Analytical Simulation Research Articles

Degradation Detection in Rice Products via Shape Variations in XCT Simulation-Empowered AI

This research explores the process of generating artificial training data for the detection and classification of defective areas in X-ray computed tomography (XCT) scans in the agricultural domain using AI techniques. It aims to determine the minimum detectability limit for such defects through analyses regarding the Probability of Detection based on analytic XCT simulations. For this purpose, the presented methodology introduces randomized shape variations in surface models used as descriptors for specimens in XCT simulations for generating virtual XCT data. Specifically, the agricultural sector is targeted in this work in terms of analyzing common degradation or defective areas in rice products. This is of special interest due to the huge biological genotypic and phenotypic variations occurring in nature. The proposed method is demonstrated on the application of analyzing rice grains for common defects (chalky and pore areas).

A numerical study of the effect of interfacial thermal resistance on thermal conductivity of Cu-B/diamond composites

Cu/diamond composite is a promising thermal management material for heat dissipation of high-power electronic devices. Heat transfer models for a Cu-B/diamond composite with varying boron contents added in the Cu matrix were constructed using the finite element (FE) method, based on the results from transmission electron microscopy (TEM) characterization. The heat transfer behavior of the Cu/diamond composites was then investigated. The predicted effective thermal conductivities were compared to experimental values, using both analytical model calculation and FE simulation. The FE simulation effectively illustrates the dependence of thermal conductivity on interface structure evolution of the composite. The heat transfer behavior of the Cu-B/diamond composites varies as the boron content increases. In the Cu-0.3wt%B/diamond composite, most of the heat flow is concentrated and transferred along the diamond particles. In the Cu-1.0wt%B/diamond composite, the heat flux distribution and flow direction are similar to those in the Cu-0.3wt%B/diamond composite, but the heat flux is substantially lower. The heat transfer behavior is closely related to the interactions between the two phases in the composite and is intensively influenced by the evolution of interfacial carbide morphology. The FE simulation provides a more accurate prediction of effective thermal conductivity compared to the analytical model calculation, as it considers the reasonable interactions between the two phases relating to the actual interfacial structure. The findings provide a fundamental basis for optimizing the interfacial structure of Cu/diamond composites and further improving their thermal conductivity.

BOT contract of high-speed rail project design considering uncertain economic spillover

The rapid development of high-speed rail (HSR) has had a significant impact on economic development, but it requires substantial investment and places a burden on public funds. The COVID-19 pandemic has further strained government budgets, making alternative funding models attractive. The build-operate-transfer (BOT) model, commonly used in highway projects, involves private companies building and operating infrastructure in exchange for toll revenues. This study explores the use of flexible and fixed BOT contracts for HSR projects and analyzes their impact on private sector investment share and social welfare in HSR projects. Our analytical and numerical simulation findings suggest that under the flexible BOT contract, the private sector investment share increases with the expectation and uncertainty of the economic spillover effect. It decreases with a short HSR infrastructure lifecycle but remains unaffected by a sufficiently long lifecycle. In contrast, a fixed BOT contract raises private sector investment share regardless of the uncertainty of economic spillover. A longer HSR lifecycle amplifies the effect of economic spillover under both types of contracts. Expected social welfare rises with the HSR infrastructure's lifecycle under the fixed BOT contract, but for the flexible BOT contract, it initially increases and then exhibits a jump-down discontinuity at the threshold lifecycle of the HSR infrastructure. The flexible BOT contract outperforms the fixed BOT contract with a shorter lifecycle or higher spillover uncertainty, while the fixed BOT contract yields more welfare improvements for a longer lifecycle.

