Terms Of Energy Research Articles

Validation of OpenFOAM with respect to the elementary processes involved in the generation of waves by subaerial landslides

This study presents a validation of the OpenFOAM multiphase solver (i.e., multiphaseInterFoam) with respect to the elementary processes involved in the simulation of waves generated by high mobility subaerial landslide with a specific focus on the computation of energy terms. These processes include slide flow over a slope, impulse wave generation, wave dispersion, wave propagation and breaking. The simulations are conducted in 2D. The results allow to determine the minimum number of cells and the appropriate model tuning to reach acceptable accuracy while maintaining the computation time in a reasonable limit. To respect energy conservation, the choice of the turbulence model appears critical. Only, with a turbulence including a buoyancy term in the equations to account for the multiphase flow, and optimized initial values of the turbulence model parameters, could be the energy components of the flow accurately calculated. This study highlights the complexity of the phenomenon and the care with which, simulations should be conducted for the accurate computation of the energy transfers in the context of subaerial landslides.

A level-set method for fast image segmentation based on local pre-fitting and bilateral filtering

PurposeAlthough many conventional level-set approaches can be used for segmenting images containing factors such as noise and intensity inhomogeneities, they still can impact the accuracy of the results seriously. To solve this problem, a level-set method for fast image segmentation based on pre-fitting and bilateral filtering is proposed.Design/methodology/approachFirstly, an improved bilateral filter was investigated for image preprocessing. Secondly, by computing the local average intensity of the preprocessed enhanced picture, two local pre-fitting functions were defined. Thirdly, a new level-set energy functional was defined. Finally, a new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level-set function in a smooth state.FindingsThe experimental results demonstrate that the proposed model has an excellent segmentation capability for images with noise and intensity inhomogeneities and has different degrees of performance improvement compared with the mainstream models.Originality/value(C1) An improved bilateral filter was investigated and integrated into the model. (C2) Proposing two local pre-fitting functions by computing the local average intensity of the preprocessed enhanced image. (C3) Proposing a new level-set energy functional. (C4) A new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level set function in a smooth state. (C5) Analyzing and comparing the performance of the proposed model with other similar models.

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.

Energy-consistent discretization of viscous dissipation with application to natural convection flow

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the ‘implied’ presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation.Numerical experiments of Rayleigh–Bénard convection (RBC) and Rayleigh–Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids. Therefore, the proposed discretization also forms an excellent starting point for testing sub-grid scale models and is a useful tool to assess energy budgets in any turbulence simulation, with or without the presence of natural convection.

A Consistent Kinetic Fokker–Planck Model for Gas Mixtures

We propose a general multi-species Fokker–Planck model. We prove consistency of our model: conservation properties, positivity of all temperatures, H-Theorem and the shape of equilibrium as Maxwell distributions with the same mean velocity and temperature. Moreover, we derive the usual macroscopic equations from the kinetic two-species BGK model and compute explicitly the exchange terms of momentum and energy.

Emergent potentials and non-perturbative open topological strings

We show that integrating out M2 branes ending on M5 branes inside Calabi-Yau manifolds captures non-perturbative open topological string physics. The integrating out is performed using a contour integral in complexified Schwinger proper time. For the resolved conifold, this contour can be extended to include the zero pole, which we argue captures the ultraviolet completion of the integrating out and yields the tree-level polynomial terms in the free energy. This is a manifestation of the Emergence Proposal, and provides further evidence for it. Unlike the case of closed strings, where the emergent terms are kinetic terms in the action, for these open strings it is tree-level potential terms which are emergent. This provides a first quantitative example of the proposal that classical tree-level potentials in string theory emerge from integrating out co-dimension one states.

Chemical Reaction Simulator on Quantum Computers by First Quantization (II)─Basic Treatment: Implementation.

