Simulation Approach Research Articles

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

Identification of potential marine bioactive compounds from brown seaweeds towards BACE1 inhibitors: molecular docking and molecular dynamics simulations approach

Identification of potential marine bioactive compounds from brown seaweeds towards BACE1 inhibitors: molecular docking and molecular dynamics simulations approach

Concurrent inhibition of IR, ITGB1, and CD36 perturbated the interconnected network of energy metabolism and epithelial-to-mesenchymal transition in breast cancer cells.

Altered energy metabolism is an emerging hallmark of cancer and plays a pivotal in cell survival, proliferation, and biosynthesis. In a rapidly proliferating cancer, energy metabolism acts in synergism with epithelial-to-mesenchymal transition (EMT), enabling cancer stemness, dissemination, and metastasis. In this study, an interconnected functional network governing energy metabolism and EMT signaling pathways was targeted through the concurrent inhibition of IR, ITGB1, and CD36 activity. A novel multicomponent MD simulation approach was employed to portray the simultaneous inhibition of IR, ITGB1, and CD36 by a 2:1 combination of Pimozide and Ponatinib. Further, in-vitro studies revealed the synergistic anticancer efficacy of drugs against monolayer as well as tumor spheroids of breast cancer cell lines (MCF-7 and MDA-MB-231). In addition, the combination therapy exerted approximately 40% of the apoptotic population and more than 1.5- to 3-fold reduction in the expression of ITGB1, IR, p-IR, IRS-1, and p-AKT in MCF-7 and MDA-MB-231 cell lines. Moreover, the reduction in fatty acid uptake, lipid droplet accumulation, cancer stemness, and migration properties were also observed. Thus, targeting IR, ITGB1, and CD36 in the interconnected network with the combination of Pimozide and Ponatinib represents a promising therapeutic approach for breast cancer.

Subtractive proteomics-guided vaccine targets identification and designing of multi-epitopes vaccine for immune response instigation against Burkholderia pseudomallei.

In this study, a methodical workflow using subtractive proteomics, vaccine designing, molecular simulation, and agent-based modeling approaches were used to annotate the whole proteome of Burkholderia pseudomallei (strain K96243) for vaccine designing. Among the total 5717 proteins in the whole proteome, 505 were observed to be essential for the pathogen's survival and pathogenesis predicted by the Database of Essential Genes. Among these, 23 vaccine targets were identified, of which fimbrial assembly chaperone (Q63UH5), Outer membrane protein (Q63UH1), and Hemolysin-like protein (Q63UE4) were selected for the subsequent analysis based on the systematic approaches. Using immunoinformatic approaches CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), IFN-positive, and B cell epitopes were predicted for these targets. A total of 9 CTL epitopes were added using the GSS linker, 6 HTL epitopes using the GPGPG linker, and 6 B cell epitopes using the KK linker. An adjuvant was added for enhanced antigenicity, an HIV-TAT peptide for improved delivery, and a PADRE sequence was added to form a 466 amino acids long vaccine construct. The construct was classified as non-allergenic, highly antigenic, and experimentally feasible. Molecular docking results validated the robust interaction of MEVC with immune receptors such as TLR2/4. Furthermore, molecular simulation revealed stable dynamics and compact nature of the complexes. The binding free energy results further validated the robust binding. In silico cloning, results revealed GC contents of 50.73 % and a CIA value of 0.978 which shows proper downstream processing. Immune simulation results reported that after the three injections of the vaccine a robust secondary immune response, improved antigen clearance, and effective immune memory generation were observed highlighting its potential for effective and sustained immunity. Future directions should encompass experimental validations, animal model studies, and clinical trials to substantiate the vaccine's efficacy, safety, and immunogenicity.

Power-law frequency-dependent Q simulations in viscoacoustic media using decoupled fractional Laplacians

