Post-processing Results Research Articles

A family of mixed finite elements for the biharmonic equations on triangular and tetrahedral grids

This paper introduces a new family of mixed finite elements for solving a mixed formulation of the biharmonic equations in two and three dimensions. The symmetric stress σ = −∇2u is sought in the Sobolev space H(divdiv, Ω; $$\mathbb{S}$$ S ) simultaneously with the displacement u in L2(Ω). By stemming from the structure of H(div, Ω; $$\mathbb{S}$$ S ) conforming elements for the linear elasticity problems proposed by Hu and Zhang (2014), the H(divdiv, Ω; $$\mathbb{S}$$ S ) conforming finite element spaces are constructed by imposing the normal continuity of divσ on the H (div, Ω; $$\mathbb{S}$$ S ) conforming spaces of Pk symmetric tensors. The inheritance makes the basis functions easy to compute. The discrete spaces for u are composed of the piecewise Pk−2 polynomials without requiring any continuity. Such mixed finite elements are inf-sup stable on both triangular and tetrahedral grids for k ⩾ 3, and the optimal order of convergence is achieved. Besides, the superconvergence and the postprocessing results are displayed. Some numerical experiments are provided to demonstrate the theoretical analysis.

Design and Optimization of Rear Wheel Assembly for All-Terrain Vehicle

This paper describes the design and optimisation of rear wheel assembly for an all-terrain vehicle. The components of rear wheel assembly are designed with an optimised design intent with light weight material selection. The designed assembly is then discretised using finite element modelling through Hexa-dominant 3D mesh. The upright knuckle and the hub are assessed for their integrity check and the performance of full assembly is verified through simulation. From the post-processed stress results, factor of safety for the designed parts is presented. The presented work can be expanded to optimise the upright and the hub based the resolved gradient stress plots from the finite element analysis.

Quantifying inter- and intra-ply shear in the deformation of uncured composite laminates

Understanding the bending mechanics of uncured carbon fiber prepreg is vital for modeling forming processes and the formation of out-of-plane wrinkling defects. This article presents a modification of standard dynamic mechanical analysis (DMA) to characterize the viscoelastic bending mechanics of uncured carbon fiber prepreg using Timoshenko beam theory, along with an updated model describing inter-ply shear in uncured laminates. By post-processing DMA results, the analysis provides temperature and rate-dependent values of inter and intra-ply shear stiffness for a carbon fiber laminate and each individual ply with experimental results for AS4/8552 presented. The new methodology provides a means to parametrize process models, and also gives an indication of optimal manufacturing conditions to enable defect-free forming and consolidation processes.

A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations

• Machining operations often require a post-process quality inspection step. • Robotic machining introduces further sources of errors in the manufacturing process. • In-process sensor data is used to infer key process quality indicators. • A GPR-based process model provides predictions with uncertainty quantification. • The machined part quality is robustly predicted from in-process information. A typical manufacturing process consists of a machining (material removal) process followed by an inspection system for the quality checks. Usually these checks are performed at the end of the process and they may also involve removing the part to a dedicated inspection area. This paper presents an innovative perturbation signal based data generation and machine learning approach to build a robust process model with uncertainty quantification. The model is to map the in-process signal features collected during machining with the post-process quality results obtained upon inspection of the finished product. In particular, a probabilistic framework based on Gaussian Process Regression (GPR) is applied to build the process model that accurately and reliably predicts key process quality indicators. Raw data provided by multiple sensors including accelerometers, power transducers and acoustic emissions is first collected and then processed to extract a large number of signal features from both time and frequency domains. A strategy for the selection of most relevant features is also explored in this work in order to reduce the input space dimension and achieve faster training times. The proposed GPR model was tested on a multi-robot countersinking application for monitoring of the machined countersink depths in composite aircraft components. Experimental results showed that the model can be used as a tool to predict the part quality through in-process sensory information, which in turn, helps to reduce the total inspection time by identifying the parts that would require further investigation.

