Ensemble Average Method Research Articles

High-speed imaging and quantitative analysis of nonequilibrium stochastic processes using atomic resolution electron microscopy

Abstract Chemical phenomena are inherently complex and stochastic, making them difficult to fully understand using conventional ensemble-averaged analytical methods. These methods primarily capture long-lived species and common structural features, limiting the study of transient intermediates and minute structural characteristics. In contrast, single-molecule time-resolved analysis using advanced microscopy techniques, particularly transmission electron microscopy, offers high spatial and temporal resolution to observe the nonequilibrium dynamics of molecules and their assemblies. This account discusses the authors’ research on developing transmission electron microscopy techniques to visualize intricate and transient interactions within molecular systems, enhancing the understanding of chemical phenomena at atomic and molecular levels.

Visualization of Multiple-Resonance-Induced Frontier Molecular Orbitals in a Single Multiple-Resonance Thermally Activated Delayed Fluorescence Molecule.

The spatial distribution and electronic properties of the frontier molecular orbitals (FMOs) in a thermally activated delayed fluorescence (TADF) molecule contribute significantly to the TADF properties, and thus, a detailed understanding and sophisticated control of the FMOs are fundamental to the design of TADF molecules. However, for multiple-resonance (MR)-TADF molecules that achieve spatial separation of FMOs by the MR effect, the distinctive distribution of these molecular orbitals poses significant challenges for conventional computational analysis and ensemble averaging methods to elucidate the FMOs' separation and the precise mechanism of luminescence. Therefore, the visualization and analysis of electronic states with the specific energy level of a single MR-TADF molecule will provide a deeper understanding of the TADF mechanism. Here, scanning tunneling microscopy/spectroscopy (STM/STS) was used to investigate the electronic states of the DABNA-1 molecule at the atomic scale. FMOs' visualization and local density of states analysis of the DABNA-1 molecule clearly show that MR-TADF molecules also have well-separated FMOs according to the internal heteroatom arrangement, providing insights that complement existing theoretical prediction methods.

The Effect of Ensemble Averaging Method on Rainfall Forecasting in Jakarta Using ARIMA and ARIMAX

The Effect of Ensemble Averaging Method on Rainfall Forecasting in Jakarta Using ARIMA and ARIMAX

Reliability ensemble averaging reduces surface wind speed projection uncertainties in the 21st century over China

Accurate prediction of future surface wind speed (SWS) changes is the basis of scientific planning for wind turbines. Most studies have projected SWS changes in the 21st century over China on the basis of the multi-model ensemble (MME) of the 6th Coupled Model Intercomparison Project (CMIP6). However, the simulation capability for SWS varies greatly in CMIP6 multi-models, so the MME results still have large uncertainties. In this study, we used the reliability ensemble averaging (REA) method to assign each model different weights according to their performances in simulating historical SWS changes and project the SWS under different shared socioeconomic pathways (SSPs) in 2015–2099. The results indicate that REA considerably improves the SWS simulation capacity of CMIP6, eliminating the overestimation of SWS by the MME and increasing the simulation capacity of spatial distribution. The spatial correlations with observations increased from 0.56 for the MME to 0.85 for REA. Generally, REA could eliminate the overestimation of the SWS by 33% in 2015–2099. Except for southeastern China, the SWS generally decreases over China in the near term (2020–2049) and later term (2070–2099), particularly under high-emission scenarios. The SWS reduction projected by REA is twice as high as that by the MME in the near term, reaching −4% to −3%. REA predicts a larger area of increased SWS in the later term, which expands from southeastern China to eastern China. This study helps to reduce the projected SWS uncertainties.

Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets

The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophisticated algorithms: local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF). Our initial findings showed 155 outliers by both LOF and IF and 147 by OCSVM. A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM isolated 44 unique outliers utilizing its transformed feature space, and IF identified 76 unique outliers leveraging its tree-based mechanics. Despite these varying results, all methods had a consensus for just 44 outliers. Employing ensemble techniques, both averaging and voting methods identified 155 outliers, whereas the weighted method highlighted 151, with a consensus of 150 outliers across the board. In conclusion, while individual algorithms provide distinct perspectives, ensemble techniques enhance the accuracy and consistency of outlier detection. This underscores the necessity of using multiple algorithms with ensemble techniques in analyzing psychological datasets, facilitating a richer comprehension of inherent data structures.

