Short Time Steps Research Articles

Efficient simulation of the heat transfer in fused filament fabrication

Heat transfer simulations of the fused filament fabrication process are an important tool to predict bonding, residual stresses and strength of 3D printed parts. But in order to capture the significant thermal gradients that occur in the FFF printing process, a fine mesh discretization and short time steps are required, leading to extensive computational efforts. In this work a simulation framework is presented which combines several efficiency measures with the objective of reducing the computational efforts required in simulating the FFF printing process without simplifying the deposition physics or reducing the overall accuracy. Thus, the material deposition has been modeled with a hybrid element activation approach and elements are adaptively coarsened through an error-based coarsening condition. Additionally, an appropriate coarsening technique is presented for geometries with air-filled infill patterns. The accuracy of the numerical framework is experimentally validated and the efficiency of the framework is validated numerically by comparing the performance of models with and without any efficiency measures. Finally, its effectiveness is shown by simulating the printing process of a larger geometry.

Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables.

Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.

Corrosion-resistant coating on AZ91D magnesium alloy based on one-step hydrothermal

ABSTRACT Magnesium alloys are widely used due to their excellent properties, but their corrosion resistance is limited by their high chemical activity. This paper proposes a one-step hydrothermal method with a relatively short reaction time and simple experimental steps for synthesising magnesium alloy coatings with good corrosion protection properties. The surface morphology, chemical composition and corrosion resistance of the in situ generated coatings were investigated using measurements such as field emission scanning electron microscopy (SEM), adhesion cross-cut tape (ACCT) test, X-ray diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), electrochemistry and immersion test, the results revealed that the corrosion current density (Icorr) of the coatings decreased by approximately four orders of magnitude and had good corrosion resistance.

High-accuracy deep ANN-to-SNN conversion using quantization-aware training framework and calcium-gated bipolar leaky integrate and fire neuron.

Spiking neural networks (SNNs) have attracted intensive attention due to the efficient event-driven computing paradigm. Among SNN training methods, the ANN-to-SNN conversion is usually regarded to achieve state-of-the-art recognition accuracies. However, many existing ANN-to-SNN techniques impose lengthy post-conversion steps like threshold balancing and weight renormalization, to compensate for the inherent behavioral discrepancy between artificial and spiking neurons. In addition, they require a long temporal window to encode and process as many spikes as possible to better approximate the real-valued ANN neurons, leading to a high inference latency. To overcome these challenges, we propose a calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model to better approximate the functions of the ReLU neurons widely adopted in ANNs. We also propose a quantization-aware training (QAT)-based framework leveraging an off-the-shelf QAT toolkit for easy ANN-to-SNN conversion, which directly exports the learned ANN weights to SNNs requiring no post-conversion processing. We benchmarked our method on typical deep network structures with varying time-step lengths from 8 to 128. Compared to other research, our converted SNNs reported competitively high-accuracy performance, while enjoying relatively short inference time steps.

Toward fixed point and pulsation quantum search on graphs driven by quantum walks with in- and out-flows: a trial to the complete graph

We treat a quantum walk model with in- and out-flows at every time step from the outside. We show that this quantum walk can find the marked vertex of the complete graph with a high probability in the stationary state. In exchange of the stability, the convergence time is estimated by \(O(N\log N)\), where N is the number of vertices. However, until the time step O(N), we show that there is a pulsation with the periodicity \(O(\sqrt{N})\). We find the marked vertex with a high relative probability in this pulsation phase. This means that we have two chances to find the marked vertex with a high relative probability; the first chance visits in the pulsation phase at short time step \(O(\sqrt{N})\), while the second chance visits in the stable phase after long time step \(O(N\log N)\). The proofs are based on Kato’s perturbation theory.

System Identification–Based Adaptive Real-Time Simulators for Power Electronic Converters—Application to Three-Phase and NPC Inverters

This paper deals with the real-time simulation of power electronic converters. It discusses a new approach for designing embedded real-time simulators (eRTSs) that approximate the static and dynamic behavior of a power converter at the switching scale. The main concept is to approximate the voltage/current experimental characteristics of each switch using dedicated transfer functions obtained after a system identification process. The adaptive feature of such eRTS consists of developing varying and online reconfigurable coefficients transfer functions. The main potential of doing so is the possibility of reconfiguring the model according to the actual electrical/thermal environment where the power converter is used. Then, the latter is subdivided into independent switching cells, represented by dedicated RT models that are fully parallelized. Furthermore, using FPGA devices makes it possible to achieve very low latencies and, consequently, a short simulation time step. Previous work was published in this context, where this approach was deeply described and tested with half-bridge DC–DC, full-bridge DC–AC, and multi-level cascaded H-bridge (five-level and nine-level) power converters. This paper recalls the main basics and, more importantly, discusses additional case studies, namely a three-phase voltage source inverter, a half-bridge NPC (neutral-point clamped) inverter, and a three-phase NPC inverter.