Quantifying the seismic attenuation of wave propagation in the earth’s interior is essential for studying subsurface structures. Previous approaches for attenuation simulations (e.g., the standard linear solid and the fractional derivative model) are mainly based on the frequency-independent quality factor Q assumption. However, seismic attenuation in high-temperature and high-pressure regions usually exhibits power-law frequency-dependent Q characteristics. To simulate this Q effect in attenuative media, we derive a new viscoacoustic wave equation with decoupled fractional Laplacians in the time domain. Unlike the existing methods using relaxation functions to fit the power-law relationship in a specific frequency band, our equation is directly derived from the approximated complex modulus, which explicitly involves the reference quality factor and fractional exponent parameters. Furthermore, this equation contains two fractional Laplacians, which can easily simulate decoupled amplitude dissipation and phase distortion effects, making it amenable to Q-compensated reverse time migration. In the implementation, a Taylor-series expansion and a pseudospectral method are introduced to solve the fractional Laplacians with variable fractional exponents. Numerical experiments demonstrate the effectiveness of our method for power-law frequency-dependent Q simulations. As a forward-modeling engine, our derived viscoacoustic wave equation is a good supplement to the current Q simulation methods and it could be applied in many seismic applications, such as Q-compensated reverse time migration and full-waveform inversion.

Experimental and numerical simulation investigation of the deformation characteristics of vertical boreholes under non-uniform horizontal principal stress

To study borehole deformation under non-uniform horizontal principal stress in the deep strata, a prediction method for horizontal principal stress was developed based on the morphological parameters of boreholes, the deformation trajectory equation for the standard circular borehole was derived based on elasticity theory, and the morphological characteristics of boreholes were analyzed. Additionally, a quantitative relationship between the geometric parameters of elliptical boreholes and horizontal principal stress was established. Subsequently, uniaxial tests on borehole deformation were conducted to verify elliptical deformation under non-uniform horizontal principal stress. A combined deductive, experimental, and numerical simulation approach to borehole deformation analysis was adopted, and the impact factors of borehole deformation were obtained. The results indicated as following: (1) the deformation morphology of borehole under non-uniform horizontal principal stress was elliptical; (2) for the given lithology, the greater the difference in horizontal principal stress, the greater were the ellipticity and elliptical deformation of borehole; (3) for given stress background, rock strength was inversely proportional to ellipticity. Additionally, the smaller the Young’s modulus and compressive strength, the larger was the Poisson’s ratio and the larger was the ellipticity. For example, the ellipticity of mudstone and coal was greater than that of limestone and sandstone; (4) with an increase in load, the displacement of borehole wall exhibited three stages: initial micro-deformation, accelerated deformation, and stable deformation; (5) horizontal principal stress can be calculated by using the morphological parameters (long and short axes) of an elliptical hole. Furthermore, a horizontal principal stress method theory can be developed based on the morphological parameters of boreholes. The results of our study can provide new ideas and methods for the measurement of in situ stress in deep boreholes and a theoretical basis for the development of equipment for measuring elliptical boreholes.

Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics

Abstract Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation including computational fluid dynamics (CFD) is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics such as temperature, velocity, and pressure without using any training data on velocity. This approach avoids solving numerically the non-linear Navier-Stokes equation, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model.

Numerical and analytical investigation of thermal hydraulic behavior of large pools in passive heat exchangers

Passive residual heat removal safety systems are a modern design concept employed in Nuclear Power Plants (NPPs), and operate based on natural circulation. Several passive safety systems such as the emergency condensers (EC) use heat exchanger tube bundles immersed in large pools of coolant acting as a heat-sink (secondary side). The primary side of the heat exchanger is connected to the reactor core and, when activated, removes the decay heat in case of an accident. Recent research revealed the limitation of the state-of-the-art heat transfer models to capture the heat transfer rates achieved in these systems. The problems can be identified in both – primary and the secondary sides. The main focus of this study is to investigate the thermal hydraulic behavior of the secondary side of an EC. The data from the NOKO test facility has been considered as the validation base of this study. It has been observed that strong temperature stratification establishes in the pool due to the heat transfer process happening in a confined volume zone. Under this condition, the saturation temperature is reached at higher elevations of the tank while lower the liquid remains subcooled. This leads to different thermal behavior of the liquid at different elevations in the tank which is not properly captured by simple models. In addition, due to the complexity of the heat transfer process the required computational power is another challenge to study the thermal hydraulic behavior of the system. Therefore, the main aim of this study was to correctly predict the temperature stratification in the pool and identify boiling onset at different locations specially on the heated surfaces, while maintaining minimum numerical complexity. To achieve these targets, 2D multi-phase CFD simulations of secondary side using two different frameworks was conducted using ANSYS Fluent. Then, the results were validated against experimental data to assess their accuracy to achieve the best simulation approach.

