional case. This model ensures an exact divergence-free approximation of the magnetic field by introducing a magnetic vector potential A and defining the magnetic field by B=curlA. The resulting framework constitutes a highly coupled, nonlinear saddle point system consisting of the Cahn–Hilliard system and MHD potential system. To solve the model efficiently, we present two fully decoupled, first-order, linear, and unconditionally energy-stable schemes and strictly prove their well-posedness and energy stability. Finally, we present several numerical examples that demonstrate the stability and effectiveness of our schemes.

Numerical simulation of deep-water wave breaking using RANS: Comparison with experiments

Wave breaking is a multifaceted physical phenomenon that is not fully understood and remains challenging to model. An effective method for investigating wave breaking involves utilising the two-phase Reynolds-averaged Navier–Stokes (RANS) equations to directly simulate breaking waves. In this study, we apply a RANS model with an adaptively refined mesh to simulate breaking waves in deep water using the stabilised RANS model proposed by Larsen and Fuhrman. This approach enables a more efficient simulation of the physics of breaking waves compared to Direct Numerical Simulations, as it places less stringent demands on grid resolution. Our findings demonstrate that the RANS model compares well with deep water wave breaking experiments in terms of surface elevation. We also give estimates of the breaking strength parameter of our RANS simulations and compared them with the literature.

Efficient simulation and valuation of equity-indexed annuities under a two-factor G2++ model

Equity-indexed annuities (EIAs) with investment guarantees are pension products sensitive to changes in the interest rate environment. A flexible and common choice for modelling this risk factor is a Hull–White model in its G2++ variant. We investigate the valuation of EIAs in this model setting and extend the literature by introducing a more efficient framework for Monte-Carlo simulation. In addition, we build on previous work by adapting an approach based on scenario matrices to a two-factor G2++ model. This method does not rely on simulations or on Fourier transformations. In numerical studies, we demonstrate its fast convergence and the limitations of techniques relying on the independence of annual returns and the central limit theorem.

Efficient simulation of low-temperature physics in one-dimensional gapless systems

Efficient simulation of low-temperature physics in one-dimensional gapless systems

Efficient and accurate simulation of vitrification in multicomponent metallic liquids with neural network potentials

Efficient and accurate simulation of vitrification in multicomponent metallic liquids with neural network potentials

Thermo-mechanical investigation of a friction oscillator under high-frequency excitation

The frictional behavior, temperature evolution, and system dynamics in tribological systems are closely coupled. However, in many cases, at least one of the mentioned phenomena is neglected or not modeled consistently. This paper presents a model of a thermo-mechanically coupled oscillator, where the reduction of friction force due to high-frequency excitation is analyzed. The oscillator’s equation of motion, the impulse balance, and the heat equation for the thermo-mechanical continuum are evaluated so that a set of nonlinear equations describes the coupled system. Using specific mathematical procedures like Laplace-transformation and a particular transformation ansatz for integral equations, the system can be transformed into a set of ordinary differential equations, enabling efficient simulation. Based on this equation set, an averaging method is applied to eliminate the fast time scale of the mechanical oscillation due to high-frequency excitation so that only the averaged influence of the motion goes into the heat evolution. This procedure leads to concise expressions to determine the stationary state of the system, and the quantities of interest can be calculated conveniently. These equations yield parameter combinations where the friction force can be reduced due to the high-frequency excitation, even though thermal expansion is considered. However, there are also parameter regions where the excitation yields a significantly larger friction force.

Mixed modeling approach for efficient simulation of single-phase PWM inverters

Mixed modeling approach for efficient simulation of single-phase PWM inverters

Rapid prototyping of perfusion cell culture devices for three-dimensional imaging of mesenchymal stem cell deposition and proliferation

Perfusion of porous scaffolds transports cells to the surface to yield cellular constructs for 3D models of disease and for tissue engineering applications. While ceramic scaffolds mimic the structure and composition of trabecular bone, their opacity and tortuous pores limit the penetration of light into the interior. Scaffolds that are both perfusable and amenable to fluorescence microscopy are therefore needed to visualize the spatiotemporal dynamics of cells in the bone microenvironment. In this study, a hybrid injection molding approach was designed to enable rapid prototyping of collector arrays with variable configurations that are amenable to longitudinal imaging of attached human mesenchymal stem cells (hMSCs) using fluorescence microscopy. Cylindrical collectors were arranged in an array that is permeable to perfusion in the xy-plane and to light in the z-direction for imaging from below. The effects of the collector radius, number, and spacing on the collection efficiency of perfused hMSCs was simulated using computational fluid dynamics (CFD) and measured experimentally using fluorescence microscopy. The effect of collector diameter on simulated and experimental cell collection efficiencies followed a trend similar to that predicted by interception theory corrected for intermolecular and hydrodynamic forces for the arrays with constant collector spacing. In contrast, arrays designed with constant collector number yielded collection efficiencies that poorly fit the trend with collector radius predicted by interception theory. CFD simulations of collection efficiency agreed with experimental measurements within a factor of two. These findings highlight the utility of CFD simulations and hybrid injection molding for rapid prototyping of collector arrays to optimize the longitudinal imaging of cells without the need for expensive and time-consuming tooling.

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.

First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

Directed Self-Assembly by Sparsely Prepatterned Substrates with Self-Responsive Polymer Brushes.

The guiding pattern in the chemoepitaxially directed self-assembly (DSA) of block copolymers is often fabricated by periodically functionalizing homogeneously random copolymer brushes tethered on a substrate. The prepatterned copolymer brushes constitute a soft penetrable surface, and their two components can in principle locally segregate in response to the overlying self-assembly process of block copolymers. To reveal how the self-responsive behavior of the copolymer brushes affects the directing effect, we develop a dissipative particle dynamics model to explicitly include the prepatterned polymer brushes and implement it to simulate the DSA of a cylinder-forming diblock copolymer melt on the sparse pattern of polymer brushes. Through large-scale dynamic simulations, we identify the windows of the content of the random copolymer, the film thickness, and the diameter of the patterned spot, for the formation of perfectly ordered hexagonal patterns composed of perpendicular cylinders. Our dynamic simulations reveal that the random copolymer brushes grafted on the unpatterned area exhibit a remarkable self-responsive ability with respect to the self-assembly of the diblock copolymers overlying them, which may widen the effective window of the content of the random copolymer. Within the processing windows of these key parameters, defect-free patterns are successfully achieved both in simulations and in experiments with sizes as large as a few micrometers for 4-fold density multiplications. This work demonstrates that highly efficient computer simulations based on an effective model can provide helpful guidance for experiments to optimize the critical parameters and even may promote the application of DSA.

Integration of Building Information Model into a Game Engine Platform for Indoor Accessibility Analyses

Understanding the movement patterns of individuals within a structure is crucial for efficient simulation. This entails the examination of network accessibility based on insights into the intricate indoor three-dimensional network topology. The combination of Building Information Modeling with Game Engines can streamline this approach. Hence, this study proposes a pipeline integrating the A* shortest path algorithm and walkable three-dimensional navigation meshes to analyze indoor accessibility. The pipeline design was deployed in a public building, where scenario-based analyses were conducted to determine the average distance and time shifts based on blockages. According to the results, exits' positioning and availability significantly impact indoor navigation and accessibility, underscoring their significance in building design and emergency preparedness in complex buildings.