Attention-based time series analysis for data-driven anomaly detection in nuclear power plants

To ensure nuclear safety, timely and accurate anomaly detection is of utmost importance in the daily condition monitoring of Nuclear Power Plants (NPPs), as any slight anomaly in a plant may result in an irreversible and serious accident, as well as high costs of maintenance and management. Nevertheless, due to the unique inherent attributes of anomalies, the difficulty of automatic detection in NPPs is increased. Previous model-driven anomaly detection approaches required skilled priori knowledge, leading to their limited usability. Commonly adopted deep learning-based data-driven anomaly detection approaches may not easily acquire the most relevant features when dealing with sensor data containing redundant information with uneven distribution of anomalies. To alleviate these issues, this paper proposes an attention-based time series model for anomaly detection to ensure safety in NPPs. First, we employ one-dimension convolutional neural network (1D-CNN) backbone for feature extraction to preserve original inherent features of time series inputs. Subsequently, we originally adopt soft attention mechanism to automatically extract the most relevant temporal features considering the specificity of anomaly detection in NPPs. The performance of the proposed model was experimentally validated on the High Temperature Gas-cooled Reactor (HTGR) anomaly case dataset simulated using the analytical code. The experimental results indicate that the proposed model was capable of detecting anomalies in NPPs with superior performance to the baseline model, while ensuring fast detection at short time steps.

A novel interpretable model ensemble multivariate fast iterative filtering and temporal fusion transform for carbon price forecasting

AbstractThe accurate forecasts of carbon prices can help policymakers and enterprises further understand the laws of carbon price fluctuations and formulate related policies and investment strategies. Nowadays, many carbon price prediction models have been proposed. However, some models ignore the time–frequency relationship when considering exogenous variables and fail to measure their importance to the forecasting results, leading to unsatisfactory results. Therefore, this study proposes a novel hybrid model for carbon price forecasting on the basis of advanced multidimensional time series decomposition techniques and interpretable multifactor models. In the proposed model, multivariate fast iterative filtering is used to decompose carbon price and its exogenous variable sequence into several intrinsic mode functions, which can overcome the nonlinearity and nonstationarity of carbon prices and obtain their intrinsic characteristics. Meanwhile, temporal fusion transform (TFT) is used to interpret predictions for multivariate time series. TFT is a new attention‐based deep learning model combining high‐performance multihorizon prediction and interpretability and can adaptively select the optimal features for carbon price prediction. Five carbon markets in Guangdong, Beijing, Shanghai, Hubei, and Shenzhen are selected for experimental studies. Empirical results indicate that the proposed model outperforms the compared benchmark models in all performance metrics. In the interpretable output of TFT, the prediction of the high‐frequency part requires the participation of exogenous variables and has a long time dependence; for the middle and low‐frequency part, only using the carbon price itself and a short time step can lead to good results. This finding can inform future research on carbon price forecasting and help policymakers.

Response of nonlinear oscillators with fractional derivative elements under evolutionary stochastic excitations: A Path Integral approach based on Laplace’s method of integration

In this paper, an approximate analytical technique is developed for determining the non-stationary response amplitude probability density function (PDF) of nonlinear/hysteretic oscillators endowed with fractional element and subjected to evolutionary excitations. This is achieved by a novel formulation of the Path Integral (PI) approach. Specifically, a stochastic averaging/linearization treatment of the original fractional order governing equation of motion yields a first-order stochastic differential equation (SDE) for the oscillator response amplitude. Associated with this first-order SDE is the Chapman–Kolmogorov (CK) equation governing the evolution in time of the non-stationary response amplitude PDF. Next, the PI technique is employed, which is based on a discretized version of the CK equation solved in short time steps. This is done relying on the Laplace’s method of integration which yields an approximate analytical solution of the integral involved in the CK equation. In this manner, the repetitive integrations generally required in the classical numerical implementation of the procedure are avoided. Thus, the non-stationary response amplitude PDF is approximately determined in closed-form in a computationally efficient manner. Notably, the technique can also account for arbitrary excitation evolutionary power spectrum forms, even of the non-separable kind. Applications to oscillators with Van der Pol and Duffing type nonlinear restoring force models, and Preisach hysteretic models, are presented. Appropriate comparisons with Monte Carlo simulation data are shown, demonstrating the efficiency and accuracy of the proposed approach.

