Search Results (396)

CO₂-biomethanation was studied in the present manuscript by considering the direct injection of hydrogen into a conventional anaerobic digester treating sewage sludge within a simulated wastewater treatment plant (WWTP). The plant was simulated using the Python 3.12.4 software, and a Monte Carlo simulation was conducted to account for the high variability in the organic content of the wastewater and the methane potential of the sludge. Two modes of operation were studied. The first mode involves the use of an anaerobic digester to upgrade biogas, and the second mode considers using the digester as a CO₂ utilization unit, transforming captured CO₂. Upgrading biogas and utilizing the extra methane to generate electricity within the same plant leads to a negative economic balance (first scenario). A hydrogen injection of 1 L of H₂/L_r d (volumetric H₂ injection per liter of reactor per day) was required to transform the CO₂ present in the biogas into methane. The benefits associated with this approach resulted in lower savings regarding heat recovery from the electrolyzer, increased electricity production, and an additional oxygen supply for the waste-activated sludge treatment system. Increasing the injection rate to values of 5 and 30 L of H₂/L_r d was also studied by considering the operation of the digester under thermophilic conditions. The latter assumptions benefited from the better economy of scale associated with larger installations. They allowed for enough savings to be obtained in terms of the fuel demand for sludge drying, in addition to the previous categories analyzed in the biogas upgrading case. However, the current electricity price makes the proposal unfeasible unless a lower price is set for hydrogen generation. A standard electricity price of 7.6 c€/kWh was assumed for the analysis, but the specific operation of producing hydrogen required a price below 3.0 c€/kWh to achieve profitability. Full article

Conductive polymer composites (CPCs) filled with anisotropic materials such as carbon nanotubes (CNTs) exhibit electrical behavior governed by percolation through filler networks. While filler volume and shape are commonly studied, the influence of orientation and alignment remains underexplored. This study uses Monte Carlo simulations to examine how the mean orientation angle and angular dispersion of CNTs affect conductive network formation. The results demonstrate that electrical connectivity is highly sensitive to orientation. Contrary to conventional assumptions, maximum connectivity occurred not at 45° but at around 55–60°. A Gaussian-based orientation probability function was proposed to model this behavior. Additionally, increased orientation dispersion enhanced conductivity in cases where alignment initially hindered connection, highlighting the dual role of alignment and randomness. These findings position orientation as a critical design parameter—beyond filler content or geometry—for engineering CPCs with optimized electrical performance. The framework provides guidance for processing strategies that control alignment and supports applications such as stretchable electronics, directional sensors, and multifunctional materials. Future research will incorporate full 3D orientation modeling to reflect complex manufacturing conditions. Full article

Coherent backscattering is an interference phenomenon that occurs in the backwards direction of the incident illumination. It arises from photons traveling the same path in opposite directions within a scattering medium. Accurately determining the background signal for normalization can be challenging in such measurements. This study investigates the use of a spatial light modulator to control spatial coherence, effectively switching the interference on and off. This approach enables independent, absolute measurements of both the signal and background across the full angular detection range without modifying the experimental setup. We demonstrate this method experimentally, highlighting the importance of accurate background determination in coherent backscattering measurements using a Fourier-based setup. Additionally, we demonstrate that measurements normalized to the correct background closely match Monte Carlo simulations of the coherent backscattering signal. Full article

Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance. Full article

Monte Carlo simulations of induced extensive air showers (EASs) by ultra-high-energy cosmic rays are widely used in comparison with measured events at experiments to estimate the main cosmic ray characteristics, such as mass, energy, and arrival direction. However, these simulations are computationally expensive, with running time scaling proportionally with the number of radio antennas included. The AugerPrime upgrade of the Pierre Auger Observatory will feature an array of 1660 radio antennas. As a result, simulating a single EAS using the full detector array will take weeks on a single CPU thread. To reduce the simulation time, detectors are commonly pre-selected based on their proximity to the shower core, using a selection ellipse based on the Cherenkov radiation footprint scaled by a fixed constant factor. While effective, this approach often includes many noisy antennas at high zenith angles, reducing computational efficiency. In this paper, we introduce an optimal method for selecting candidate detectors with high predicted signal-to-noise ratio for proton and iron primary cosmic rays, replacing the constant scaling factor with a function of the zenith angle. This approach significantly reduces simulation time—by more than 50% per CPU thread for the heaviest, most inclined showers—without compromising signal quality. Full article