How Can the Optical Variation Properties of Active Galactic Nuclei Be Unbiasedly Measured?

The variability of active galactic nuclei (AGNs) is ubiquitous, but has not yet been understood. Measuring the optical variation properties of AGNs, such as the variation timescale and amplitude, and then correlating them with their fundamental physical parameters has long served as a critical way of exploring the origin of AGN variability and the associated physics of the accretion process in AGNs. Obtaining accurate variation properties of AGNs is thus essential. It has been found that the damped random walk process can describe the AGN optical variation well, but there is a controversy over how long a minimal monitoring baseline is required to obtain unbiased variation properties. In this work, we settle the controversy by exhaustively scrutinizing the complex combination of assumed priors, adopted best-fit values, ensemble averaging methods, and fitting methods. The new proposal is then an optimized solution where unbiased variation properties of an AGN sample possessing the same variation timescale can be obtained with a minimal baseline of about 10 times their variation timescale. Finally, the new optimized solution is used to demonstrate the positive role of the time-domain surveys to be conducted by the Wide Field Survey Telescope in improving constraints on AGN variation properties.

Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging

ABSTRACT The gradual densification of seismic networks has facilitated the acquisition of large amounts of data. However, alongside natural tectonic earthquakes, seismic networks also record anthropogenic events such as quarry blasts or other induced events. Identifying and distinguishing these events from natural earthquakes requires experienced interpreters to ensure that seismological studies of natural phenomena are not compromised by anthropogenic events. Advanced artificial intelligence methods have already been deployed to tackle this problem. One of the applications includes Convolutional Neural Networks (CNN) to discriminate different kinds of events, such as natural earthquakes and quarry blasts. In this study, we investigate the effects of ensemble averaging and fine-tuning on seismic event discrimination accuracy to estimate the potential of these methods. We compare discrimination accuracy of two different CNN model architectures across three datasets. This was done with the best models from an ensemble of each model architecture, as well as with ensemble averaging and fine-tuning methods. Soft voting was used for the CNN ensemble predictions. For the transfer learning approach, the models were pretrained with data from two of the datasets (nontarget regions) and fine-tuned with data from the third one (target region). The results show that ensemble averaging and fine-tuning of CNN models leads to better generalization of the model predictions. For the region with the lowest numbers of one event type, the combination of ensemble averaging and fine-tuning led to an increase in discrimination accuracy of up to 4% at station level and up to 10% at event level. We also tested the impact of the amount of training data on the fine-tuning method, showing, that to create a global model, the selection of comprehensive training data is needed.

