Short Time Steps Research Articles

Large Eddy simulation of the fluctuating wind environment at a bridge site in the mountainous area

To have a comprehensive understanding of the complex wind environment at a bridge site in the mountainous area, a numerical simulation study of the wind environment under the mean and the fluctuating wind flow conditions was carried out and the results were compared. First, according to the weighted amplitude wave superposition (WAWS) method, the fluctuating wind speed time history was compiled by UDF. And the wind speed time history was added to the inlet boundary of a numerical empty wind tunnel to verify the feasibility of the simulation method of the fluctuating wind. Then, with a bridge in the mountainous area in Yunnan as the engineering background, a numerical simulation study of the wind environment of the bridge site area under the mean wind flow and the fluctuating wind flow was carried out by using FLUENT. The study indicates that Large Eddy Simulation (LES) method more accurate than Reynold average method with a sufficient number of grids and a short enough time step. The average wind characteristics of the bridge site under the mean wind and the fluctuating wind are not much different. The fluctuating wind characteristics at the bridge site are mainly affected by the terrain and the pulsating component of the wind flow. There are different terrain pulsation effects at the bridge site under different incoming flow directions.

String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor.

The kinetics of a dynamical system comprising two metastable states is formulated in terms of a finite-time propagator in phase space (position and velocity) adapted to the underdamped Langevin equation. Dimensionality reduction to a subspace of collective variables yields familiar expressions for the propagator, committor, and steady-state flux. A quadratic expression for the steady-state flux between the two metastable states can serve as a robust variational principle to determine an optimal approximate committor expressed in terms of a set of collective variables. The theoretical formulation is exploited to clarify the foundation of the string method with swarms-of-trajectories, which relies on the mean drift of short trajectories to determine the optimal transition pathway. It is argued that the conditions for Markovity within a subspace of collective variables may not be satisfied with an arbitrary short time-step and that proper kinetic behaviors appear only when considering the effective propagator for longer lag times. The effective propagator with finite lag time is amenable to an eigenvalue-eigenvector spectral analysis, as elaborated previously in the context of position-based Markov models. The time-correlation functions calculated by swarms-of-trajectories along the string pathway constitutes a natural extension of these developments. The present formulation provides a powerful theoretical framework to characterize the optimal pathway between two metastable states of a system.

Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing

Abstract. The increase in computing power and recent model developments allow for the use of global kilometer-scale weather and climate models for routine forecasts. At these scales, deep convective processes can be partially resolved explicitly by the model dynamics. Next to horizontal resolution, other aspects such as the applied numerical methods, the use of the hydrostatic approximation, and time step size are factors that might influence a model's ability to resolve deep convective processes. In order to improve our understanding of the role of these factors, a model intercomparison between the nonhydrostatic COSMO model and the hydrostatic Integrated Forecast System (IFS) from ECMWF has been conducted. Both models have been run with different spatial and temporal resolutions in order to simulate 2 summer days over Europe with strong convection. The results are analyzed with a focus on vertical wind speed and precipitation. Results show that even at around 3 km horizontal grid spacing the effect of the hydrostatic approximation seems to be negligible. However, time step proves to be an important factor for deep convective processes, with a reduced time step generally allowing for higher updraft velocities and thus more energy in vertical velocity spectra, in particular for shorter wavelengths. A shorter time step is also causing an earlier onset and peak of the diurnal cycle. Furthermore, the amount of horizontal diffusion plays a crucial role for deep convection with more diffusion generally leading to larger convective cells and higher precipitation intensities. The study also shows that for both models the parameterization of deep convection leads to lower updraft and precipitation intensities and biases in the diurnal cycle with a precipitation peak which is too early.

The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions

Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz.

Study on the effect of timestep and thermography method for pavement watering technology

In this study, effect of the timestep and thermography method when measuring the surface temperature of the road and surround temperature on pavement watering was verified. The study result showed that the temperature of the surface and surround air were dropped after watering according to the cooling effect, and the temperature change tends to occur considerably within 10 min in this experiment. Therefore the short timestep should be applied according to the site condition to assume the cooling effect of pavement watering. In addition, thermography method showed to be inaccurate right after the watering and when the solar radiation is strong. Therefore, it is necessary to consider the above when using thermography method.