THE METHOD OF THE VEHICLE SUSPENSION SYSTEM DAMPING ELEMENT LOAD CHARACTERISTIC SYNTHESIZING

BACKGROUND. Nowadays the question of increasing the smoothness of running of vehicles is actual. The main way of solution is to carry out synthesis of design parameters of suspension systems. For carrying out such works and obtaining the most satisfactory characteristics new methods of synthesis of load characteristics of vehicle suspension devices are necessary. AIMS. The purpose of this work is to develop a new method of synthesizing the characteristic of the shock absorber of the vehicle suspension system. METHODS. The study solves the problem of controlling vibrations on their propagation paths by determining the required load characteristic of a vehicle suspension damping device. Analytical methods and simulation modeling methods are applied in the solution process. RESULTS. As a result of the work a new method based on the construction of the zone of permissible damping values and selection of the characteristic on its basis has been created. On the basis of the obtained method a number of nonlinear characteristics of the damping element of the primary system of vehicle suspension and selection of the most suitable one from the point of view of smoothness of motion were formed. CONCLUSIONS. As a result of the work a new method of synthesizing the damping characteristic of the shock absorber has been obtained. As a result of comparative evaluation the efficiency of the new method is confirmed. For the considered object of research the difference in smoothness of motion amounted to 25%.

Playing Divide-and-Choose Given Uncertain Preferences

We study the classic divide-and-choose method for equitably allocating divisible goods between two players who are rational, self-interested Bayesian agents. The players have additive values for the goods. The prior distributions of those values are common knowledge. We consider both the cases of independent values and values that are correlated across players (as occurs when there is a common-value component). We describe the structure of optimal divisions in the divide-and-choose game and identify several cases where it is possible to efficiently compute equilibria. An approximation algorithm is presented for the case when the distribution over the chooser’s value for each good follows a normal distribution, along with a randomized approximation algorithm for the case of uniform distributions over intervals. A mixture of analytic results and computational simulations illuminates several striking differences between optimal strategies in the cases of known versus unknown preferences. Most notably, given unknown preferences, the divider has a compelling “diversification” incentive in creating the chooser’s two options. This incentive leads to multiple goods being divided at equilibrium, quite contrary to the divider’s optimal strategy when preferences are known. In many contexts, such as buy-and-sell provisions between partners, or in judging fairness, it is important to assess the relative expected utilities of the divider and chooser. Those utilities, we show, depend on the players’ levels of knowledge about each other’s values, the correlations between the players’ values, and the number of goods being divided. Under fairly mild assumptions, we show that the chooser is strictly better off for a small number of goods, whereas the divider is strictly better off for a large number of goods. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: This work was supported by the Mossavar-Rahmani Center for Business and Government, Harvard University, and the National Science Foundation [Grant DGE1745303]. Any opinions, findings, and conclusions or recommendations expressed in this material reflect the views of the authors and not necessarily those of the National Science Foundation or the Mossavar-Rahmani Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00350 .

Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage

Computer simulation is one of the tools that is often used to support activities related to the management of the production process. Computer simulation allows for the analysis of the parameters of production or logistics processes, among others. The study presents a comparative analysis of analytical methods and computer simulation (FlexSim 22.2.1 software) in relation to the subject matter. The use of computer simulation allowed for the verification of the number of production resources, determined by analytical methods, and for the proposal of improvements that would allow for shortening the duration of the production process and reducing its costs. The proposed improvements at the stage of planning the production process in terms of the number of production resources used to implement the assumed production plan allowed for the reduction of the duration of the production process and its costs. Therefore, it is justified to use computer simulation in solving problems related to various aspects of the functioning of an enterprise.

The Post-Kelly Strategy: A Negative Feedback Model of Reallocating Ant Foragers.

In ant foraging, the manner of group-mass recruitment demonstrates remarkable adaptability between tandem running and mass recruitment. In contrast to tandem running, where a leader recruits only one worker, the first phase of group-mass recruitment is characterized by strong invitations from leaders that result in a large group of recruits leaving the nest together in a rush, thereby accelerating the process of recruiting towards discovered resources. Furthermore, unlike sole mass recruitment, the influence of leaders during this first phase enhances the accuracy of information about food qualities and ensures a more rational allocation of recruits compared to simply following a dominant pheromone trail. In this study, we propose a model that integrates the Kelly criterion for the first phase of group-mass recruitment, followed by a post-Kelly strategy incorporating a delayed Pólya urn with two stages for the second phase of group-mass recruitment. The analytical process and simulation demonstrate that the Kelly criterion aims to maximize recruitment intensity during the initial foraging phase, employing crowd tactics to capture all available food sources and enhance competitiveness with other food-exploiting species. On the other hand, the post-Kelly strategy elucidates how the crowding negative feedback mitigates congestion resulting from overexploitation and improves overall efficiency in food exploitation.