Chemical simulation is a key application area that can leverage the power of quantum computers. A chemical simulator that implements a grid-based first quantization method has promising characteristics, but an implementation fully in quantum circuits seems to have not been published. Here, we present "crsQ" (chemical reaction simulator Q), which is a quantum circuit generator that generates such a chemical simulator. The generated simulator is capable of antisymmetrization of the initial wave function and time-evolution of the wave function based on the Suzuki-Trotter decomposition. The potential energy term of the Hamiltonian is implemented using arithmetic gates, such as adders, subtractors, multipliers, dividers, and square roots. Circuit diagrams and output samples are shown. The number of qubits in the circuits scales on the order of O(η log η), where η is the number of electrons. Each component of the generated circuit was verified in unit tests. Along with this development, we designed frameworks to ease the development of large-scale circuits, namely, a temporary qubit allocation framework and an abstract syntax tree framework for arithmetic formulas. These frameworks are expected to be useful in large-scale quantum circuit generators.

Measuring the solubility of tacrolimus in supercritical carbon dioxide (binary and ternary systems), comparing the performance of machine learning models with conventional models

The solubility of tacrolimus in a supercritical carbon dioxide (SC-CO2) with and without co-solvent (ternary and binary system) was investigated. The experimental conditions include the temperatures of 308 to 338 K and the pressures of 12 to 30 MPa were considered. The values of tacrolimus solubilization in SC-CO2 in binary and ternary systems were from 0.416 × 10−5 to 1.589 × 10−5 and from 0.389 × 10−4 to 0.875 × 10−4, respectively. The obtained results presented that adding a co-solvent (ethanol) could affect tacrolimus solubility significantly, the mole fraction of tacrolimus in SC-CO2/co-solvent under all experimental conditions was at least 4.9 more than that of SC-CO2 alone. The correlation of the solubility data was achieved by three distinct methods: (1) SRK EoS with van der Waals (vdW2) and Wong-Sandler (WS) as mixing rules, (2) solution theory, (3) empirical and (4) machine learning based models. The analysis of variance (ANOVA) results showed that Sparks et al. (AIC −817.05, and AARD 5.08 %) and Bian et al. (AIC −815.77 and AARD 5.17 %) models could correlate the experimental data of tacrolimus in SC-CO2 with more accuracy, as compared to the other empirical models. Sodeifian and Sajadian model with AARD% and AIC of 2.99 and −726.22, respectively, has acted as the most accurate empirical model in ternary system. The solution theory was able to calculate the solubility for the tacrolimus-SC-CO2-ethanol system with high accuracy (AARD, % 4.85, AIC −701.41, F value 169.70 and Radj 0.9622). Also, the Gradient Boosting model (AARD accurately predicted the outcomes with perfect accuracy for binary and ternary systems. Finally, total (ΔHtotal, 23.21 cal/mol), solvation (ΔHsol, −19.47 cal/mol) and vaporization (ΔHvap, 42.68 cal/mol) enthalpies were obtained by using an energy term in Chrastil and Bartle models.

Improving turbulence modeling for gas turbine blades: A novel approach to address flow transition and stagnation point anomalies

Accurate prediction of temperature and Heat Transfer Coefficient (HTC) distributions over gas turbine blades is crucial for the design process and life assessment of these components. Numerical studies of flow over gas turbine blades face significant challenges in accurately simulating two complex phenomena: (1) the transition of flow from laminar to turbulent, and (2) stagnation point flow at the leading edge. Many turbulence models tend to overpredict the temperature on turbine blades, leading to incorrect identification of hot-spot regions and, consequently, erroneous estimations of blade life. This paper investigates the performance of various turbulence models in simulating flow and heat transfer over gas turbine vanes. The study includes three full turbulence models, i.e., Spalart-Allmaras (SA), Shear Stress Transport k−ω (SST-kw), and v2−f (V2F), as well as two transitional models, i.e., Transition SST (Trans-SST) and k−kL−ω (k-kl-w). Simulation results indicate that the v2−f, Trans-SST, and k−kL−ω models can detect flow transition. However, the transition length and onset location predicted by the Trans-SST and k−kL−ω models do not align with experimental data. Conversely, the v2−f model suffers from over-predictions at the leading edge due to stagnation point anomaly. To address these issues and due to capacities of the V2F model, this study proposes two modifications to enhance the performance of the V2F model. First, the production term of turbulent kinetic energy is redefined to mitigate the stagnation point anomaly. Second, the model is recalibrated to improve the prediction of flow transition. The new model, named the Production Modified V2F (PMV2F) model, shows promising results in predicting temperature and heat transfer coefficients.