Crank-Angle Resolved Flow Measurements in the Intake Duct of a Research Engine Using Novel and Fast Response Aerodynamic Probes

Despite the public debate nowadays on the future of Internal Combustion Engines (ICE), which is impeding their development, one limitation towards further optimization of ICE in terms of fuel consumption and emissions can be seen in the current approach and more specifically in the transient engine operation and its control. The main drawbacks in the current approach source from: 1) complex structure of mechanization including sensors and actuators, 2) low time resolution and accuracy of sensing (cost driven), 3) complex Electronic Control Unit (ECU)-software architecture associated with huge calibration effort and 4) recently, funded research due to unsecure business model of ICE is becoming less. To overcome these difficulties unexploited potential should be utilized. Some of this potential lies in cycle-by-cycle and cylinder-by-cylinder accurate fuel and air control, and in the development of physical based virtual sensors with high time resolution and accuracy. One of the main motivations for this study was to develop a measurement technique that enables crank-angle resolved air mass flow rate measurements during engine operation in a dynamometer test cell. The measurement principle is quite simple and is based on gauging the dynamic pressure in both the intake and exhaust duct at the closest possible positions to the valves. To fulfill these requirements aerodynamic probes have been developed and manufactured utilizing 3D printing. The probes have been integrated in special developed flanges, which correspond exactly to the shape of the air channels in the cylinder head of the engine. Hence, they can be mounted either in front of the valves at the intake or behind the valves at the exhaust duct. Results at different engine operating conditions have been obtained, analyzed and correlated to other sensors like air-flow meter. Those post-processed results can be further used to validate 1-D gas exchange models, or 3-D Computational Fluid Dynamics (CFD) port flow models. The ultimate scope of these measurements is to calibrate fast physical-based gas exchange models that can be directly used in the engine control framework on an embedded system.

HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40 kb bin) but is invalid at high resolution (e.g., 5 or 10 kb bins) because spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field-based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT)-based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line, GM12878, and mouse embryonic stem cells (mESCs). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal-to-noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks in both GM12878 and mESCs. HiC-ACT, effectively a summary statistics-based approach, is computationally efficient (∼6min and ∼2 GB memory to process 25,000 pairwise interactions).

Study of the effect of baffles on longitudinal stability of partly filled fuel tanker semi-trailer using CFD

Sloshing of liquid in partially filled fuel tanker vehicles has a strong effect on the directional stability and safety performance. Under the maneuver of the vehicle, such as steering, braking, or accelerating, the liquid fuel in the tanker tends to oscillate. As a result, hydrodynamic forces and moments raise. It leads to reduce the stability limit and the controllability of the vehicle. To minimize the effect of sloshing, the baffles are usually added to the tanker. This paper presents the study of the effect of baffles on the longitudinal stability of the fuel tanker semi-trailer using the computational fluid dynamics (CFD) approach. A three-dimensional fluid dynamic model of a typical tanker with different baffle configurations is developed. The User Defined Function (UDF) is used to control the acceleration of the tanker according to the simulation scheme. Transient simulations are performed for the cases of constant acceleration longitudinal maneuvers with different levels of fuel in the tanker. The volume of fluid (VOF) and air obtained from the simulation is used to indirectly calculate the center of gravity of the tanker. The post-processing results show that the baffles could provide resistance to the fluid sloshing, resulting in an improvement of the longitudinal stability of the tanker semi-trailer. The results also prove that the benefit of the baffle to the fuel tanker vehicle’s stability depends on the size of the baffle, as well as the number of baffles. The 40% height three baffles model is the proper baffle model to resist the longitudinal sloshing in the partially filled tanker of the studied trailer. By adding baffles, shifting of load on the kingpin and the rear axis are less than 5% and 2% as the tanker is filled with 50% and 70% fluid level respectively.

ANSWERS® TOOLS FOR UNCERTAINTY QUANTIFICATION AND VALIDATION

ANSWERS® is developing a set of uncertainty quantification (UQ) tools for use with its major physics codes: WIMS/PANTHER (reactor physics), MONK (criticality and reactor physics) and MCBEND (shielding and dosimetry). The Visual Workshop integrated development environment allows the user to construct and edit code inputs, launch calculations, post-process results and produce graphs, and recently uncertainty quantification and optimisation tools have been added. Prior uncertainties due to uncertainties in nuclear data or manufacturing tolerances can be estimated using the sampling method or using the sensitivity options in the physics codes combined with appropriate covariance matrices. To aid the user in the choice of appropriate validation experiments, the MONK categorisation scheme and/or a similarity index can be used. An interactive viewer has been developed which allows the user to search through, and browse details of, over 2,000 MONK validation experiments that have been analysed from the ICSBEP and IRPhE validation sets. A Bayesian updating approach is used to assimilate the measured data with the calculated results. It is shown how this process can be used to reduce bias in calculated results and reduce the calculated uncertainty on those results. This process is illustrated by application to a PWR fuel assembly.