Effects of the passive voltage divider in a photomultiplier tube: Analytical model, simulations and experimental validation

The effects of the passive resistive voltage divider network in a photomultiplier tube (PMT) have been investigated by developing an in-house Monte Carlo simulation code and compared with experimental measurements and an analytical model. The simulation code follows an iterative procedure that takes into account the transport and amplification of the electrons within the device depending on the electrostatic fields produced by the electrode voltages. The PMT gain, dynode voltages, rise time and transit time have been studied as a function of the photocathode current and supply voltage. A good agreement between the analytical model, the simulations and numerous experimental measurements using a Hamamatsu R13408-100 PMT has been obtained. The simulation results endorse the use of logistic functions within the analytical model to account for the collection efficiency in the last dynode stages. This works deepens the understanding of passive voltage dividers and develops an advanced behavioral circuit model of photomultiplier tubes. Although validated for a single PMT, the proposed methodology is applicable to any PMT model. This aids in optimizing the design of fully active voltage dividers, to be applied in extremely pulsed applications with high count rates such as prompt gamma-ray imaging during proton therapy.

Proton-free induction decay MRSI at 7 T in the human brain using an egg-shaped modified rosette K-space trajectory.

Proton (1H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency because of their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time. Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in 6 min in a 2D slice in the brain. An elliptical phase-encoding sequence was measured in one volunteer in 22 min, and a 3D sequence was measured in one volunteer within 19 min. The SNR per-unit-time, linewidth, Cramer-Rao lower bounds (CRLBs), lipid contamination, and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory. Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an SNR per-unit-time increase of up to 12% compared to rosette's and 23% compared to elliptical phase-encoding, dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in vivo, and phantom data. The CRLBs of γ-aminobutyric acid and N-acetylaspartylglutamate improved non-significantly for the modified rosette trajectory, whereas the linewidths and lipid contamination remained similar. By optimizing the shape of the rosette trajectory, the modified rosette trajectories achieved higher SNR per-unit-time and enhanced data quality at the same scan time.

Symmetrical Short-Circuit Behavior Prediction of Rare-Earth Permanent Magnet Synchronous Motors

Since the advent of rare-earth permanent magnet (PM) materials, PM synchronous machines (PMSMs) have become popular in power generation, industrial drives, and e-mobility. However, rare-earth PMs in PMSMs are prone to temperature- and operation-related irreversible demagnetization. Additionally, faults can endanger components like inverters, batteries, and mechanical structures. Designing a fault-tolerant machine requires considering these risks during the PMSM design phase. Traditional transient finite element analysis is time-consuming, but fast analytical simulation methods provide viable alternatives. This paper evaluates methods for analyzing dynamic three-phase short-circuit (3PSC) events in PMSMs. Experimental measurements on a PMSM prototype serve as benchmarks. The results show that accounting for machine saturation reduces discrepancies between measured and predicted outcomes by 20%. While spatial harmonic content and sub-transient reactance can be neglected in some cases, caution is required in other scenarios. Eddy currents in larger machines significantly impact 3PSC dynamics. This work provides a quick assessment based on general machine parameters, improving fault-tolerant PMSM design.

Performance Analysis of Reflective Intelligent Surface Assisted Bidirectional Full‐Duplex Communication Systems Over Nakagami‐m$$ m $$ Fading Channels

ABSTRACTIn this paper, a passive reflective intelligent surface (RIS) assisted bidirectional full‐duplex (FD) wireless system in a realistic Nakagami‐ fading with reflective elements and two source nodes is proposed. The source nodes are equipped with dual antennas for FD mode transmission and reception. The primary cause of degradation in the performance of the FD system is self‐interference, which can be reduced by increasing the number of reflective elements on the meta‐surface. Analytical expressions have been derived for the proposed RIS‐aided bidirectional FD wireless systems in terms of outage probability and bit error rate (BER). Numerical results show that doubling the number of reflecting elements considerably improves the outage performance of the proposed system in terms of signal‐to‐noise ratio (SNR) compared with RIS‐assisted half‐duplex (HD) mode. Additionally, different modulation schemes have been employed to evaluate the BER performance of the proposed system. The accuracy of analytical expressions is validated by matching Monte Carlo with analytical simulations.

