Post-processing Tools Research Articles

Capturing fracture initiation and crack propagation of car windshields

To facilitate the development of accurate material models for glass, there is a need for reliable experimental procedures that capture the stochastic nature of the material. The experimental setup must be easy to model numerically and output relevant data for validation of important material characteristics. We conducted an experimental study on three types of windshields exposed to quasi-static loading. A robust and repeatable testing procedure was designed with the aim of revealing the stochastic behavior of windshields. 3D-Digital Image Correlation (3D-DIC) was used to monitor the windshield displacement field and we were able to capture fracture initiation locations and crack propagation data by using high-speed cameras combined with a pose estimation-based post-processing tool implemented in Python. We performed finite element (FE) simulations in LS-DYNA to estimate major principal stress values at first fracture. The results from the experimental study provided new insight and valuable data for the development of numerical models of the problem at hand.

Water Level in Observation Wells Simulated From Fracture and Matrix Water Heads Outputted by Dual‐Continuum Hydrogeological Models: POWeR‐FADS

AbstractDo observation wells in fractured porous aquifers measure water head in the fracture network, water head in the matrix, or some combination of both? This question necessarily arises when calibrating dual‐continuum hydrogeological models against on‐field data. One can assume that observation wells measure fracture water head, because matrix permeability is negligible compared to fracture permeability. Nevertheless, this reasoning is invalid for wells poorly‐connected to the fractures. Yet, the possibility of such a poor connection at given depths has never been implemented in a physics‐based manner when comparing matrix and fracture water heads simulated by dual‐continuum models to on‐field data. To fill this knowledge gap, a physically based, easy to calibrate, open‐source postprocessing tool, POWeR‐FADS (Program for Observation Well Representation in Fractured Aquifer Dual‐continuum Simulations), available at https://github.com/BJeannot1/POWeR-FADS, has been developed. It introduces as parameters well geometry and the altitude of lowest interception of the fractures by the well. From these, POWeR‐FADS nonintrusively postprocesses time series of matrix and fracture water heads at the well, as simulated by any planar, bidimensional dual‐continuum hydrogeological model, to calculate water exchanges involving the observation well and thus the evolution of water level in the well. Synthetic test cases show that POWeR‐FADS makes it possible to simulate peculiar behaviors that are similar to patterns actually observed by the authors in on‐site observation wells of a fractured porous aquifer, like “floors” in observed water levels, delayed but sharp rises at the beginning of recharge events, or inflexion points accelerating the drawdown velocity during the recession phase.

Simulation of the liquid flow distribution in laboratory-scale additively manufactured structured packings

This article introduces a technique to simulate the liquid flow inside different additively manufactured structured packings with enclosing column wall on laboratory scale using the software OpenFOAM. The physical problem is handled by means of a periodic setup and makes use of common numerical discretization techniques of the software. The periodic setup reduces the required computational resources while permitting simulations of the liquid distribution in a large part of the column even for complex packing structures. A novel post-processing tool is used for a systematic evaluation of the liquid distribution. When optimizing packing structures, these computational fluid dynamics simulations eliminate the necessity to manufacture every developed geometry. As a proof of concept, this work simulates the liquid flow inside two additively manufactured packing structures. The results are validated with experimental data and show, that an improved liquid distribution can be obtained inside laboratory-scale packing structures.

A Parallel Computational–Statistical Framework for Simulation of Turbulence: Applications to Data-Driven Fractional Modeling

In this work, an open-source computational–statistical platform to obtain synthetic homogeneous isotropic turbulent flow and passive scalar transport is presented. A parallel implementation of the well-known pseudo-spectral method in addition to the comprehensive record of the statistical and small-scale quantities of the turbulent transport are offered for executing on distributed memory CPU-based supercomputers. The user-friendly workflow and easy-to-run design of the developed package are disclosed through an extensive and step-by-step example. The resulting low- and high-order statistical records vividly verify a well-established and fully developed turbulent state as well as the seamless statistical balance of conservation laws. The post-processing tools provided in this platform would allow the user to easily construct multiple important transport quantities from primitive turbulent fields. The programming codes for this tool are accessible through GitHub (see Data Availability Statement).