Non-negative matrix factorization for the analysis of particle number concentrations: Characterization of the temporal variability of sources in indoor workplace

The temporal variability of indoor Particle Number (PN) concentrations, their determinants and their relative contributions in an occupied workspace were investigated. The presented study is based on the receptor modeling approach, focusing on Non-negative Matrix Factorization (NMF) to provide new insights on the source time variability. Continuous size distribution from 0.3 μm to 20 μm were collected with a short time step sampling (1 min) over six months in 2015. The measurements were made inside and outside an open-plan office occupied by 6–8 persons. NMF distinguished five major patterns obtained from PN concentrations time series. The apportionment results were expressed as source diurnal profiles and strengths by relating the obtained source contributions to the source information provided by the office occupancy and natural ventilation (the effect of opening windows). Factor 2 contributes to 75% of the total contributions for finer size fraction (<0.5 μm). Combining NMF results with indoor occupancy and windows states successfully demarcated the main sources of fluctuation. The diurnal profiles of the third factor (F3) and PN0.9−1.8 concentrations time series are very similar (r = 0.95). The diurnal variation of factor 1 is very similar to that observed for CO2 variations and PN6.25−12.5 time series. Coarse particles (>17.5 μm) are associated with the 4th factor. The latter does not contribute to any of the other particle ranges. The NMF factors interpretation was supported by correlation analysis and statistical tests, as well as by temporal variation comparison.

Ethylenediamine Functionalized Cellulose Acetate [CAEDA] Nanosized Solid Base Catalyst in the Synthesis of 2‐Amino‐4H‐Chromenes and 5‐Substituted‐1H‐Tetrazoles

AbstractA series of 2‐amino‐4H‐chromenes by combining dimedone, different substituted aromatic aldehydes and malononitrile and 5‐substituted‐1H‐tetrazoles by combining variable aldehydes, hydroxylamine hydrochloride and sodium azide have been efficiently synthesized in presence of ethylenediamine functionalized cellulose acetate as a solid base catalyst i.e Cellulose acetate[1,2‐ethylenediamine][CAEDA]. This catalyst is low‐cost, non‐toxic, unexplored, easily available, reusable, eco‐friendly in nature. This synthesized solid base catalyst has been characterized by fourier transform infrared spectroscopy (FTIR), themogravimetric analysis (TGA), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) spectroscopic techniques. The new catalytic method exhibits some noteworthy advantages such as short reaction time, high yields, easy workup and purification steps, simple preparation method which makes this strategy more useful for the synthesis of chromene and tetrazole derivatives then earlier methods.

Modeling non-Markovian data using Markov state and Langevin models.

Markov processes provide a popular approach to construct low-dimensional dynamical models of a complex biomolecular system. By partitioning the conformational space into metastable states, protein dynamics can be approximated in terms of memory-less jumps between these states, resulting in a Markov state model (MSM). Alternatively, suitable low-dimensional collective variables may be identified to construct a data-driven Langevin equation (dLE). In both cases, the underlying Markovian approximation requires a propagation time step (or lag time) δt that is longer than the memory time τM of the system. On the other hand, δt needs to be chosen short enough to resolve the system timescale τS of interest. If these conditions are in conflict (i.e., τM > τS), one may opt for a short time step δt = τS and try to account for the residual non-Markovianity of the data by optimizing the transition matrix or the Langevin fields such that the resulting model best reproduces the observables of interest. In this work, rescaling the friction tensor of the dLE based on short-time information in order to obtain the correct long-time behavior of the system is suggested. Adopting various model problems of increasing complexity, including a double-well system, the dissociation of solvated sodium chloride, and the functional dynamics of T4 lysozyme, the virtues and shortcomings of the rescaled dLE are discussed and compared to the corresponding MSMs.

ARRT Development for the Upcoming Human Exploration Missions

AbstractThe health risks of space radiation present big challenges to space exploration, with the possibility of large Energetic Solar Particle Events (ESPEs) inducing Acute Radiation Sickness (ARS) during upcoming Artemis missions. An operational software Acute Radiation Risks Tool (ARRT) was developed to directly use measurements from onboard dosimeters to project organ doses during times of increased radiation exposure, so that any possible ARS risks of the astronauts can be modeled and monitored in real time using a data stream at the astronaut location. To enable ARRT to handle variant scenarios of any possible ESPEs in an automatic manner for mission operation, two data sets were employed in developing its modules, one involving historical solar protons recorded over the past four decades and the other using the real‐time telemetry readings of dosimeters onboard International Space Station (ISS). Though vastly different in terms of data cadence, smoothness, and data gaps, all events in these data sets can be correctly processed to output organ doses and ARS risks and generate flight notes for communication within the Flight Control Team (FCT). All these tasks are completed with close interactions between multiple modules developed with many state‐of‐the‐art facilities of full stack web applications. This work demonstrates that ARRT meets the requirement to project radiation exposure and to provide clinical guidelines in very short time steps as the ESPE unfolds, even for the longest event in data sets, making this tool eligible to be tested during the upcoming unmanned Artemis mission and utilized in future space exploration.