Stochastic Modeling of a Base Station in 5G Wireless Networks for Energy Efficiency

ABSTRACTThe potential benefits of 5G networks, such as faster data speeds and improved user experiences, come with a critical challenge—efficiently preserving energy in base stations (BSs). Addressing this challenge is crucial, necessitating a focus on maximizing the energy efficiency of these stations. BSs play a vital role in providing coverage and capacity by using different frequency bands adapting to diverse communication needs. The choice of a specific frequency band can impact network performance, data rates, coverage range, and signal propagation. This study emphasizes the crucial challenge of preserving energy in 5G BSs and underscores the significance of strategic frequency band selection for optimizing energy efficiency and network performance. For this, stochastic models are introduced for BSs, specifically the Markov and semi‐Markov models, which help choose frequency bands based on system traffic. Closed‐form solutions for steady‐state system size probabilities in these models are derived. A sensitivity analysis is conducted to assess power consumption in various scenarios. Furthermore, a comparison between analytical findings and simulation outcomes regarding power consumption is presented. The convergence of results in both methodologies emphasizes a significant level of agreement; that is, the introduced stochastic models provide insights and are validated by a close alignment between analytical and discrete‐event simulation outcomes. We have shown the behavior of power consumption with respect to three different distributions named deterministic, exponential, and hypo‐exponential. This research highlights the importance of strategic frequency band selection for 5G BSs to optimize energy efficiency and meet the demands of evolving communication networks.

Modeling and Monitoring of the Tool Temperature During Continuous and Interrupted Turning with Cutting Fluid

In metal cutting, a large amount of mechanical energy converts into heat, leading to a rapid temperature rise. Excessive heat accelerates tool wear, shortens tool life, and hinders chip breakage. Most existing thermal studies have focused on dry machining, with limited research on the effects of cutting fluids. This study addresses that gap by investigating the thermal behavior of cutting tools during continuous and interrupted turning with cutting fluid. Tool temperatures were first measured experimentally by embedding a thermocouple in a defined position within the tool. These experimental results were then combined with simulations to evaluate temperature changes, heat partition, and cooling efficiency under various cutting conditions. This work presents novel analytical and numerical models. Both models accurately predicted the temperature distribution, with the analytical model offering a computationally more efficient solution for industrial use. Experimental results showed that tool temperature increased with cutting speed, feed, and cutting depth, but the heat partition into the tool decreased. In continuous cutting, cooling efficiency was mainly influenced by feed rate and cutting depth, while cutting speed had minimal impact. Interrupted cutting improved cooling efficiency, as the absence of chips and workpieces during non-cutting phases allowed the cutting fluid to flow over the tool surface at higher speeds. The convective cooling coefficient was determined through inverse calibration. A comparative analysis of the analytical and numerical simulations revealed that the analytical model can underestimate the temperature distribution for complex tool structures, particularly non-orthogonal hexahedral geometries. However, the relative error remained consistent across different cutting conditions, with less error observed in interrupted cutting compared to continuous cutting. These findings highlight the potential of analytical models for optimizing thermal management in metal turning processes.

Chirality-induced phonon spin selectivity by elastic spin–orbit interaction

Spin and orbital degrees of freedom are crucial in not only fundamental particles but also classical waves such as optical systems, wherein the spin-orbit interaction (SOI) of light provides new perspectives for manipulating electromagnetic waves. Elastic waves possess similar spin angular momentum (SAM) and orbital angular momentum (OAM). However, the elastic counterpart of SOI remains unexplored, even for ubiquitous elastic waveguides (WG). Here, we demonstrate the existence of elastic SOI in helical WG. We prove that the torsion and curvature of helical WG induces synthetic gauge potentials in describing the elastic vibrations. Through analytical theory and simulations, we unveil the interplay among elastic SAM, intrinsic OAM, and extrinsic OAM, impacted by the elastic SOI. Importantly, results show that elastic SOI can introduce the Chirality-Induced Phonon Spin Selectivity. These findings advance our understanding of angular momentum physics in elastic waves and enable practical strategies for wave manipulation.