Accurate prediction of aging effects on microstructure evolution and related mechanical strength of Mg-Zn alloys via multiscale simulations

Age strengthening is a common method to achieve superior mechanical properties employed in magnesium (Mg) alloys. However, the experimental approaches to examine the aging effects on mechanical properties are primarily based on trial and error, posing challenges for the design and development of high-strength Mg alloys. A promising solution to address this challenge is to establish an accurate prediction model for the aging effects on Mg alloys, covering the evolution of microstructure and corresponding mechanical properties. This paper introduces a multiscale simulation approach to accurately predict the aging effects on the Mg-2.3Zn (at.%) alloy. To begin, we establish a phase field model to simulate the microstructure evolution at 150 ℃, demonstrating precipitate growth during aging in alignment with experimental observations. Secondly, based on the microstructures obtained from phase field simulations, the corresponding yield strength is determined through finite element method simulations. The simulation successfully captures the peak-aged behavior of yield strength during aging, resulting in a yield strength of 167 MPa at the peak-aged state, with a deviation of less than 5 % from experimental results. In-depth analysis indicates that the peak value of total yield strength arises from the interplay between solid solution strengthening and precipitate strengthening. Furthermore, precipitate strengthening reaches a peak value at a specific aging time, deviating from the total yield strength. This deviation is primarily attributed to the effect of precipitate size on shear strengthening during aging. This study presents a general method for predicting the microstructure evolution and corresponding mechanical properties of alloys through multiscale simulations, offering valuable insights for designing and predicting the performance of metallic alloys.

Molecular Dynamic Simulation and 3d-pharmacophore Modeling of Alpha Mangostin and Its Derivatives against Estrogen Alpha Receptor

Background: Human estrogen receptor alpha (ERα), which is known to play a role in mediating cell proliferation, metastasis, and resistance to apoptosis, is one of the targets of breast cancer therapies. Alpha mangostin (AM) is an active xanthone compound from Garcinia mangostana L. which has activity as an ERα inhibitor. Objectives: This research aims to predict the pharmacokinetic and toxicity, and to study the molecular interactions of AM derivatives with the ERα using computer-aided simulation approaches through molecular docking, molecular dynamic, and pharmacophore screening to develop novel anti-breast cancer agents. Methods: Marvinsketch and Chimera programs were used to design and optimize the structure of AM and its derivatives. For screening the pharmacokinetic and toxicity profiles, the PreADMET web was used. The AutoDockTools 1.5.6 and LigandScout 4.4.3 Advanced software were used to conduct the molecular docking simulation and pharmacophore screening, respectively, while the molecular dynamic simulation was performed using AMBER 16. The results were visualized by Biovia Discovery Studio. Results: Molecular docking using Autodock showed that FAT10 derivate has lower binding free energy (ΔG) (-12.04 kcal/mol) than AM (-8.45 kcal/mol) when docking to ERα and both performed the same hydrogen bond with Thr347. These support the results of the MMPBSA calculation on dynamic simulation which shows FAT10 (-58.4767 kcal/mol) has lower ΔG than AM (-42.7041 kcal/mol) and 4-OHT (- 49.0821 kcal/mol). The pharmacophore screening results also showed that FAT10 fitted the pharmacophore with a fit score of 47.08. Conclusion: From the results, it can be suggested that FAT10 has promising activity as ERα antagonist. Further in vitro and in vivo experiments should be carried out to support these in silico studies.

Spatial sensitivity distribution assessment and Monte Carlo simulations for needle-based bioimpedance imaging during venipuncture using the finite element method.

Despite being among the most common medical procedures, needle insertions suffer from a high error rate. Impedance measurements using electrode-equipped needles offer promise for improved tissue targeting and reduced errors. Impedance visualization usually requires an extensive pre-measured impedance dataset for tissue differentiation and knowledge of the electric fields contributing to the resulting impedances. This work presents two finite element simulation approaches for both problems. The first approach describes the generation of a multitude of impedances with Monte Carlo simulations for both, homogeneous and inhomogeneous tissue to circumvent the need to rely on previously measured data. These datasets could be used for tissue discrimination. The second method describes the simulation of the spatial sensitivity distribution of an electrode layout. Two singularity analysis methods were employed to determine the bulk of the sensitivity within a finite volume, which in turn enables consistent 3D visualization. The modeled electrode layout consists of 12 electrodes radially placed around a hypodermic needle. Electrical excitation was simulated using two neighboring electrodes for current carriage and voltage pickup, which resulted in 12 distinct bipolar excitation states. Both, the impedance simulations and the respective singularity analysis methods were compared with each other. The results show that the statistical spread of impedances is highly dependent on the tissue type and its inhomogeneities. The bounded bulk of sensitivities of both methods are of similar extent and symmetry. Future models should incorporate more detailed tissue properties such as anisotropy or changing material properties due to tissue deformation to gain more accurate predictions.