Continuous Simulation Data Stream: A dynamical timescale-dependent output scheme for simulations

Exa-scale simulations are on the horizon but almost no new design for the output has been proposed in recent years. In simulations using individual time steps, the traditional snapshots are over resolving particles/cells with large time steps and are under resolving the particles/cells with short time steps. Therefore, they are unable to follow fast events and use efficiently the storage space. The Continuous Simulation Data Stream (CSDS) is designed to decrease this space while providing an accurate state of the simulation at any time. It takes advantage of the individual time step to ensure the same relative accuracy for all the particles. The outputs consist of a single file representing the full evolution of the simulation. Within this file, the particles are written independently and at their own frequency. Through the interpolation of the records, the state of the simulation can be recovered at any point in time. In this paper, we show that the CSDS can reduce the storage space by 2.76x for the same accuracy than snapshots or increase the accuracy by 67.8x for the same storage space whilst retaining an acceptable reading speed for analysis. By using interpolation between records, the CSDS provides the state of the simulation, with a high accuracy, at any time. This should largely improve the analysis of fast events such as supernovae and simplify the construction of light-cone outputs.

Embedded Fully FPGA-Based Real-Time Simulators for Static Power Converters With Power Switch Characteristics Approximated by Identification

This article suggests a new approach for designing embedded real-time (RT) simulators for power electronic converters. The main concept is to approximate the voltage/current characteristics of each power switch by dedicated coefficient varying transfer functions. The latterare obtained through system identification. The potential of such approach is the possibility to make this identification from real measurements and considering the end-user electrical/thermal environment. The converter is then structured into independent switching cells, represented by dedicated RT models that are executed in parallel. This is the same inside each switching cell model where the inherent transfer functions are simultaneously activated. Furthermore, each transfer function has been implemented with a parallel form giving the possibility to achieve high identification accuracy without compromising the timing performances. Then, with such full-parallel organization and using field programmable gate array devices, it is possible to achieve very low latencies and consequently, a short simulation time step. This article presents the developed proof of concept, applied to power converters with different levels of complexity, namely, a half-bridge dc–dc, a full-bridge dc–ac, and multilevel cascaded H-bridge (5-level and 9-level) power converters. For each case study, RT results are provided, analyzed, and compared withtheir offline counterparts.

A novel thermal model for PV panels with back surface spray cooling

To improve the performances of photovoltaic panels, water-based cooling systems have been considered as an interesting solution. This study proposes a novel mono-dimensional thermal model that includes the spray cooling phenomena on the back surface. The model is based on the energy balance method with a Finite Difference approach providing the thermal field across the thickness, and was validated with data of an experimental setup with a PV panel equipped with two spray nozzles. Simulations were carried out over a large number of days in the summer period with a short time step (5 s). Results showed an average RMSE of 1.37 °C, RMSEP of 0.005% and NSE of 96.5% for the back surface temperature, and an average RMSE of 3.39 W, RMSEP of 0.36% and NSE of 99.6% for the electric power. A further assessment of the cooling phenomenon highlighted the contribution of sensible and latent energy exchanges to the panel energy balance. Finally, a comparison of the performances with a non-cooled PV panel showed an average increase of 7.8% of the electric power in solar radiation peak hours and a reduction of 28.2% of the average cell temperature.