Single-Entity Bioelectrochemistry

Interfacing biological catalytic entities, such as whole cells or enzymes, with electrodes, has a wide range of applications, ranging from fundamental studies of the entities themselves to power generation, bioremediation, chemical synthesis, and biosensing. In all cases, the operation of these bioelectrocatalytic systems is based on electron transfer phenomena within protein molecules, whether isolated or residing inside cells or parts of cells, as well as between proteins and electronics. Typically, the electron transfer and catalytic properties of proteins are studied using ensemble averaging methods, which obscure any heterogeneity and dynamics of individual molecules. This information, however, is especially important for understanding electron transfer and catalytic processes within a living cell where only one or a few protein molecules are present. While single-entity electrochemical measurements are particularly useful for studying electron transfer reactions at the nanoscale level, they have limited applications for investigating biological entities due to the experimental challenges associated with direct measurements of small electrical currents. 1,2 Here we will discuss our recent progress in investigating biological electron transfer reactions at the single-entity level, from bacterial cells to single redox proteins, using nano-impact (collision) single-entity electrochemistry. 3 We demonstrate how advances in electrode fabrication methods and detection setups enable current detection in the fA range. When combined with the developed data processing algorithm based on unsupervised learning and template matching techniques, this enabled unprecedented insight into electron transfer processes at the nanoscale. We will talk about insights provided by the method in the study of several important bioelectrocatalytic systems: single E.coli cells with overexpressed Fe-Fe hydrogenases, single bilirubin oxidase molecules, and single catalase molecules. In addition, we will discuss models that we developed for understanding the single bioentity electrode interactions for diffusion-controlled enzymes when the product of the catalytic reaction is detected on the electrode, as well as systems based on direct electron transfer between the bioentity and the electrode. Overall, we show how single-entity bioelectrochemistry can shed light on biological electron transfer and catalysis processes and discuss the challenges it currently faces. Zhang, J.-H.; Zhou, Y.-G. Nano-Impact Electrochemistry: Analysis of Single Bioentities. TrAC Trends Anal. Chem. 2019, 115768.Davis, C.; Wang, S. X.; Sepunaru, L. What Can Electrochemistry Tell Us About Individual Enzymes? Curr. Opin. Electrochem. 2020, 100643.Sekretaryova, A. N.; Vagin, M. Yu.; Turner, A. P. F.; Eriksson, M. Electrocatalytic Currents from Single Enzyme Molecules. J. Am. Chem. Soc. 2016, 138 (8), 2504–250

Multiscale simulations reveal the role of PcrA helicase in protecting against spontaneous point mutations in DNA

Proton transfer across hydrogen bonds in DNA can produce non-canonical nucleobase dimers and is a possible source of single-point mutations when these forms mismatch under replication. Previous computational studies have revealed this process to be energetically feasible for the guanine-cytosine (GC) base pair, but the tautomeric product (G∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^*$$\\end{document}C∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^*$$\\end{document}) is short-lived. In this work we reveal, for the first time, the direct effect of the replisome enzymes on proton transfer, rectifying the shortcomings of existing models. Multi-scale quantum mechanical/molecular dynamics (QM/MM) simulations reveal the effect of the bacterial PcrA Helicase on the double proton transfer in the GC base pair. It is shown that the local protein environment drastically increases the activation and reaction energies for the double proton transfer, modifying the tautomeric equilibrium. We propose a regime in which the proton transfer is dominated by tunnelling, taking place instantaneously and without atomic rearrangement of the local environment. In this paradigm, we can reconcile the metastable nature of the tautomer and show that ensemble averaging methods obscure detail in the reaction profile. Our results highlight the importance of explicit environmental models and suggest that asparagine N624 serves a secondary function of reducing spontaneous mutations in PcrA Helicase.

Direct observation of glycans bonded to proteins and lipids at the single-molecule level.

Proteins and lipids decorated with glycans are found throughout biological entities, playing roles in biological functions and dysfunctions. Current analytical strategies for these glycan-decorated biomolecules, termed glycoconjugates, rely on ensemble-averaged methods that do not provide a full view of positions and structures of glycans attached at individual sites in a given molecule, especially for glycoproteins. We show single-molecule analysis of glycoconjugates by direct imaging of individual glycoconjugate molecules using low-temperature scanning tunneling microscopy. Intact glycoconjugate ions from electrospray are soft-landed on a surface for their direct single-molecule imaging. The submolecular imaging resolution corroborated by quantum mechanical modeling unveils whole structures and attachment sites of glycans in glycopeptides, glycolipids, N-glycoproteins, and O-glycoproteins densely decorated with glycans.

Drive versus Pressure Contributions to Genioglossus Activity in Obstructive Sleep Apnea.