The influence of renewable energy and financial development on testing the environmental Kuznets curve in Lebanon: ARDL approach

This study considers the impacts of financial development and the consumption of renewable energy in Lebanon for the period 1990–2021, employing the Environmental Kuznets Curve. The financial sector in Lebanon is considered a major engine in the economic development. Green energy sources and environmental protection are taking higher importance nowadays with the increase of implications for climate change and global warming worldwide. This paper examines the Environmental Kuznets Curve’s presence and implications for Lebanon’s financial development and renewable energy consumption. The econometric model used annual data from the World Development Indicators. Utilizing the autoregressive distributed lag (ARDL) technique, both near- and long-term relationships were estimated. The findings support the Environmental Kuznets Curve hypothesis and show that energy consumption and real income have a statistically significant beneficial effect on carbon emissions and that their square has a statistically significant negative impact on carbon emissions over the long and short term. The results show variations in signs for financial development between the short and long term and stable results for renewable energy with negative signs in both terms. These results show the importance of further research on the influence of financial development and green energy consumption on EKC. Therefore, policymakers need to pay more attention to these variables for a sustainable economy that is facing the effects of climate change.

Be-dataHIVE: a base editing database

Base editing is an enhanced gene editing approach that enables the precise transformation of single nucleotides and has the potential to cure rare diseases. The design process of base editors is labour-intensive and outcomes are not easily predictable. For any clinical use, base editing has to be accurate and efficient. Thus, any bystander mutations have to be minimized. In recent years, computational models to predict base editing outcomes have been developed. However, the overall robustness and performance of those models is limited. One way to improve the performance is to train models on a diverse, feature-rich, and large dataset, which does not exist for the base editing field. Hence, we develop BE-dataHIVE, a mySQL database that covers over 460,000 gRNA target combinations. The current version of BE-dataHIVE consists of data from five studies and is enriched with melting temperatures and energy terms. Furthermore, multiple different data structures for machine learning were computed and are directly available. The database can be accessed via our website https://be-datahive.com/ or API and is therefore suitable for practitioners and machine learning researchers.

Fixed-Magnetization Ising Model with a Slowly Varying Magnetic Field

The motivation for this paper is the analysis of the fixed-density Ising lattice gas in the presence of a gravitational field. This is seen as a particular instance of an Ising model with a slowly varying magnetic field in the fixed magnetization ensemble. We first characterize the typical magnetization profiles in the regime in which the contribution of the magnetic field competes with the bulk energy term. We then discuss in more detail the particular case of a gravitational field and the arising interfacial phenomena. In particular, we identify the macroscopic profile and propose several conjectures concerning the interface appearing in the phase coexistence regime. The latter are supported by explicit computations in an effective model. Finally, we state some conjectures concerning equilibrium crystal shapes in the presence of a gravitational field, when the latter contributes to the energy only to surface order.

Optimization of Low-Pressure Turbine Blade by Means of Fine Inspection of Loss Production Mechanisms

Abstract In this study, Proper Orthogonal Decomposition (POD) was applied to Large Eddy Simulations (LES) of two high-loaded low-pressure turbine cascades under unsteady inflow conditions to investigate entropy production within different parts of the blade passage. The terms for Turbulent Kinetic Energy (TKE) production, diffusion, and dissipation from the stagnation pressure transport equation were integrated across the computational domain. The POD-based method enables the decomposition of contributions from various coherent flow dynamics to TKE production and dissipation, such as the migration, bowing, tilting, and reorientation of incoming wake filaments, as well as the breakdown of streaky structures in the blade boundary layer and the formation of Von Karman vortices in the blade wake. This approach helps designers identify the dominant POD modes (i.e., turbulent flow structures) responsible for loss generation, understand their dynamics and locate where they primarily act. Using this optimization strategy, a new low-pressure turbine profile was designed. LES calculations on the optimized geometry showed that it is possible to design a higher-loaded profile with a lower global loss coefficient. The new loading distribution, characterized by stronger acceleration in the early part of the blade passage, results in reduced upstream wake migration losses and lower TKE production in the trailing edge wake zone due to early suction side boundary layer transition.