Experimental analysis of grills configuration for an open joint ventilated facade in summertime

This paper focuses on the experimental analysis of a ventilated façade made of recycled materials, under typical summertime Mediterranean conditions. The test area is around 16 m2 and it is equipped with several sensors for a complete seasonal characterization of the chimney phenomenon and the thermal performance. Indeed, despite several studies are available in the literature, these are usually based on short monitoring campaign and the experimental installations have limited size. The study introduces a new statistical approach for selecting the daily reference scenarios aimed at the comparison of the thermal field and the ventilation with two degrees of openness of the grids in the air-gap. A seasonal analysis is also developed by means of violin plots constructed in order to study both the real distribution and the statistical indexes of the analyzed parameters.The analysis indicated that the opened channel is always characterized by higher air flow speeds with 75% of monitored values under 0.4 m/s and peak values near 2.0 m/s compared to the semi-closed configuration. In the seasonal analysis, the surface temperature of the insulation has a median values 4.5 °C lower than the semi-closed configuration meanwhile the third quartile is respectively 36.5 °C and 41.1 °C. Considering the temperature differences across the back wall, with the opened configuration, around 25% values indicated an outgoing conductive heat flow meanwhile during the sunniest hours, the median value is 3.3 °C in case of opened channel and 7.4 °C in the semi-closed one. The proposed method and results could help designers for select the best performing solution for their climatic conditions and researchers to develop more accurate analysis with short and long time-step.

Correlation between the sum of the flux conduction transfer function coefficients and a dimensionless function of the thermal properties of building walls

The estimation of the conduction transfer function (CTF) coefficients is the main step to obtain the transient heat flows on the inside and outside surfaces of a multilayer wall, and thus the heating and cooling loads in a building. Taking a wide variety of construction types, 76 walls from the ASHRAE Handbooks of Fundamentals, it is demonstrated that the aspects of wall transient thermal behaviour can be summarised by a non-dimensionalised function V of the thermal capacity and resistance of the layers of the wall. The sum of the flux CTFs, Σdk, is highly correlated with this measure and the correlation coefficient increases when the sampling time interval decreases. The current interest for nearly zero energy buildings demand management techniques requires the calculation of the CTFs for highly insulated walls and short sampling intervals. The study is extended to the possibility of the CTFs with shorter time steps but longer time steps are needed for thick walls. The theory of the plain slab runs as a thread through the study: the scatter of wall Σdk values cluster around the Σdk:V relation for a slab; the response factor function (exit flux following imposition of a temperature change at the other surface) follows readily from a classical expression in transient thermal response (and from which the slab CTF’s readily follow), and an appendix presents the slab’s recently discovered exact Σdk:V relation.

Development of a free surface flow solver using the Ghost Fluid Method on OpenFOAM

This study is devoted to the development of a VoF/GFM solver using the open source CFD library OpenFOAM. An industrial point of view is adopted in this paper, meaning that simulations are run with large CFL conditions. Weaknesses of the standard OpenFOAM VoF solver interFoam are identified (free surface wiggles and light phase accelerations). Using available literature, MULES algorithm has been abandoned to the profits of algebraic schemes (HRIC, MHRIC, MCICSAM) and the Ghost Fluid Method (GFM, Fedkiw et al. (1999)) has been adopted for dealing with free surface discontinuity. The resulting solver called marineFoam is tested and compared to interFoam on the following test cases : 3D wave impact on a tall structure (dam break) (Gomez-Gesteira, 2013), periodic wave propagation simulation (Larsen et al., 2019), model and full-scale ship resistance simulations (Larsson et al., 2014; Tokyo Workshop, 2015; Lloyd’s Register, 2016). marineFoam can allow large CFL numbers while avoiding free surface wiggles and most of spurious currents. interFoam achieves reliable results but requires additional VoF sub-cycle iterations or a shorter time step.

Overlong simulations of the solar system dynamics with two alternating step-lengths

We compare the performance of four symplectic integration methods with leading order symplectic corrector in simulations of the Solar System. These simulations cover 10 Gyr. They are longer than the astrophysical predicted future of the present-day Solar System, thus this work is mainly a study of the integration methods. For the outer Solar System simulation, where the used stepsize was 100 days, the energy errors do not show any secular evolution. The maximum errors show a dependence on the method. The simulations of the full Solar System from Mercury, and including Pluto as a test particle, were calculated with a stepsize of 7 days. The energy errors behave somewhat differently having a small secular behavior. This may due to the short timestep and the short period of the planet Mercury or some small round off error produced by the code. Comparison of the eccentricity evolution’s within simulations show that some planets are dynamically strongly coupled. Venus and Earth form a dynamical pair, also Jupiter and Saturn form a dynamical pair. The FFT of the analysis of the simulations suggests that all the giant planets form a single dynamical quadruple system. The orbit of Mercury is possibly unstable. Each simulation is stopped when Mercury is expelled. All the methods show similar results for times less than 30, Myr in the way that the results for orbital elements are same within plotting precision. Inclusion of Mercury in simulations shortens the Solar System e-folding time to 3.3, Myr. It is clear that chaos has a strong effect in the evolution of orbital elements, especially eccentricities. This is easily seen in Mercury’s orbit when the simulation time exceeds at least 30, Myr. Our low-order simulations seem to match high-order methods over long timescales.