Rationale: Loss of pharyngeal dilator muscle activity is a key determinant of respiratory events in obstructive sleep apnea (OSA). After the withdrawal of wakefulness stimuli to the genioglossus at sleep onset, mechanoreceptor negative pressure and chemoreceptor ventilatory drive feedback govern genioglossus activation during sleep, but the relative contributions of drive and pressure stimuli to genioglossus activity across progressive obstructive events remain unclear. We recently showed that drive typically falls during events, whereas negative pressures increase, providing a means to assess their individual contributions to the time course of genioglossus activity. Objectives: For the first time, we critically test whether the loss of drive could explain the loss of genioglossus activity observed within events in OSA. Methods: We examined the time course of genioglossus activity (EMGgg; intramuscular electromyography), ventilatory drive (intraesophageal diaphragm electromyography), and esophageal pressure during spontaneous respiratory events (using the ensemble-average method) in 42 patients with OSA (apnea-hypopnea index 5-91 events/h). Results: Multivariable regression demonstrated that the falling-then-rising time course of EMGgg may be well explained by falling-then-rising drive and rising negative pressure stimuli (model R = 0.91 [0.88-0.98] [95% confidence interval]). Overall, EMGgg was 2.9-fold (0.47-∞) more closely associated with drive than pressure stimuli (ratio of standardized coefficients, βdrive:βpressure; ∞ denotes absent pressure contribution). However, individual patient results were heterogeneous: approximately one-half (n = 22 of 42) exhibited drive-dominant responses (i.e., βdrive:βpressure > 2:1), and one-quarter (n = 11 of 42) exhibited pressure-dominant EMGgg responses (i.e., βdrive:βpressure < 1:2). Patients exhibiting more drive-dominant EMGgg responses experienced greater event-related EMGgg declines (12.9 [4.8-21.0] %baseline/standard deviation of βdrive:βpressure; P = 0.004, adjusted analysis). Conclusions: Loss of genioglossus activity precipitating events in patients with OSA is strongly associated with a contemporaneous loss of drive and is greatest in those whose activity tracks drive rather than pressure stimuli. These findings were upheld for events without prior arousal. Responding to falling drive rather than rising negative pressure during events may be deleterious; future therapeutic strategies whose aim is to sustain genioglossus activity by preferentially enhancing responses to rising pressure rather than falling drive are of interest.

Hydrodynamic Characteristics of Strong, Unsteady Open-Channel Flow

Due to climate change, the intensity of extreme rainfall has been observed to increase with a shorter duration, causing flash floods (strong, unsteady flow) that lead to serious loss of life and economic damage all over the world. In this study, by repeating the same flume experiments twenty times over a bare bed or with a submerged vane installed, the hydrodynamic characteristics of a strong, unsteady open-channel flow were investigated. Acoustic Doppler velocimetry (ADV) was used to measure the instantaneous three-dimensional velocity, and the ensemble average method was then adopted to obtain the time-varying mean flow velocities. Reynolds decomposition was applied to disintegrate the instantaneous velocity to time-varying average velocity and fluctuating velocity. Turbulence characteristics such as turbulent intensity, turbulent bursting, and power spectral density (PSD) were analyzed against water depth variations. The results show that the loop pattern of the streamwise velocity against the water depth variations could significantly affect the turbulence characteristics of unsteady flow. Near the bed, the peaks of the turbulent intensity and the TKE lag behind the peak of the water depth. The PSD revealed that the turbulent energy increases at the rising and falling stages were due to the generation of small-scale turbulence vortices or eddies. As a submerged vane was present, the increase in the angle of attack caused the increase in the turbulent intensity and TKE, which means that the induced vortex became stronger and the wake region was larger. When the angle of attack was equal to 20°, the TKE abruptly enlarged in the falling stages, implying the breaking-up of the induced vortex. The PSD of the transverse fluctuation velocity showed multiple spikes at the high-frequency part, possibly denoting the shedding frequency from the induced vortex. Further downstream, behind the submerged vane, the peak frequencies of the PSD became imperceptible, likely because of the induced vortex decays or other factors such as the turbulence generated from the free surface or the channel bed mixing with the turbulence from the induced vortex.

Methods to investigate nucleosome structure and dynamics with single-molecule FRET.

