Numerical Simulations Research Articles

Electromagnetic–thermal–mass transport triple hybrid model for energy-efficient hydrogen release from metal particles by induction heating

Hydrogen storage in metals is anticipated to be a highly promising technical approach for managing hydrogen in diverse energy applications, emphasizing the need to establish efficient operating conditions. In this study, we developed a numerical model that can simultaneously calculate hydrogen and heat transport combined with electromagnetic induction heating of metal particles. Using the developed numerical simulator, we investigated the behavior of the hydrogen release process from the hydrogen-absorbing metal and proposed important indicators for the operation. The fractions of the electromagnetic energy absorbed by the metal particle used for the hydrogen release, sensible heat, interfacial heat conduction, and thermal radiation were systematically evaluated. Considering the trade-off between temperature rise and the hydrogen concentration gradient, an optimal diameter of metal particles existed for the maximum hydrogen release rate. Similarly, the trade-off between temperature rise and energy consumption revealed an optimal electromagnetic power density for the heat utilization fraction for hydrogen release. Our electromagnetic–thermal–mass transport triple hybrid model constitutes a useful numerical simulator for the design of energy-efficient hydrogen storage systems.

Towards non-invasive tissue hydration measurements with optical coherence tomography.

The attenuation coefficient ( ) measured by optical coherence tomography (OCT) has been used to determine tissue hydration. Previous dual-wavelength OCT systems could not attain the needed precision, which we attribute to the absence of wavelength-dependent scattering of tissue in the underlying model. Assuming that scattering can be described using two parameters, we propose a triple/quadrupole-OCT system to achieve clinically relevant precision in water volume fraction. In this study, we conduct a quantitative analysis to determine the necessary precision of measurements and compare it with numerical simulation. Our findings emphasize that achieving a clinically relevant assessment of a 2% water fraction requires determining the attenuation coefficient with a remarkable precision of 0.01 . This precision threshold is influenced by the chosen wavelength for attenuation measurement and can be enhanced through the inclusion of a fourth wavelength range.

Computational fluid dynamics numerical simulation of flue gas emission from stack and tower integration system of coal-fired power plant

ABSTRACT The method of Computational Fluid Dynamics (CFD) simulation has been extensively employed in the stack and tower integration technology (STIT) research; however, a significant number of studies overlooked the impact of thermal buoyancy on the external flow field of cooling tower. In light of this, an improved approach is proposed in this study. A model of a cooling tower and its surrounding flow field was established, and its reliability was confirmed too. Based on our model, the performance of three enhancement strategies under various crosswind speeds and ambient temperatures was simulated. The results indicate that all three enhancement strategies can effectively simulate the effects of thermal buoyancy. In the simulations, the elevation of the flue gases increased from 306 m to either 426 m or 476 m. In the present study, ambient conditions characterized by crosswind speeds below 4 m/s and temperatures below 297.15 K were deemed suitable for the dispersion of stack and tower integration (STI) flue gases. A power function distribution was identified between crosswind speed and the mean height of the plume, while a linear relationship was observed between ambient temperature and the mean height of the plume.

СИНТЕЗ ЭЛЕКТРОПРИВОДА КОМПРЕССОРНОЙ УСТАНОВКИ ЭЛЕКТРОВОЗА

The study objective is to improve the reliability and energy efficiency of the electric locomotive compressor drive. To achieve this, the required power of the electric motor was calculated the performance of the compressor unit, the volume of the main tanks and the selected electric motor were checked to start and adjust the capacity at low supply voltage. The task to which the paper is devoted is to design an electric drive for a compressor motor. A vector model of an asynchronous motor was made. The process of direct start-up and starting an asynchronous electric motor with the use of a scalar control system and IR compensation were modeled. Research methods: systematic approach; provisions of the theory of automatic control and electrical engineering; numerical PC simulation using Matlab software package. The novelty of the work is in the use of a frequency-controlled electric drive to start the asynchronous electric motor of the compressor unit instead of the normally installed starting capacitors. This design solution will significantly reduce the amplitude of the starting currents, which in turn will reduce the wear of the winding insulation. The study results are presented, based on the results of the constructed models, as graphs of the dependencies of speed and current during direct start of the electric motor and with the control system. Conclusions: the scalar control system, thanks to the introduction of an intensity setter, as well as IR compensation diagram, allow extending the transition time and controlling the value of the inrush current in acceptable values. Such an approach to make an electric drive compressor motor is able to significantly increase its reliability and service life, as well as to increase the overall energy efficiency of the system.