Effectiveness of physical soil and water conservation measures under simulated and observed dataset in Ethiopia: Meta‐analysis

AbstractTo combat land degradation through conserving soil and water resources, Ethiopia has undergone vigorous implementation of soil and water conservation (SWC) measures, predominantly physical structures. To evaluate the effectiveness of these implemented structures, various researches have been conducted through using both measured and simulated approaches. This study was initiated to analyze the reliability of SWAT‐based simulated studies results on the effectiveness of SWC measures toward erosion reduction potential against the measured dataset using unpaired t‐test statistical analysis. In this study, 123 published papers were downloaded, of which 70 were found to be more related to the objective of the review. After applying four refining criteria, only 43 were found more compatible with the study objective and used for data analysis. Studies conducted by different scholars show up to 98% and 93.50% soil loss reduction effectiveness by SWC measures in measured and simulated approaches, respectively. The simulated SWC measure efficiency output was also evaluated with respect to the ground‐measured data of the same structure within the same agroecology class. Finally, the unpaired t‐test results indicated that the grass strip in the mid‐highland agroecology showed a significant difference from the measured one at a 95% significance level. However, none of the other measures showed significant differences between the measured and simulated datasets. Hence, the SWAT model can effectively simulate the effectiveness of physical SWC measures on soil loss reduction if it is well‐calibrated and validated with sediment yield data. Refining model parameters that can be sufficiently captured and represent biological measures (grass strip), incorporating additional field data for calibration and validation coupled with exploring alternative modeling approaches that efficiently simulate biological SWC was set as a recommendation to obtain an effective biological measure simulation.

Synthesis of Flurbiprofen Based Amide Derivatives as Potential Leads for Diabetic Management: In Vitro α‐glucosidase Inhibition, Molecular Docking and DFT Simulation Approach

Synthesis of Flurbiprofen Based Amide Derivatives as Potential Leads for Diabetic Management: <i>In Vitro</i> α‐glucosidase Inhibition, Molecular Docking and DFT Simulation Approach

Gold-based housing financing model: proposing an alternative housing financing model for Islamic bank

Purpose This paper aims to propose a new housing finance mechanism through gold price as an alternative to interest rate in Islamic home financing, especially on Bai’Bithaman Ajil (BBA) contract. Design/methodology/approach This study using simulation approach to calculate the monthly installments for home financing using gold price references. In simple terms, propose a financing formula in the BBA contract by converting the selling price of the house to the gold price, and then the monthly installments also follow the actual gold price. The authors provide an example by simulating this formula using historical data and cases of housing financing at Indonesian Islamic banks. The authors compare housing financing models based on gold prices and interest rates. Finally, The authors can compare the two housing financing models that are affordable for low-income people. Findings The results show that in the initial period, monthly installments of BBA based on gold price were lower than home financing based on interest rate. This result makes it possible for low-income people who cannot access financing based on interest rates to access financing based on gold price. However, the total installments of financing based on gold prices are higher than the financing model based on interest rates. Research limitations/implications The paper confines one contract, namely, BBA, as it is claimed to be more Shariah-compliant than others. Practical implications These findings suggest an alternative model for Islamic banks and regulatory authorities in Indonesia to replace the interest rate reference with the gold price in BBA contract housing financing. This model can offer competitive advantages for Islamic banks, including lower initial installments and inflation-protected profits, serving as a means of differentiating them from conventional banks. Social implications Gold price-based housing financing model in Islamic banks will increase the affordability of housing financing for low-income people. Originality/value This paper tries to solve two problems, namely, first, the problem of assuming that Islamic and conventional banks are the same, and second, the problem of housing finance affordability. This study needs to be explored.