The nucleosome is the fundamental building block of chromatin. Changes taking place at the nucleosome level are the molecular basis of chromatin transactions with various enzymes and factors. These changes are directly and indirectly regulated by chromatin modifications such as DNA methylation and histone post-translational modifications including acetylation, methylation, and ubiquitylation. Nucleosomal changes are often stochastic, unsynchronized, and heterogeneous, making it very difficult to monitor with traditional ensemble averaging methods. Diverse single-molecule fluorescence approaches have been employed to investigate the structure and structural changes of the nucleosome in the context of its interactions with various enzymes such as RNA Polymerase II, histone chaperones, transcription factors, and chromatin remodelers. We utilize diverse single-molecule fluorescence methods to study the nucleosomal changes accompanying these processes, elucidate the kinetics of these processes, and eventually learn the implications of various chromatin modifications in directly regulating these processes. The methods include two- and three-color single-molecule fluorescence resonance energy transfer (FRET), single-molecule fluorescence correlation spectroscopy, and fluorescence (co-)localization. Here we report the details of the two- and three-color single-molecule FRET methods we currently use. This report will help researchers design their single-molecule FRET approaches to investigating chromatin regulation at the nucleosome level.

Projection and Analysis of Floods in the Upper Heihe River Basin under Climate Change

The projection of future hydrological processes can provide insights into the risks associated with potential hydrological events in a changing environment and help develop strategies to cope with and prevent them. The Heihe River basin in Northwest China is crucial for providing water resources to water-scarce regions. Thus, understanding the future runoff trends in the context of climate change can optimize water allocation, alleviate water shortages, and mitigate flood risks in the region. In this study, we use meteorological data from 10 general circulation models under two future scenarios to drive the Soil and Water Assessment Tool (SWAT) model and project hydrological processes in the upper Heihe River basin from 2026 to 2100. After examining the future changes in total runoff in the basin, we assess the magnitude, frequency, and timing of daily flood events in the future. The results of the multi-model ensemble averaging (MMEA) method show that the change in the multi-year average annual runoff is −4.5% (2026–2050), −1.8% (2051–2075), and +2.0% (2076–2100) under the SSP245 scenario and −1.0% (2026–2050), +0.4% (2051–2075), and +0.2% (2076–2100) under the SSP585 scenario compared to the historical period. The analysis of flood magnitudes indicates that the basin will experience higher-magnitude floods in the future, with the largest increase rates of 61.9% and 66.4% for the 1-day maximum flows under the SSP245 and SSP585 scenarios, respectively. The flood return period is projected to be shorter in the future, and the 1-day maximum flows of a 100-year flood are expected to increase by 44.7% and 63.7% under the SSP245 and SSP585 scenarios, respectively. Furthermore, a significant shift in the flood timing is expected, with the highest frequency moving from July to August, representing a one-month lag compared to the historical period. Our findings suggest that the hydrological characteristics of the upper Heihe River basin may be significantly altered in the future due to the effects of climate change, resulting in floods with higher magnitudes and frequencies and different timings. Therefore, it is imperative to consider these changes carefully when developing risk prevention measures.

Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle

Abstract. Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias-correct or calculate ensemble averages over the original forcing data to reduce the climate-driven uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables.

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Near-surface characteristics of velocity profile and momentum transport after wave-breaking in deep water

Wave-breaking plays an important role in the process of mixing in the upper ocean, which can cause wave momentum and energy flux loss, enhance near-surface turbulence, and adjust the distribution of the momentum flux at the air-sea interface. In this paper, wave-breaking was generated using a laboratory method of controllable deep water breaking waves, and flow field characteristics after wave-breaking were measured and analyzed using PIV technology and the ensemble average method. The velocity threshold was proposed for the effective influence of wave-breaking on the flow field, then the EID (Effective-Influenced-Depth) of wave-breaking, which is Dv in the paper, was quantified, and the quantitative relationship between Dv and energy loss induced by breaking was established. Velocity characteristics of the near-surface flow field and transport characteristics of horizontal momentum induced by wave-breaking were analyzed, which can contribute to the parameterization of wave-breaking. The results show that the energy injected by wave-breaking is transported downstream along the direction of wave propagation. The vertical-influenced range of wave-breaking is about 1–2 times the wave-breaking height. The time integral of horizontal momentum flux increases significantly in the depth of about 1 time the wave-breaking height near the surface and decays significantly with depth. Mass transport of the fluid near the surface induced by wave-breaking mainly occurs in the depth where momentum flux increases significantly. After about 29 times the SWWP (spectrally weighted wave period) after breaking, fluid near the surface is no longer under the action of horizontal stress induced by wave-breaking, so no mass transport occurs anymore. For parameterization of wave-breaking in circulation models, the research can provide a reference in time and space scale, as well as experimental data support for the establishment of a new influence mode.