Confidence intervals for mean response times of a open queueing network: calibration approach

Response time is a key factor in the analysis of the different queueing network model properties. This study computes a series of response times using a data-based recurrence relation. The true average response time is determined using the sample averages from those response times. Several confidence intervals are created for the open queueing net- work model’s average response times using the calibration approach. Using a numerical simulation analysis, the accuracy of the various confidence intervals is evaluated. Journal of Statistical Research 2023, Vol 57, No.1-2, pp.69-79

Effect of ammonia energy ratio and diesel pilot-injection strategy on the performance of ammonia-diesel dual fuel engine

ABSTRACT The combustion strategy under a higher ammonia energy ratio (AER) is expected to improve the combustion performance of ammonia-diesel dual-fuel (ADDF) engines, leading to additional reductions in greenhouse gas (GHG) emissions. In order to meet the engine’s performance requirements while achieving the low carbon emissions, this study utilized CFD numerical simulations to analyze the combustion and emission characteristics for an ADDF engine at AER levels from 0% to 90%. It focused primarily on evaluating the effectiveness of diesel pilot-injection strategy in reducing GHG emissions at AER50%. The obtained results showed that the unburned NH3 and N2O emissions exhibited an upward trend with the increment of AER, leading to a degradation in the indicated thermal efficiency (ITE), while GHG emissions were more significant than in the original diesel-only combustion mode. Based on AER50%, the ITE exhibited a trend of initially rising and then falling with the increment of diesel pilot-injection ratio (DPR) from 0% to 60%, reaching a maximum value of 47.1% at DPR 40%. At this point, the unburned NH3 emission decreased by 42.4% compared to the DPR0%, and no longer declined after the DPR exceeded 40%. Based on AER 50% and DPR 40%, ITE peaked at 48.1% with the optimal timing for diesel pilot-injection, which represented a 1.6% rise in ITE relative to the original diesel-only combustion mode, while GHG emissions decreased by 35%, and NO emissions increased by 25%.

The effect of strategic alliances on supply chain resilience under external shocks

ABSTRACT Enterprises opt for alliances to bolster supply chain resilience (SCR) against external shocks. Yet, studies on the impact of strategic alliances on SCR from an operations management perspective are still lacking. This study develops a supply chain network (SCN) cascading failure model that accommodates the presence of strategic alliances, drawing upon complex network theory and cascading failure theory. Using numerical simulations and empirical examples, the study delves into how enterprises can effectively enhance their SCR by forming strategic alliances and appropriately allocating redundant resources. The findings reveal that: (I) Strategic alliances enhance SCR; (II) When enterprises maintain a degree of redundancy, the strengthening effect of alliances on SCR is even more pronounced, particularly under significant external shocks; and (III) In the context of cascading failures, disruptions to smaller enterprises have a more pronounced effect on SCR. Notably, weak-weak alliances contribute more to SCR than strong-strong alliances.

Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

Undrained cavity expansion-contraction analysis in CASM and its application for pressuremeter tests

Many geotechnical scenarios involve cavity unloading from a loaded state, particularly in pressuremeter tests, and the unloading data of pressuremeter tests has exceptional attraction as it is less disturbed by the insertion process. However, the analyses for continuous cavity loading and unloading (i.e., cavity initially experiences expansion and then contracts) in critical state soils are rarely studied. To this end, a novel semi-analytical solution based on the unified state parameter model for clay and sand (CASM) is proposed for the whole expansion-contraction of spherical and cylindrical cavities under undrained condition. The problem assumes that the cavity is unloaded after a monotonic loading stage, leading to plastic regions during both loading and unloading periods. The cavity response for the whole expansion-contraction process is investigated, with the total pressure and stress paths at the cavity wall presented and validated against numerical simulation. The developed solution is successfully implemented to interpret both loading and unloading data of pressuremeter tests. The undrained shear strength, in-situ effective horizontal stress and initial overconsolidation ratio are back analyzed by using a curve fitting method based on the proposed solution.

Mechanism for sound dissipation in a two-dimensional degenerate Fermi gas

We numerically study the transport properties of a two-dimensional Fermi gas in a weakly and strongly interacting regimes, in the range of temperatures close to the transition to a superfluid phase. For that we excite sound waves in a fermionic mixture by using the phase imprinting technique, follow their evolution, and finally determine both their speed and attenuation. Our formalism, originated from a density-functional theory, incorporates thermal fluctuations via the grand canonical ensemble description and with the help of Metropolis algoritm. From numerical simulations we extract temperature dependence of the sound velocity and diffusivity as well as the dependence on the interaction strength. We emphasize the role of virtual vortex–antivortex pairs creation in the process of sound dissipation.