This paper presents the design of an airborne DMD-based dual-path multi-target imaging spectrometer that is capable of achieving instantaneous imaging over a two-dimensional large field of view and the simultaneous spectral analysis of thousands of targets. It also offers advantages such as high spatial resolution, high spectral resolution, high timeliness, and low platform requirements. However, its working mechanism inherently causes misalignment errors in the dual-path images that it obtains due to exposure time differences. To address this issue, we propose a dual-path exposure time difference compensation method based on a velocity vector field model, enabling dynamic and precise matching of the dual paths. For target image points that move beyond the field of view, we propose an attitude compensation method based on optimal angular velocity coordination. Monte Carlo simulation results show that the maximum root mean square error of the compensation method across the entire field of view is 0.9792 pixels in the x-direction and 0.7130 pixels in the y-direction. Experimental results demonstrate the effectiveness of the method, which meets the requirements for practical applications and provides a reliable foundation for the real-world implementation of dual-path multi-target imaging spectrometers. Full article

With the rapidly increasing number of satellites and orbital debris, collision avoidance and reentry prediction are very important for space situational awareness. A precise orbital prediction through orbit determination is crucial to enhance the space safety. The two-line element (TLE) data sets are publicly available to users worldwide. However, the data sets have uneven qualities and biases, resulting in exponential growth of orbital prediction errors in the along-track direction. A hybrid algorithm of the simplex method and genetic algorithm is proposed to improve orbit determination accuracy using TLEs. The parameters of the algorithm are tuned to achieve the best performance of orbital prediction. Six satellites with consolidated prediction format (CPF) ephemeris and four satellites with precise orbit ephemerides (PODs) are chosen to test the performance of the algorithm. Compared with the results of the least-squares method and simplex method based on Monte Carlo simulation, the new algorithm demonstrated its superiorities in orbital prediction. The algorithm exhibits an accuracy improvement as high as 40.25% for 10 days of orbital prediction compared to that using the single last two-line element. In addition, six satellites are used to evaluate the time efficiency, and the experiments prove that the hybrid algorithm is robust and has computational efficiency. Full article

The paper considers an extended interpretation of the second law of thermodynamics and its implications to power conversion optimization in multidomain systems. First, a generalized, domain-independent version of the classical Gouy-Stodola theorem is derived for interconnected systems which satisfy the Clausius postulate of the second law. Mechanical, electrical and more general Hamiltonian systems do not satisfy this postulate, however the related property of energy cyclodirectionality may be satisfied. A generalized version of the Gouy-Stodola theorem is then obtained in inequality form for systems satisfying this property. The result defines average forms of entropy generation and lost work for multi-domain systems. The paper then formulates an optimal control problem for a representative electromechanical system, obtaining complete, closed-form solutions for the load power transfer and energy harvesting cases. The results indicate that entropy generation minimization is akin to the maximum power transfer theorem. For the power harvesting case, closed-loop stability is guaranteed and practical controllers may be designed. The approach is compared against direct minimization of losses, both theoretically and with Monte Carlo simulations. Full article

In recent years, high-entropy materials (HEMs) have garnered significant attention due to their unique multi-principal element compositions, which endow them with remarkable properties distinct from traditional materials. The order and disorder in HEMs are particularly complex, influenced by factors such as temperature, pressure, and composition, and are closely related to their mechanical and physical properties. This review systematically summarizes the progress in understanding the order and disorder in HEMs, with a focus on the role of data science in this field. We introduce the basic concepts of order and disorder and the related research in HEMs, discuss the nonlinear behaviors of HEMs, and elaborate on the relevant applications of data science, including analysis by machine learning, molecular dynamics simulations, and Monte Carlo simulations. Challenges and future directions are also explored, aiming to provide comprehensive insights into materials science. Full article

In the present study, a thermal–electromagnetic hydrodynamics model has been used to study welding temperature and melt flow characteristics during the laser welding of 316L steel. This welding was performed using an assisted electromagnetic field. In addition, a Monte Carlo model was used to study grain growth during solidification with the purpose of achieving a better understanding of the control of the microstructure. Based on the numerical model, which has been validated by experimental data, the effects of the current intensity of the electromagnetic field on the temperature distribution, melt flow characteristics, and grain growth are discussed here in detail. The simulation results showed that both Marangoni convection and welding temperature could be controlled by the magnetic damping effect, and that they increased due to the electromagnetic heating effect when using an electromagnetic field. Furthermore, when controlling the temperature distribution and melt flow velocity in the laminar flow of the melt pool, which was assisted by a 30 A current intensity of the electromagnetic field, the temperature gradient decreased by 13.5%. This decrease could be even larger than 50% when a turbulent flow was formed in the melt pool, which has here been demonstrated for a current intensity of 100 A. In addition, undercooling was found to decrease because of the increase in the melt flow velocity when using an assistive electromagnetic field. This led to a longer nucleation time in the melt pool. Furthermore, more and larger directional columnar grains, grown by the driving force of the temperature gradient, could be formed after the consumption of the small, nucleated grains near the solid–liquid interface. In short, by controlling the temperature distribution and melt flow velocity, the required grain morphology (equiaxed or columnar) and dimension (radius, length, or width) can be controlled by coarsening and epitaxial growth. Full article