Ensemble average method for runtime saving in Large Eddy Simulation of free and Ducted Fuel Injection (DFI) sprays

Computational Fluid Dynamics (CFD) with Large Eddy Simulation (LES) turbulence model is a valuable tool to investigate complex problems. However, for high Reynolds number problems, the associated huge computational cost often leads researchers to the use of more simplified and less accurate approaches, especially if statistics is needed for the generalization of the results and comparison against experimental data. Therefore, the introduction of innovative methodologies to reduce the computational cost maintaining results reliability would be of paramount importance for LES-based investigation.In this context, the aim of this work is to assess a runtime saving methodology to ensemble average several axial symmetric spray simulations obtained with LES. In particular, the number of independent samples for the average procedure has been increased by exploiting the axial symmetry characteristics of a diesel spray case study, extracting more realizations from a single simulation. This ensemble average approach was compared with the standard one, based on one realization per simulation, at equal statistical sample size. Main spray physical quantities and turbulence characteristics were examined, both globally and locally. The same procedure was also applied to a different diesel-relevant spray configuration, known as ducted fuel injection.The reliability of this ensemble average methodology has been herein proven for both spray configurations, highlighting a dramatic runtime saving without any worsening of the accuracy level. In particular, this approach, as applied in this work, guaranteed a computational cost reduction of 50–75%. Thereby, the present methodological assessment could motivate researchers involved in the investigation of spray processes to undertake the path of statistically significant LES analysis.

Understanding the nanoscale interactions of surface plasmon-mediated semiconductor surfaces with water and light for renewable energy harvesting and conversion

Photocatalysts integrated with surface plasmon metals serve as an interesting platform for fully understanding how energy harvesting and storage are managed through physical and chemical enhancement processes. Nanoelectrode with a single plasmon metal nanoparticle (NP) provides unique features capable of resolving complex enhancement processes which otherwise cannot be achievable through conventional ensemble averaging methods. Preliminary theoretical results are presented here to illustrate both electrochemical and optical field enhancement characteristics of a single NP when interacting with a semiconductor surface. Potential methodologies to construct single plasmon nanoelectrodes and challenges are discussed in this opinion.

A further investigation on the data assimilation-based small-scale reconstruction of turbulence

Existing works have shown that the small-scale errors of turbulence can be completely eliminated through data assimilation (DA), provided that all the large-scale Fourier modes below a critical wavenumber kc≈0.2η−1 are continuously enforced, where η is the Kolmogorov length scale. Here, we further explore the DA-based small-scale reconstruction problem, for which the large-scale data are insufficient. Under such conditions, an unexpected artificial jump in the energy spectrum is observed. To alleviate this issue and improve the reconstruction accuracy, several approaches have been attempted, including ensemble averaged assimilation, temporally sparse data assimilation (TSDA), and filtering the penalty term in the assimilation. It is shown that ensemble averaging can tangibly reduce the reconstruction error, but the resulted energy spectrum is invariably lower than the true spectrum; TSDA can effectively remove the jump in the energy spectrum, but the reduction of the reconstruction error is limited. Filtering the penalty term can also rectify the energy spectrum, but it makes the reconstruction error larger. Based on these observations, we re-scale the ensemble averaged solution according to the rectified energy spectrum. Both the energy spectrum and the small-scale reconstruction accuracy have been improved by the re-scaled ensemble average method. Furthermore, we also test the current approach in the spatial nudging-based reconstruction of turbulence. Again, enhanced predictions are obtained for both the energy spectrum and the instantaneous turbulent field, invariably demonstrating the effectiveness and robustness of the proposed method.