Long-time behaviors of wave equations stabilized by boundary memory damping and friction damping

In this paper, we study the long-time behaviors of wave equations subject to boundary memory damping and friction damping. Different from assumptions that memory kernel is a nonnegative, monotone function in the previous literatures, we assume that the primitive of the memory kernel is a generalized positive definite kernel (abbreviated to GPDK), which may vary sign or oscillate. The key to the problem lies in establishing the connection between memory damping and energy terms. By combining the properties of the positive definite kernel with classical multiplier methods, and constructing auxiliary systems, we ultimately establish the asymptotic stability, exponential stability and polynomial stability of systems featuring boundary memory damping and friction damping. To illustrate our theoretical results, we provide some numerical simulations.

Phase Field Study of Discontinuous Precipitation in a Miscibility Gap System

AbstractA phase field model is developed to study the discontinuous (cellular) precipitation (DP) reaction in a hypothetical A-B miscibility gap system. Unlike previous treatments, the model employs an interaction energy term which couples the composition and the phase field variable to enable the necessary grain boundary movement. The influence of factors such as interaction energy, interfacial mobility and grain boundary diffusivity on the transformation rate and overall microstructure evolution are discussed.

Practical twin-field quantum key distribution parameter optimization based on quantum annealing algorithm

Abstract Twin-field quantum key distribution (TF-QKD) is widely studied since it can surpass the key capacity of repeaterless QKD, whereas electromagnetic interference (EMI) is one of the main challenges in its practical applications. This study is based on the Faraday–Michelson TF-QKD. Analyze the effect of EMI on the rotation angle of the Faraday mirror causing an additional quantum bit error rate (QBER). Moreover, the quantum annealing algorithm (QA) based on quantum tunneling mechanism is applied to the optimization of practical TF-QKD. Mapping the secure key rate of TF-QKD into the evaluation function of QA and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect. Meanwhile, the QA is improved in terms of chaotic optimization to obtain the dynamic initial value of the algorithm, the design of a perturbation method to skip the locally optimal solution, and the use of suitable temperature and magnetic field decay functions. Optimizing TF-QKD with QA, the standard deviation of QBER fluctuation caused by EMI is reduced to 0.206, the mean square error of the signal-state pulse intensity is only 3.38 × 10 − 4 , and the optimization accuracy of the secure key rate can reach 99.8 % . Additionally, this optimization method shortens the runtime and reduces computational resource consumption, making it highly efficient for practical implementations.

4+2]‐Cycloaddition Reactions to Corannulene Accelerated by η6‐Coordination of Ruthenium Complexes

AbstractThe influence of different (LRu)nII (L=Cp– (n=1, 2); C6H6) moieties η6‐coordinated to corannulene ((CpRu)+, (Benzene)Ru2+ and (CpRu)22+) has been explored by using the Activation Strain Model (ASM) of reactivity together with the Energy Decomposition Analysis (EDA) scheme. The results obtained have been compared with those obtained for the non‐coordinated corannulene counterpart. It has been observed that the presence of the ruthenium moiety favors both kinetically and thermodynamically the [4+2]‐cycloaddition reaction with cyclopentadiene. The factors governing these differences depend on the nature of the [Ru] complex. Whereas the interaction energy is solely responsible for the lower activation energy for the monocationic system (CpRu)+, not only the higher interaction but also the lower strain energy explain the much higher reactivity observed for the dicationic systems ((C6H6)Ru2+ and (CpRu)22+). In this latter case, the Pauli repulsion energy term is the factor controlling the differences in the interaction energies, following the so‐called Pauli repulsion‐lowering concept. Finally, when η6‐coordinating mono‐ or dicationic ruthenium complexes, although the [4+2]‐cycloaddition reaction in the external rim bonds remains the thermodynamically and kinetically more favourable, the cycloaddition becomes also thermodynamically feasible in some internal bonds.

Coupled crystal plasticity-phase field simulation of twin-twin interaction in magnesium