This paper concerns interactive Monte Carlo simulations for adiabatic ensembles and a genetic algorithm to research and educational contexts. In the Introduction, we discuss some concepts of thermodynamics, statistical mechanics and ensembles relevant to molecular simulations. The second and third sections of the paper comprise two programs in JavaScript regarding (i) argon in the grand-isobaric ensemble focusing on the direct calculation of entropy, vapor–liquid equilibria and radial distribution functions and (ii) an ideal system of quantized harmonic oscillators in the microcanonical ensemble for the determination of the entropy and Boltzmann distribution, also including the definition of Boltzmann and Gibbs entropies relative to classical systems. The fourth section is concerned with a genetic algorithm program in Java, as a pedagogical alternative to introduce the Second Law of Thermodynamics, which summarizes artificial intelligence methods and the cumulative selection process in biogenesis. Full article

Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks. Full article

Enhancing the precision of carbon accounting not only improves climate policy design, but also contributes directly to sustainability goals by enabling more targeted and accountable emission reduction strategies. Therefore, accurate carbon inventories are foundational to evidence-based climate action and sustainable development planning. This study estimates the carbon emissions of Hunan Province from 2016 to 2020 using the sectoral approach and energy activity data across four major sectors—industrial production, thermal power generation, transportation, and residential life. Emission factors (EFs) were drawn from three different sources: direct measurements, IPCC (Intergovernmental Panel on Climate Change) default values, and published literature. An improved Monte Carlo simulation method was employed to assess the uncertainty of carbon emission accounting associated with different EF sources. The experimental results indicated that carbon emissions calculated based on the literature and default EFs were systematically higher than those derived from empirical measurements, primarily due to discrepancies in the industrial and power generation sectors. In a representative year (2017), the carbon emission estimated based on measured EFs produced the narrowest confidence intervals, reflecting lower uncertainty (−5.31–8.17%), while the uncertainties of carbon emissions calculated using the literature and default EFs were −6.88–9.03% and −5.77–9.94%, respectively. The industrial carbon emissions were the dominant source of overall uncertainty, while the transportation carbon emission had a comparatively minor impact. Importantly, across all departments, the use of measured EFs significantly reduced the uncertainty of carbon inventories, reinforcing the value of locally calibrated data. These findings underscore the urgent need for improved EF measurement systems and standardized accounting practices to support the reliability of subnational carbon inventories. Full article

This paper investigates the outage probability of a hybrid Radio Frequency–Underwater Wireless Optical Communication (RF-UWOC) system that employs the Decode-and-Forward protocol under composite fading channels. It is assumed that the RF link experiences Generalized K distribution fading along with atmospheric path loss, while the UWOC link endures generalized Gamma distribution turbulent fading, accounting for underwater path loss and pointing errors. Based on these assumptions, when intensity modulation with direct detection (IM/DD) and heterodyne detection (HD) are, respectively, utilized at the receiver, the average outage probability and its corresponding asymptotic expression for the considered hybrid dual-hop systems under high signal-to-noise ratios are derived. Subsequently, Monte Carlo simulations are conducted to validate the accuracy of the theoretical analysis results and to explore the influence of various key system parameters on the dual-hop systems. Full article

The study of thruster plume flow fields can yield a series of thruster performance parameters such as thrust effect, spacecraft plume contamination, etc., which is of great significance for thruster development. The paper presents a physical simulation model of a laser thruster under a vacuum back-pressure environment. Through the finite difference method and the Direct Simulation Monte Carlo (DSMC) calculation method, based on the actual laser ablation thruster structure and working mode, the changes in the flow-field distribution in the laser thruster plume under different vacuum back-pressure conditions are obtained. The influence of different vacuum back-pressure conditions on the plume density, pressure, temperature, and velocity field of the thruster was verified through physical experiments, and the evolution of the plume flow field during the laser ablation of a polyamide glycidyl ether (GAP) solid target material was analyzed in detail. The simulation results are in good agreement with the test results, and the deviation between the simulated data and the test data from 0 to 3000 ns is less than 10.4%. This study presents a foundation for a prediction model of laser ablation thrusters under vacuum environments and provides an important reference for ground tests and in-orbit applications of satellite laser propulsion systems. Full article