Managing complex datasets to predict Bactrocera oleae infestation at the regional scale

Predicting the occurrence of B. oleae infestation is needed in sustainable olive tree growing, for the application of preventive control strategies. In this study, machine learning (ML) algorithms were tested to predict the occurrence of B. oleae infestation. Regional-based and long-term (2002–2019) dataset on infestation collected by trained technicians in the sampling network (Tuscany, central Italy) was related to geographic variables, bioclimatic indices and the frequency of infestation in the previous year. Five ML algorithms were compared. Data collected from 2002 to 2017 were divided with an 80/20 proportion in “training” and “test 20%”, considering the group “farm ID × year”. In addition, data collected in 2018 and 2019 (“test 18–19”) were also tested.The algorithm C5.0 was selected as the most suitable algorithm (AUC = 0.9) and used to predict the occurrence of B. oleae infestation. The following results were obtained: (i) a negative effect on the probability of infestation of the distance from the sea of the olive groves and of the frequency of infestation of the previous year and (ii) a positive effect of doy (day of the year), cumulative degree days and mean air temperature of the previous winter.The occurrence of infestation was predicted with an accuracy of 0.8 on “test 20%” and of 0.7 on “test 18–19”. The potential of this methodology to predict the occurrence of B. oleae infestation in a short-time step, e.g., one week, needs to be tested in a real application, running the model weekly.

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.

Assessing soil water content variation in a small mountainous catchment over different time scales and land covers using geographical variables

Soil Water Content (SWC) plays a key role in hydrological processes at the catchment scale. It is highly dependent on local variables (such as soil properties, vegetation type, and topography) and rainfall, which makes it vary in space and time. To characterize its complexity, scientists need SWC monitoring at short time-steps and at different spatial scales (plot, hillslope, sub-catchment). Today such monitoring is possible due to sensors based on geophysical methods that measure SWC at the plot scale, but assessing the space–time variation of SWC remains a challenge in mountainous catchments. This study focuses on a small headwater catchment (Laval, 0.86 km2, Draix-Bléone observatory) composed primarily of black marls in which soil water content variations are assumed to occur primarily in the subsurface (0 cm–50 cm). To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. At the hourly and daily time scales, SWC variations were highly dependent on curvature index and NDVI during wetting period, and primarily on soil depth and NDVI during drying periods. At 15 min time steps, 7 rainfall-runoff events have been investigated. SWC variations were highly dependent on rainfall intensity and initial SWC. At the same time step, matrix flow was characterized as the main subsurface flow type during fall flood events in every monitored environment while preferential flow appeared during summer flood events and mainly in grassland.

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

Accelerated spin dynamics using deep learning corrections

Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results. New approaches to efficient spin dynamics simulations are limited by integration time step barrier to solving the equations-of-motions of many-body problems. Using a short time step leads to an accurate but inefficient simulation regime whereas using a large time step leads to accumulation of numerical errors that render the whole simulation useless. In this paper, we use a Deep Learning method to compute the numerical errors of each large time step and use these computed errors to make corrections to achieve higher accuracy in our spin dynamics. We validate our method on the 3D Ferromagnetic Heisenberg cubic lattice over a range of temperatures. Here we show that the Deep Learning method can accelerate the simulation speed by 10 times while maintaining simulation accuracy and overcome the limitations of requiring small time steps in spin dynamic simulations.

An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

This paper presents an enhanced vertical ground heat exchanger (GHE) model for whole-building energy simulation (WBES). WBES programs generally have computational constraints that affect the development and implementation of component simulation sub-models. WBES programs require models that execute quickly and efficiently due to how the programs are utilized by design engineers. WBES programs also require models to be formulated so their performance can be determined from boundary conditions set by upstream components and environmental conditions. The GHE model developed during this work utilizes an existing response factor model and extends its capabilities to accurately and robustly simulate at timesteps that are shorter than the GHE transit time. This was accomplished by developing a simplified dynamic borehole model and then exercising that model to generate exiting fluid temperature response factors. This approach blends numerical and analytical modeling methods. The existing response factor models are then extended to incorporate the exiting fluid temperature response factor to provide a better estimate of the GHE exiting fluid temperature at short simulation timesteps.