Analytical simulation of Darcy–Forchheimer nanofluid flow over a curved expanding permeable surface

Abstract This research paper presents an analytical simulation of Darcy–Forchheimer flow over a porous curve stretching surface. In fluid dynamics, the Darcy–Forchheimer model combines Forchheimer adjustment and high-velocity effects with Darcy’s formula for porous media flow: two nanofluid particles, molybdenum disulphide, and graphene oxide, form nanofluid with the base fluid blood. The governing partial differential equations for momentum and energy are converted into a nonlinear ordinary differential equations system by applying the appropriate similarity transformations. The homotopy analysis method is used to solve the transform equations analytically. The impact of essential factors includes the Forchheimer parameter, porosity parameter, slip parameter, Eckert number, nanoparticle volume friction, magnetic field parameter, and curvature parameter. The results have applications in the design of sophisticated cooling systems, where effective thermal control is essential.

An analytical 1D model for computing low-frequency electromagnetic fields in material layers: Application to metallurgical furnaces

An analytical one-dimensional model for the distribution of electric fields within multiple material layers is developed and analyzed. The model originates from the study of large three-phase electric smelting furnaces for ferroalloys and is derived from the low-frequency time-harmonic Maxwell's equations. A solution is obtained for a general case with N layers of material with different electromagnetic properties. A practical demonstration of the utility of the model is given through an application to a multilayer configuration representing the lining and casing in a FeMn furnace, with validation against 2D simulations. In addition, for a specific two-layer scenario with a highly conductive material, an approximate solution for the adjacent layer is derived. This approximation allows the distribution of the adjacent layer to depend only on its individual properties, and shows that the dissipated power reaches a maximum value when the skin depth/thickness ratio is around one. Comparative analysis between the analytical model and 2D simulations shows good qualitative agreement.

Force-compensation-based adaptive thermal shape correction method for XFEL optics

With the successive development of free electron laser (FEL) facilities based on superconducting technology, the advance and diversity of beamline optical design have posed more stringent challenges to the controlling of thermal deformation for key optical elements. In this article, an adaptive thermal shape correction structure is presented, which converts the thermal stress into a bending moment to correct the mirror thermal bump by utilizing the difference in coefficient of thermal expansion (CTE) between materials, and the location relative to the mirror neutral plane. This moment is involved owing to the temperature rise derived from the FEL heat load, which has a certain adaptability to various thermal surface profile and can be precisely controlled by a chiller temperature regulation. In this work, we optimize the dimensions and position of the thermal shape correction blocks by analytical method and FEA simulation respectively. Eventually, this force-compensation-based adaptive scheme can achieve sub-nano sensitivity (∼ 0.1 nm) of mirror shape control, considering factors such as ease of engineering implementation and operational feasibility, even under repetition rates up to 100 kHz.

Ion motion in strongly magnetized cluster laser plasma

Abstract This paper investigates the interaction of high intensity, circularly polarized, short laser pulses with heavy cluster plasma through analytical modeling and numerical simulations. The optimal parameters were found for the generation of several GigaGs quasi-stationary magnetic field by using the upgraded analytical model and detailed 3D PIC simulations. It is confirmed that a field of such strength slows down the thermal expansion of the cluster core in the direction transverse to the laser beam axis, which can be used in nuclear physics. The generated inward shocks produce ion core compression, which is relevant to nuclear fusion. The electrostatic interaction between the rotating laser field, electrons and ions leads to an ion spiral density distribution in the cluster’s transversal plane, which is absent in a cluster irradiated by linearly polarized pulses with the same parameters.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT

Objective.This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors.Approach.The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3×3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations.Main results.Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%and 31%across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%.Significance.The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.