7T MRI with post-processing for the presurgical evaluation of pharmacoresistant focal epilepsy.

Background:We aimed to evaluate the diagnostic yield of seven-tesla (7T) magnetic resonance imaging (MRI) with post-processing of three-dimensional (3D) T1-weighted (T1W) images by the morphometric analysis program (MAP) in epilepsy surgical candidates whose 3T MRI results were inconclusive or negative.Methods:We recruited 35 patients with pharmacoresistant focal epilepsy. A multidisciplinary team including an experienced neuroradiologist evaluated their seizure semiology, video-electroencephalography data, 3T MRI and post-processing results, and co-registered FDG-PET. Eleven patients had suspicious lesions on 3T MRI and the other 24 patients were strictly MRI-negative. 7T MRI evaluation was then performed to aid clinical decision. Among patients with pathologically proven focal cortical dysplasia (FCD) type II, signs of FCD were retrospectively evaluated in each MRI sequence (T1W, T2W, and FLAIR), and positive rates were analyzed in each MAP feature map (junction, extension, and thickness).Results:7T MRI evaluation confirmed the lesion in nine of the 11 (81.8%) patients with suspicious lesions on 3T MRI. It also revealed new lesions in four of the 24 (16.7%) strictly MRI-negative patients. Histopathology showed FCD type II in 11 of the 13 (84.6%) 7T MRI-positive cases. Unexpectedly, three of the four newly identified FCD lesions were located in the posterior quadrant. Blurred gray–white boundary was the most frequently observed sign of FCD, appearing on 7T T1W image in all cases and on T2W and FLAIR images in only about half cases. The 7T junction map successfully detected FCD (10/11) in more cases than the extension (1/11) and thickness (0/11) maps. The 3D T1W images at 7T exhibited superior cerebral gray–white matter contrast, more obviously blurred gray–white boundary of FCD, and larger and brighter positive zones in post-processing than 3T T1W images.Conclusion:7T MRI with post-processing can enhance the detection of subtle epileptogenic lesions for MRI-negative epilepsy and may optimize surgical strategies for patients with focal epilepsy.

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Abstract. As the deep learning algorithm has become a popular data analysis technique, atmospheric scientists should have a balanced perception of its strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. Despite the enormous success of the algorithm in numerous applications, certain issues related to its applications in air quality forecasting (AQF) require further analysis and discussion. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network (CNN), in two common applications: (i) a real-time AQF model and (ii) a post-processing tool in a dynamical AQF model, the Community Multi-scale Air Quality Model (CMAQ). In both cases, the CNN model shows promising accuracy for ozone prediction 24 h in advance in both the United States of America and South Korea (with an overall index of agreement exceeding 0.8). For the first case, we use the wavelet transform to determine the reasons behind the poor performance of CNN during the nighttime, cold months, and high-ozone episodes. We find that when fine wavelet modes (hourly and daily) are relatively weak or when coarse wavelet modes (weekly) are strong, the CNN model produces less accurate forecasts. For the second case, we use the dynamic time warping (DTW) distance analysis to compare post-processed results with their CMAQ counterparts (as a base model). For CMAQ results that show a consistent DTW distance from the observation, the post-processing approach properly addresses the modeling bias with predicted indexes of agreement exceeding 0.85. When the DTW distance of CMAQ versus observation is irregular, the post-processing approach is unlikely to perform satisfactorily. Awareness of the limitations in CNN models will enable scientists to develop more accurate regional or local air quality forecasting systems by identifying the affecting factors in high-concentration episodes.

Performance analysis of double-groove cutter based on ABAQUS numerical simulation

In order to study the influence of double-slotted cutter on chip forming, this paper uses the general finite element simulation software ABAQUS, and builds a finite element model based on Johson-cook material model and Johson-cook fracture criterion. This paper analyzes the changes in cutting force and cutting temperature obtained when cutting titanium alloys with three different double-slot structures. This paper draws the cutting force and cutting temperature change curve at the tool tip, and analyzes and compares the post-processing results of the three geometries. The test results show that the double concave arc groove cutter has less cutting force and cutting temperature during cutting, and the cutting process is relatively stable, which is more suitable for cutting than other groove types.