Analysis of the Time Step Influence in the Yearly Simulation of Integrated Seawater Multi-Effect Distillation and Parabolic trough Concentrating Solar Thermal Power Plants

The joint demand for power and freshwater is continuously increasing due to population growth, the rise of economic activity, and climate change. Integrated concentrating solar thermal power and desalination (CSP+D) plants may provide a key solution for the pressing freshwater deficit and energy problems in many regions of the world. Simulation tools with an accurate prediction of the yearly electric energy and freshwater production are needed. This paper analyzed the influence of the time step in the annual simulation of a CSP+D plant composed of a seawater multi-effect distillation unit and a parabolic trough concentrating solar thermal power plant, considering the location of Tabernas (Spain). A dynamic simulation tool of this system was developed, implementing the models in Engineering Equation Solver. The annual electricity and water productions obtained for the study case considered were 154 GWh and 3.45 hm3, respectively, using 5 min time steps, and 94 GWh and 2.1 hm3, respectively, with 1 h time steps. The results obtained show that a short time step interval (5 min) is recommended when using the detailed CSP model considered, which is prepared for simulation with short time steps. Step times of 1 h lead to excessive errors (about 30% in summer and 100% in winter), which underestimate the actual production.

Numerical aspects of a Godunov-type stabilization scheme for the Boltzmann transport equation

We discuss the numerical aspects of the Boltzmann transport equation (BE) for electrons in semiconductor devices, which is stabilized by Godunov’s scheme. The k-space is discretized with a grid based on the total energy to suppress spurious diffusion in the stationary case. Band structures of arbitrary shape can be handled. In the stationary case, the discrete BE yields always nonnegative distribution functions and the corresponding system matrix has only eigenvalues with positive real parts (diagonally dominant matrix) resulting in an excellent numerical stability. In the transient case, this property yields an upper limit for the time step ensuring the stability of the CPU-efficient forward Euler scheme and a positive distribution function. Similar to the Monte-Carlo (MC) method, the discrete BE can be solved in time together with the Poisson equation (PE), where the time steps for the PE are split into shorter time steps for the BE, which can be performed at minor additional computational cost. Thus, similar to the MC method, the transient approach is matrix-free and the solution of memory and CPU intensive large systems of linear equations is avoided. The numerical properties of the approach are demonstrated for a silicon nanowire NMOSFET, for which the stationary I–V characteristics, small-signal admittance parameters and the switching behavior are simulated with and without strong scattering. The spurious damping introduced by Godunov’s (upwind) scheme is found to be negligible in the technically relevant frequency range. The inherent asymmetry of the upwind scheme results in an error for very strong scattering that can be alleviated by a finer grid in transport direction.

Understanding creep in TiAl alloys on the nanosecond scale by molecular dynamics simulations

Molecular dynamics (MD) simulations of creep generally face the problem that the creep most often evolves on time scales hard to capture with MD due to their typically short time step size. Consequently, MD studies of creep often use unrealistically high temperatures and stresses and simplified atomistic models to make creep-like processes happen on computationally accessible time scales. Apparently, this compromises the physical reliability of such studies. To alleviate this problem, we designed an MD model of titanium aluminide (TiAl) with a microstructure matching at least many of the key parameters of experimentally observed microstructures. We applied this MD model with stresses much lower than the ones used in most previous creep studies (well below yield stress) and in the temperature range 0.55TM-0.7TM, with melting temperature TM. Compared to typical previous MD studies, this much more realistic setup produces creep rates more than three orders of magnitude smaller and thus much closer to reality. We identified the driving mechanisms of primary creep on the nanosecond scale that agree very well with recent experimental observations, thus contributing towards the overarching goal of bridging the gap between atomistic creep simulations and continuum-scale creep simulations for engineering applications.

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.