Development and Validation of a Novel Zero-Dimensional Heat Rejection Model for High-Efficiency Engines

In recent years, the trend towards the performance maximization of modern internal combustion engines has led to the creation of accurate simulation models to optimize the engine design and operating conditions. Temperature management is crucial to achieve the performance goals of an internal combustion engine without affecting the component’s reliability. Formula 1 mandates that only a limited number of experimental tests can be performed, which leads to the necessity of simulators capable of substituting empirical tests. Furthermore, the requirement of adapting the vehicle setup before each race weekend to maximize the performance on each circuit layout necessitates short computational time. To address this, the development of a zero-dimensional model of the thermal flows within an engine is presented in this paper. This model allows to precisely compute the dynamic variations of all the heat flows inside the combustion engine, excluding only the radiative ones and the engine components’ temperatures. The new simulation approach has been developed and validated on a Formula 1 engine and shown to be precise and fast. The results demonstrate the value of the proposed model with an average engine fluid temperature error of less than 1 °C for a computational cost comparable with on-board applications.

Approaches for Fast Brownian Dynamics Simulation with Constraints

Constraints can be used to eliminate quickly fluctuating degrees of freedom in dynamical systems enabling solutions that resolve the behavior of the system with fewer time steps over long time scales. In Brownian dynamics models, these constraints lead to linear equations which represent saddle point problems describing the velocity tangent to the constraint manifold and the forces resulting from imposition of the constraint on the dynamics. Simulations of these constrained systems must solve these saddle point problems, which are often ill-conditioned and require clever preconditioning in order to facilitate large scale simulation using iterative methods like GMRES. In this work, we show that the projected conjugate gradient (PrCG) method, which applies conjugate gradients projected onto the tangent space of the constraint manifold, has certain advantages compared to preconditioned iterative methods like GMRES, without the need to devise and compute a preconditioner. Specifically, for simulations of beads interacting hydrodynamically with commonly encountered holonomic constraints, PrCG exhibits linear scaling with the number of beads similar to GMRES. However, because PrCG does not require a preconditioner it often requires less precomputational effort both in terms of computational complexity and storage, depending on the nature of the constraints. Furthermore, PrCG produces iterates which are always feasible, allowing for independent tuning of the error tolerance on the bead velocities from the error tolerance on the constraint equations. We demonstrate PrCG's efficiency by solving several example low Reynolds number systems involving rigid bodies, freely jointed chains, and immobile particles, as well as a mixed constraint problem with both a rigid body and immobile particles, comparing to GMRES and analytical solutions, when available.

Analysis of the Drag Reduction Performance and Rheological Properties of Drag-Reducing Additives

In the practical application of hydraulic rotating machinery, it is essential to thoroughly explore drag reduction and rheological characteristics of drag-reducing additives to optimize machinery efficiency and reduce equipment consumption. This paper combines simulation and experimental approaches to investigate the drag-reduction performance and rheological properties of drag-reducing additives. Numerical simulations are initially conducted to investigate the shear-thinning properties of drag-reducing fluid and explore variations in drag-reduction rate. Turbulent phenomena characteristics are described by analyzing turbulent statistical quantities. Subsequently, the rheological behaviors of polyethylene oxide (PEO), cetyltrimethyl ammonium chloride (CTAC), and their mixed solutions under different conditions are scrutinized using a rotational rheometer. The findings indicate that the drag reduction effect amplifies as the rheological index n and characteristic time λ decrease. The numerical simulations show a maximum drag reduction rate of 20.18%. In rheological experiments, a three-stage viscosity variation is observed in single drag-reducing additives: shear thickening, shear thinning, and eventual stabilization. Composite drag-reducing additives significantly reduce the apparent viscosity at low shear rates, thereby strengthening the shear resistance of the system.

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Insights into the dynamic symphony: a comprehensive computational exploration of pure and fluorinated 6O.6 liquid crystals with the help of DFT analysis

ABSTRACT This article is based on the Density Functional Theory (DFT) simulations approach to examine the structural and vibrational behaviour of pure 6O.6 and fluorinated 6O.6 liquid crystalline compounds. The molecular interactions of the molecules were investigated by their optimised molecular structure. Identification of significant functional groups and their dynamic behaviours are made with the help of vibrational frequencies obtained through DFT calculations. Vibrational Energy Distribution Analysis (VEDA) software was used to perform the Potential Energy Distribution (PED) function of FT-IR and Raman spectra. Furthermore, changes in vibrational peaks provide insight into structural modifications. This investigation mainly focused on analysing the different reactivity, thermodynamic parameters and non-linear optical (NLO) properties of the compounds. This also helps to open a new path for further investigations into optoelectronic applications and molecular dynamics.