The importance of short lag-time in the runoff forecasting model based on long short-term memory

It is still very challenging to enhance the accuracy and stability of daily runoff forecasts, especially several days ahead, owing to the non-linearity of the forecasted processes. Here, we hypothesize that short lag-time has a significant impact on forecasting results. Thus, we incorporate short previous time steps into long short-term memory (LSTM) and develop the Self-Attentive Long Short-Term Memory (SA-LSTM). In SA-LSTM, the self-attention mechanism is used to model interdependencies within short previous time steps. SA-LSTM is evaluated at eight runoff datasets. The experimental results demonstrate that, compared with state-of-art benchmark models, SA-LSTM achieves the best performance. The RMSEs of SA-LSTM are at least 2.3% smaller than that of the second best model at the seventh day. The NSEs and NSE_In of SA-LSTM are at least 4.6% and 6.4% higher than those of the second best model at the seventh day. Furthermore, SA-LSTM also surpasses the baseline methods for base, mean and peak flows. The superiority of SA-LSTM can be attributed to its exploitation of information in short lag-time.

Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach.

Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms.

On the performance of energy walls by monitoring assessment and numerical modelling: a case in Italy

Thermoactive diaphragm walls have proved their efficiency for near-surface geothermal energy use. To get insights into the heat transfer process occurring between the heat exchanger pipes and the surrounding boundaries, an instrumented real case located in northern Italy was taken as reference. Combining on-site monitoring data with computational simulations, the role of the basement space in governing the heat fluxes in the different seasons, and the energy performance of the diaphragm wall and of the ground source heat pump system, is highlighted. The numerical analysis represents an effective predictive tool, but is also highly sensitive with respect to parameters of uncertain or complex definition, such as the boundary thermal conditions and the thermal input at the pipe inlet. Intermittent operation and idle periods require a refined simulation, discretising the time dependency of the input variables in appropriate short time steps.

Hierarchical multiscale numerical modelling of internal erosion with discrete and finite elements

This paper presents a coupled finite and discrete-element model (FEM and DEM) to simulate internal erosion. The model is based on ICY, an interface between COMSOL, an FEM engine, and YADE, a DEM code. With this model, smaller DEM subdomains are generated to simulate particle displacements at the grain scale. Particles in these small subdomains are subjected to buoyancy, gravity, drag and contact forces for short time steps (0.1 s). The DEM subdomains provide the macroscale (continuum) model with a particle flux distribution. Through a mass conservation equation, the flux distribution allows changes in porosity, hydraulic conductivity and hydraulic gradient to be evaluated for the same time steps at a larger, continuum scale. The updated hydraulic gradients from the continuum model provide the DEM subdomains with updated hydrodynamic forces based on a coarse-grid method. The number of particles in the DEM subdomains is also updated based on the new porosity distribution. The hierarchical multiscale model (HMM) was validated with the simulation of suffusion. Results for the proposed HMM algorithm are consistent with results based on a DEM model incorporating the full sample and simulation duration. The proposed HMM algorithm could enable the modelling of internal erosion for soil volumes that are too large to be modelled with a single DEM subdomain.

Simulations of future changes in thermal structure of Lake Erken: proof of concept for ISIMIP2b lake sector local simulation strategy

Abstract. This paper, as a part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), assesses the impacts of different levels of global warming on the thermal structure of Lake Erken (Sweden). The General Ocean Turbulence Model (GOTM) one-dimensional hydrodynamic model was used to simulate water temperature when using ISIMIP2b bias-corrected climate model projections as input. These projections have a daily time step, while lake model simulations are often forced at hourly or shorter time steps. Therefore, it was necessary to first test the ability of GOTM to simulate Lake Erken water temperature using daily vs hourly meteorological forcing data. In order to do this, three data sets were used to force the model as follows: (1) hourly measured data, (2) daily average data derived from the first data set, and (3) synthetic hourly data created from the daily data set using generalised regression artificial neural network methods. This last data set is developed using a method that could also be applied to the daily time step ISIMIP scenarios to obtain hourly model input if needed. The lake model was shown to accurately simulate Lake Erken water temperature when forced with either daily or synthetic hourly data. Long-term simulations forced with daily or synthetic hourly meteorological data suggest that by the late 21st century the lake will undergo clear changes in thermal structure. For the representative concentration pathway (RCP) scenario, namely RCP2.6, surface water temperature was projected to increase by 1.79 and 1.36 ∘C when the lake model was forced at daily and hourly resolutions respectively, and for RCP6.0 these increases were projected to be 3.08 and 2.31 ∘C. Changes in lake stability were projected to increase, and the stratification duration was projected to be longer by 13 and 11 d under RCP2.6 scenario and 22 and 18 d under RCP6.0 scenario for daily and hourly resolutions. Model changes in thermal indices were very similar when using either the daily or synthetic hourly forcing, suggesting that the original ISIMIP climate model projections at a daily time step can be sufficient for the purpose of simulating lake water temperature.