Analysis of the Dynamical Properties of Discrete Predator-Prey Systems with Fear Effects and Refuges

This paper examines the dynamic behavior of a particular category of discrete predator-prey system that feature both fear effect and refuge, using both analytical and numerical methods. The critical coefficients and properties of bifurcating periodic solutions for Flip and Hopf bifurcations are computed using the center manifold theorem and bifurcation theory. Additionally, numerical simulations are employed to illustrate the bifurcation phenomenon and chaos characteristics. The results demonstrate that period-doubling and Hopf bifurcations are two typical routes to generate chaos, as evidenced by the calculation of the maximum Lyapunov exponents near the critical bifurcation points. Finally, a feedback control method is suggested, utilizing feedback of system states and perturbation of feedback parameters, to efficiently manage the bifurcations and chaotic attractors of the discrete predator-prey model.

Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures.

Machine learning-enabled weather forecasting for real-time radioactive transport and contamination prediction

Effective responses to nuclear accidents require accurate and fast predictions of radionuclide transport in the atmosphere. However, real-time forecasting has been challenging, since predicting the transport requires meteorological forecasting, which relies on computationally intensive numerical weather simulations. To address this challenge, we have developed statistical emulators for the Weather Research and Forecasting model (WRF) to forecast wind and temperature fields around the source location. The emulator is constructed based on pre-computed WRF simulation results and their boundary conditions from global circulation models (GCM), enabling the rapid prediction of local-scale wind/temperature fields by ingesting the GCM-predicted boundary conditions available online for the next several days. We have explored two methods: (1) deep neural network and (2) a vector autoregressive model with exogenous variables (VARX). A Gaussian plume model is then used to take these field as input and to predict the distribution of radioactive material/dose in the environment. The results show that both emulators are capable of forecasting the wind field for several days in the future in real-time, while not compromising accuracy. The autoregressive model has shown a strong performance of predicting the temperature field, owing to the strong temporal autocorrelations in the temperature data. This framework offers a powerful tool for informed decision-making during emergencies such as enabling meteorology-informed timing of containment venting, and real-time guidance of evacuation strategies.

A method determining critical operating parameters for landing aircraft based on runway pavement skid resistance

ABSTRACT Poor skid resistance of airport pavement is the critical cause of runway excursion accidents. While the current determination methods of skid resistance are limited to the independent operating parameters and the discontinuously monitoring the skid resistance, the empirically calculating critical operating parameters. In this study, an aircraft tire-fluid-airport pavement skid resistance model was developed using numerical simulation technology. The wide-body aircraft B777 was taken as an example, the available skid number (SN) was used to continuously track the skid resistance of pavement under various landing conditions. The combined effects of groove type, water film thickness and landing weight on the pavement skid resistance performance were evaluated when the pavement reached the maintenance threshold state. Results show that the skid resistance of the ungrooved pavement was subpar according to FAA. The skid resistance was positively connected with the grooved size and landing weight but negatively correlated with the water film thickness. Under certain operating conditions, the FAA maintenance threshold groove still meets the requirements for aircraft landing safety throughout the whole range of landing speeds. A real-time determination method of pavement skid resistance was proposed. It can guide pavement maintenance management departments to avoid runway excursion accidents.

Fidelity susceptibility probes of dynamical quantum criticality

Fidelity susceptibility is a quantum information-based approach that has been an effective tool for detecting and characterizing quantum phase transitions in an equilibrium setting. Motivated by the usefulness of the static counterpart, we propose that it can serve as a versatile tool to identify dynamical quantum phase transitions. Further, we develop linear-scale simulation methods for dynamical fidelity susceptibility calculations, which are based on the Chebyshev polynomial-expansion approach. We study the Aubry–André model as a benchmark for the validation of the computational technique. Numerical simulations show nonanalytic behaviors of the fidelity susceptibility in the time domain, signaling the existence of dynamical quantum phase transitions triggered by the incommensurate potential modulation of quantum quench systems. In addition, we find an excellent scaling collapse of the data onto a single curve in the limiting quench processes, which leads to a single-parameter scaling in the fidelity susceptibility. The feasibility of experimental measurement of the fidelity susceptibility opens up a new avenue toward an understanding of the nonequilibrium transport of quantum systems.

Contrasting Effects of Urban Land Cover Change and Anthropogenic Heat on Summer Precipitation Over the Yangtze River Delta of China: Analyses From an Atmospheric Moisture Budget Perspective

AbstractBoth urban land cover (ULC) change and anthropogenic heat (AH) emission are important causes of urban heat island, but their relative contributions to the changes in urban precipitation and the related mechanism remain unclear. Based on numerical simulations utilizing the latest realistic urban fraction and AH data over the Yangtze River Delta urban agglomeration, we found that ULC and AH resulted in nearly opposite effects on precipitation. Various dynamical and thermodynamic processes were involved according to the atmospheric moisture budget analyses. AH increased precipitation particularly during afternoon, and the increases were stronger during heavy precipitation events because of the enhanced moisture convergence effect together with the release of moisture storage previously accumulated in the atmosphere. Differently, ULC reduced mean precipitation mainly due to suppressed evaporation. During weak precipitation events, the suppressed evaporation was largely balanced by the intensified moisture convergence, but during heavy events, ULC caused more pronounced precipitation reduction because the moisture convergence response disappeared and failed to offset the evaporation effect. The relative contributions of different dynamical and thermodynamic processes such as those related to circulation, moisture gradient, and background moisture availability to the temporal variation in the total moisture convergence were further quantified. Overall, our results help better understand the relative roles of different aspects of urbanization on precipitation, and suggest that compared to ULC, reduction in AH emission that is tightly related to the energy consumption structure could be more efficient for mitigating the risk of extreme precipitation.