Performance assessment of real-time multi-GNSS integrated PPP with uncombined and ionospheric-free combined observables

For precise position services, the real-time precise point positioning (PPP) is a promising technology. The real-time PPP performance is expected to be improved by multi-system combination. The performance of real-time multi-system PPP needs to be periodically investigated, with the increasing number of available satellites and the continuously improved quality of real-time precise products of satellite clocks and orbits. In this study, a comprehensive performance assessment is conducted for the four-system integrated real-time PPP (FSIRT-PPP) with GPS, BDS, Galileo and GLONASS in both static and kinematic modes. The datasets from 118 stations spanning approximately a month are used for analysis, and the real-time stream CLK93 is employed. The superior performance of FSIRT-PPP is validated by comparing with the results of GPS/BDS, GPS/Galileo, GPS/GLONASS, GPS-only, BDS-only, Galileo-only and GLONASS-only cases. The FSIRT-PPP using ionospheric-free (IF) combined observables can achieve a convergence time of 10.9, 4.8 and 11.8 min and a positioning accuracy of 0.4, 0.5 and 0.7 cm in the static mode in the east, north and up directions, respectively, while the derived statistic is 15.4, 7.0 and 16.4 min, and 1.6, 1.2 and 3.4 cm in the kinematic mode in the three directions, respectively. Moreover, we also compare the position solutions of real-time PPP adopting IF combined and uncombined (UC) observables, and prove the mathematical equivalence between the two PPP models in the converged stage, provided that there are no external ionospheric corrections or constraints given to the estimated ionospheric delays in the UC model. The difference between the fully converged positioning accuracy of IF-based and UC-based real-time PPP is marginal, but the UC-based real-time PPP has longer convergence time due to the influence of the significant unmodeled time-varying errors in the real-time precise products as well as the different parameterization between them. For completeness, the real-time kinematic PPP results in harsh environments and the post-processed PPP results are also presented.

Assessment of Static Positioning Accuracy Using Low-Cost Smartphone GPS Devices for Geodetic Survey Points’ Determination and Monitoring

Recent developments enable to access raw Global Navigation Satellite System (GNSS) measurements of mobile phones. Initially, researchers using signals gathered by mobile phones for high accuracy surveying were not successful in ambiguity fixing. Nowadays, GNSS chips, which are built in the latest smartphones, deliver code and primarily carrier phase observations available for detailed analysis in post-processing applications. Therefore, we decided to check the performance of carrier phase ambiguity fixing and positioning accuracy results of the latest Huawei P30 pro smartphone equipped with a dual-frequency GNSS receiver. We collected 3 h of raw static data in separate sessions at a known point location. For two sessions, the mobile phone was mounted vertically and for the third one—horizontally. At the same time, a high-class geodetic receiver was used for L1 and L5 signal comparison purposes. The carrier phase measurements were processed using commercial post-processing software with reference to the closest base station observations located 4 km away. Additionally, 1 h sessions were divided into 10, 15, 20 and 30 min separate sub-sessions to check the accuracy of the surveying results in fast static mode. According to the post-processing results, we were able to fix all L1 ambiguities based on Global Positioning System (GPS)-only satellite constellation. In comparison to the fixed reference point position, all three 1 h static session results were at centimeters level of accuracy (1–4 cm). For fast static surveying mode, the best results were obtained for 20 and 30 min sessions, where average accuracy was also at centimeters level.

Delamination propagation under high loading rate

Analytical theory for the dynamic delamination behavior of a double cantilever beam (DCB) under high loading rate is developed. Structural vibration and wave dispersion are considered in the context of Euler-Bernoulli beam theory. The theory is developed for both initiation and propagation of delamination in mode I. Two solutions for the energy release rate (ERR) are given for a stationary delamination: an accurate one and a simplified one. The former is based on global energy balance, structural vibration and wave dispersion; the latter is ‘local’ since it is based on the crack-tip bending moment. For the simplified solution to be accurate, sufficient time is needed to allow the establishment of all the standing waves. For a propagating delamination, a solution for the ERR is derived using the same simplification with the crack-tip bending moment. The obtained ERR solutions are verified against experimental data and results from finite-element simulations, showing excellent agreement. One valuable application of the developed theory is to determine a material’s dynamic loading-rate-dependent delamination toughness by providing the analytical theory to post-process test results of dynamic DCB delamination.

Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

Lactate as clinical tumour biomarker: Optimization of lactate detection and quantification in MR spectroscopic imaging of glioblastomas

PurposeIncreased lactate (Lac) level in brain tumours is in vivo detectable by 1H MR spectroscopy (MRS) but is frequently overlapped by strong lipid signals, which either leads to a weak quality of the Lac signal or even inhibit its detection. We sought to optimize the separation of Lac from lipid signals in intermediate-echo time MRS acquisitions thus allowing its applicability as clinical biomarker in glioblastomas. MethodsData of 27 patients with glioblastoma multiforme (GBM) were analysed using standard post-processing software as well as in-house modified technique based on the same commercially available software. The patients’ Lac concentration values provided by the MRS post-processing technique were converted to realistic concentrations by using an appropriately calibrated phantom. The Cramér-Rao lower bound (%CR) was the principal criterion for estimating the quality of the MRS post-processing results. ResultsBased on the ex vivo calibration, the analysis of the in vivo MR spectroscopy measurements led to an improvement of the %CR(Lac) value from 18 % to 8 %. In a single case, the detection of Lac was achievable only by the modified technique, as Lac signal was contaminated with lipids using the standard analysis. The resulting in vivo Lac values from the modified analysis (median: 4.77 mmol/l, range: 1.5–9.2) were considered as a realistic order of magnitude for the metabolite concentrations, whereas no Lac was identified in the normal appearing white matter. This qualified also Lac mapping as a biomarker for regional heterogeneity in GBM. ConclusionsThe proposed methodology is a promising first step for more reliable analysis of Lac signal, decontaminating it from lipid peaks in MRS, and may help to establish Lac as a biomarker for brain tumours in clinical routine.

Effect of pipe characteristics in umbrella arch method on controlling tunneling-induced settlements in soft grounds

Abstract Recent developments in tunneling have stimulated design practitioners to more effectively utilize the underground spaces. However, tunneling at shallow depth in soft grounds gives rise to concerns associated with tunnel instability. Umbrella arch method (UAM), as a pre-reinforcement approach of tunnels in complex geological conditions, is widely used to maintain the tunnel stability. Quantitative assessment of the impacts of the entire approach and forepoling pipe features on tunnel stability remains challenging due to the complex nature of the UAM application. This study aimed to assess the effect of pipe design parameters on reinforcing the tunnels excavated in soft grounds. This practical investigation considered the actual field conditions attributed to the tunneling procedure and UAM deployment. Then, the tunneling process was modeled and the tunnel excavation-induced settlements were calculated. The post-processed results confirmed that deploying the UAM substantially reduced the tunnel crown and ground surface settlements by 76% and 42%, respectively. Investigation of various design parameters of pipes underscored the significance of incorporating the optimum value for each individual parameter into design schemes to more effectively control the settlements. Additionally, contrasting the settlement reduction rates (SRRs) for pipe design variables showed that the tunnel stability is more sensitive to the changes in the values of diameter and length, compared to values of the installation angle and center-to-center distance of the pipes.

Evaluation of Air Side Characteristics Performance of Finned Tube Evaporator

Improvement of Refrigeration system performance should be supported by increasing efficiency of its components. Mainly, two components must have excellent performance are condenser and evaporator. In this study examined a finned tube heat exchanger for evaporator application, furthermore research goal is aimed to enhance the evaporator designs in order to get higher efficiency and effectiveness of the heat exchanger. The effects of distance of fin, shape and size of the fin and combination of tube, will be investigated deeply and the heat transfer coefficient is going to examine using U-LMTD method based on some parameters of heat. This paper is still focus on design CFD model to comply the research purpose. The results were found that geometry design and meshing already compatible to get reasonable post processing results. Analysis of the air velocity vector in the heat exchanger showed good agreement toward references. Vortex and turbulence occurs in side are of the tubes and wake or stagnant flow seems in the behind tubes. The next examine is going to concern with model validation toward experimental results and investigated heat transfer coefficient in details.

Massively parallel numerical simulation using up to 36,000 CPU cores of an industrial-scale polydispersed reactive pressurized fluidized bed with a mesh of one billion cells

For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon.

Enhancing device-independent estimation of quantum randomness from real experimental data

In the protocols of device-independent quantum random number generator, it is essential to certify the randomness of the generated numbers. However, statistical fluctuations in measurement data might break the nosignaling conditions, making those data unphysical. In this letter, we proposed to circumvent this problem by using the standard constrained least-squares method. As examples, we apply our present approach on post-processing two different experimental data by utilizing either CHSH correlations or complete measurement statistics, and do comparisons with the original distribution. The post-processing results not only prove the effectiveness of our work to solve the unphysical problem, but also show that randomness certified by processing complete measurement statistics is always larger than through Bell-like inequalities.