Twin-twin interactions significantly influence the mechanical properties of magnesium and its alloys. A thorough understanding of the underlying mechanisms governing these interactions is essential for designing Mg alloys with enhanced strength and toughness. In this work, a crystal plasticity-twin coupled phase field (CP-TPF) model incorporating multiple extension twin variants and considering the role of dislocation slipping is proposed to investigate the interactions between/among the same twin variants and those between co-zone twin variants in Mg single crystals. The model incorporates an additional energy term to represent the interaction among different twin variants and couples the CP and TPF models through order parameters and stress tensor. The simulated results show that the interaction between the same twin variants can either promote or inhibit the twin propagation, and multiple twins tend to generate concurrently in Mg single crystal to minimize the free energy associated with the accumulation of elastic strain. During co-zone twin-twin interaction, localized thickening of the recipient twin occurs due to the concentrated stresses induced by the intrusion twin, and the mutual extrusion of the two twins leads to blunting of the intrusion twin tip. Both the coalescence of the same twin variants and the formation of twin-twin boundaries between the co-zone twin variants contribute to the effective mechanism of twinning-induced hardening. Moreover, local dislocation accommodation plays a crucial role in twin-twin interactions. It relaxes the stress concentration near the twin tips and twin-twin boundaries and significantly contributes to the uneven migration of the twin boundary.

Submesoscale processes and their seasonality generated by freshwater discharge at the Yangtze estuary

As the intersection between land and sea, the estuary is a hotpot for natural and human-induced processes that affect its physical and chemical characteristics, as well as biodiversity and ecosystems. Due to a combination of freshwater discharge and the unique topography of river estuaries, submesoscales with a spatial scale of O(1) km can be easily generated due to the presence of strong salinity fronts at river estuaries. As the biggest river in Asia, the basically dynamical characteristics and their seasonal mechanisms of submesoscales at the Yangtze estuary remain unclear. Using a nested high-resolution (300 m) simulation established by the Coastal and Regional Ocean Community model (CROCO), the seasonal variability of submesoscales at the Yangtze estuary is investigated and the underlying generation mechanisms are analyzed based on the analysis of kinetic energy budget. The results of normalized relative vorticity, horizontal salinity gradient, and submesoscale kinetic energy (SMKE) show that submesoscale processes have a seasonal variation at the Yangtze estuary. This seasonality is tightly related to the freshwater input with a correlation coefficient of up to 0.66 (the confidence level >99%). To figure out the dominant energy sources that control the SMKE budget, five terms including the baroclinic term (BC), the barotropic term (BT), the advection of submesoscale kinetic energy term (ADK), the pressure work term (PW), and vertical mixing term (VM) are diagnosed. The results suggest that BC, BT, and PW are the source producing SMKE, while ADK and VM tend to dissipate SMKE. This work suggests the important role of submesoscale activities in the energy cascade and enhancing the transports of tracers at the estuary.

Computational insights into inhibiting EphA2: Integrating structure-based virtual screening, docking, and molecular dynamics simulations for small molecule discovery.

Elevated expression and dysfunction of ephrin type A receptor-2 (EphA2) have been implicated in the initiation and progression of cancer, metastasis, and unfavorable clinical outcomes. A promising strategy to counteract this dysregulation involves the development of small-molecule inhibitors that target EphA2. Our study focuses on this objective. To initiate Structure-Based Virtual Screening (SBVS), we leveraged an advanced online platform, the Mcule database, which houses an extensive collection of millions of chemical compounds. Using drug similarity filters, we efficiently identified ten thousand potential hits. By further refining the selection through toxicity profiling, we prudently narrowed down the candidates to a more manageable set of 100 molecules. Using the Mcule Single Click, DockThor, and SwissDock tools, we conducted multi-scoring docking assessments of thirty-seven compounds that satisfied the ADME standards. A comprehensive evaluation of Gibbs binding free energy terms, as derived from these docking tools, facilitated the identification of top-ranking docking hits. Remarkably, among the known inhibitors, dasatinib displayed the most robust binding to EphA2 with an average ΔG of -9.0 kcal/mol. Intriguingly, alternatives have emerged in recent years. Notably, small molecules such as Mcule-1579910267 (ΔG: -9.3 kcal/mol), Mcule-1893218381 (ΔG: -9.2 kcal/mol), Mcule-3981378344 (ΔG: -9.3 kcal/mol), and Mcule-8617639093 (ΔG: -9.1 kcal/mol) exhibited a notably strong binding affinity to EphA2, rivaling dasatinib. Subsequently, the four leading ligands along with dasatinib were selected for the MD simulations. Our rigorous analyses during the MD simulation phase encompassing RMSD, RMSF, SASA, ΔGsolv, and Rg underscored the favorable stability of Mcule-8617639093. This compelling evidence ultimately signifies the potential for selective EphA2 inhibition.