Simultaneous influence of contact angle, capillary number, and contraction ratio on droplet dynamics in hydrophobic microchannel

Abstract In droplet-based microfluidic systems, droplet motion and behavior largely depend on fluid properties, flow conditions, and microchannel geometry. This study presents a model to predict droplet dynamics within a contraction microchannel, considering various factors, such as contact angle (θ), capillary number (Ca), and contraction ratio (C), within defined ranges, through the employment of a three-dimensional numerical simulation method. Droplets may undergo trap, squeeze, or breakup as they traverse the contraction microchannel, contingent upon the aforementioned factors. The transition from trap to squeeze is determined by the critical value of Capillary number (〖Ca〗_I). Within the squeezed regime, the contact angle influences droplet deformation and velocity within the contraction microchannel. However, when capillary number (〖Ca〗_II) in contraction microchannel increases to a specific threshold corresponding to the contraction ratio, the droplet no longer in contact with the wall and becomes independent of the contact angle, a phenomenon referred to as detachment. Moreover, the contact angle's impact on the transition of droplets from squeeze to breakup is contingent upon the microchannel's contraction ratio. This study provides a comprehensive overview of droplet behavior within microfluidic systems, offering valuable insights for the optimization and design of microsystems.

Thermodiffusively unstable laminar hydrogen flame in a sufficiently large 3D computational domain – Part II: NOx formation mechanism and flamelet modeling

In this work, the NOx formation mechanism in three-dimensional (3D) thermodiffusively unstable premixed hydrogen flames is investigated through direct numerical simulations (DNS) and a reaction path analysis based on the DNS dataset. In part I of this study (Wen et al., 2024), the characteristic patterns of the instabilities were studied using the same dataset. Here, first, the effects of the computational setup (2D vs. 3D) and curvature on the flux ratio of nitrogen atom are quantified. In addition, the composition space model (CSM) proposed in previous works is extended to account for the NOx chemical reaction mechanism to predict NOx formation in thermodiffusively unstable premixed hydrogen flames. The flamelet solutions are obtained from the premixed flamelet equations in composition space so that the wide range of curvatures associated with the strongly corrugated flame front can be considered. The performance of the CSM in predicting the NOx species and the important radicals involved in the NOx formation pathways is evaluated through an a priori analysis. To investigate the effects of curvature on the NOx formation pathways and the performance of the composition space model, the positively- and negatively-curved regions in the DNS are studied separately. The results show that different from the 2D simulation, the NNH reaction pathway becomes dominant in the 3D simulation due to the increased range of curvature, which promotes the accumulation of the highly diffusive H radical in the positively-curved regions. The NNH reaction pathway is dominant in the positively-curved regions, while the N2O reaction pathway is more important in the negatively-curved regions. The flamelet model based on the composition space solutions that consider the effects of curvature yields accurate predictions for the radicals that are sensitive to the effects of curvature.Novelty and significance statementThe novelty of the present work includes the following aspects,(i) Analysis of NOx formation mechanism based on the large-scale 3D DNS dataset of a laminar fuel-lean premixed hydrogen flame on a sufficiently large domain;(ii) Quantification of the effects of computational setup (2D and 3D) and curvature on the NOx formation pathways;(iii) Extension of the previous composition space model (CSM) by incorporating the NOx chemical reaction mechanism to consider the effects of curvature on the NOx formation.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Axial mechanical response of concrete pipe jacking considering the deflection of the bell-and-spigot joint: Full-scale test and numerical simulation

The bell-and-spigot joints play a vital role in the axial mechanical response of pipe jacking. This paper investigates the influence of joint deflection on the axial stress distribution and jacking load transfer of concrete pipe jacking via full-scale tests and numerical simulation. The joint deflection contributes to the single-side eccentricity or diagonal eccentricity of the pipe segments. The increase in joint deflection influences the extension of axial stress concentration in the vertical direction near the joint, while the increase in jacking load enhances the maximum compressive stress of the pipe wall in the alignment direction. In addition, the nonlinear axial strain–stress property of the cushioning plank among the segments resulted in the nonlinear transfer of the jacking load. Moreover, the joint deflection also induces the visible tensile stress or inelastic stress of the pipe under diagonal loading mode, indicating a risk of structural damage.