Data-driven correction of coarse grid CFD simulations

Computational fluid dynamics is a cornerstone of the modern aerospace industry, providing important insights through aerodynamic analysis while reducing the need for expensive experiments and tests. A consistent spatial discretization on so-called grids approximates the analytical solution of the partial differential equation with increasing number of discrete points. But computing a rigorous amount of CFD simulations on fine grids can be a daunting task due to the high computational cost involved. Thus, it is of interest to find methods which generate accurate results while keeping computational costs low. This work proofs that machine learning as a post-processing tool is capable of improving the accuracy of coarse grid simulations. The results show that three machine learning models with varying complexity, namely random forest, neural network, and graph neural network, are capable of finding patterns in coarse grid simulations. These patterns are used for a vertex-wise prediction of the discretization error to approximate the field variables of interest of the corresponding fine grid simulation mapped onto the coarse grid. Initial training and testing is performed on the two dimensional RAE2822 airfoil leading to corrected flow fields, improved surface integrals and coefficients, even when shocks are present. Additional tests are performed on the RAE5212 airfoil, showing the generalization limits of the trained models. Finally, the training and prediction is showcased on a three dimensional geometry. The proposed method promises to reduce computational expenses while increasing the accuracy of the coarse grid results which works locally, e.g. it corrects the error for each vertex individually and is therefore not restricted by the number of grid points. The presented results obtained by the machine learning models during post-processing are a promising baseline for more integrated developments, where the models will interact in a dynamic fashion with the flow solver to further improve coarse grid simulations.

High-resolution reconstruction of non-motorized trajectory in shared space: A new approach integrating the social force model and particle filtering

High-resolution trajectory data from the intersection shared space (ISS) is an ideal resource for testing autonomous driving and studying microscopic traffic flow theory in urban road traffic environments. It is common to use computer vision (CV) technologies to extract trajectory data from camera recordings at the ISS. However, it is challenging to accurately extract non-motorized (NONM) trajectories due to vehicle occlusion and mutual interference, which cause trajectory noise and missing. This study proposed a novel NONM trajectory reconstruction method, which integrates the social force model with the particle filter to reduce trajectory noise. The approach was tested on trajectories extracted from two advanced CV algorithms (ie, Yolov7 and MASK RCNN), and its performance was compared with seven SOTA methods. The results show that the proposed method achieved a much better RMSE (0.6 meters) than both the baseline (unprocessed CV trajectories, the RMSE is 1.16 meters) and selected SOTA methods (the best RMSE is 0.78 meters). The contribution of the study is to enhance the classical noise reduction algorithm by introducing the driving interaction model; ablation experiments and sensitivity analysis further demonstrate the importance of the driving interaction model in reducing trajectory noise and the stability of the method. The approach also includes an LSTM-Hungarian part to make up the missing trajectory. The proposed method is expected to serve as a useful post-processing tool for current CV algorithms.

A semi-automatic tool to quantify training load in endurance athletes

Abstract Funding Acknowledgements Type of funding sources: None. Background Despite the widespread use of wearables in endurance sports allowing the measurements needed for quantification of the training load (TL), studies in sports cardiology consistently use less reliable questionnaires to assess TL.[1] This scant use of activity trackers to determine TL can be explained by the large workload for data analysis. Purpose We aimed to develop a semi-automated post-processing tool that allows quantifying TL, based on heart rate (HR) monitoring by commercial wearables, for future use in clinical and epidemiological studies. We compared the calculated maximal HR automatically derived from HR monitoring during endurance training with the maximal HR obtained during incremental exercise testing in the lab. We describe TL calculated by our tool in young and older endurances athletes and compare it with the TL obtained via questionnaires. Methods Training data was collected in two multicenter studies. Master@Heart is a cohort study of endurance trained middle-aged men between 45 and 70 years.[2] Pro@Heart is a prospective study in young endurance athletes between 16 and 23 years.[3] For the purpose of this substudy, 44 athletes (38 ± 17 years) that had recorded all their training sessions using a chest-worn HR monitor for one year were included. Using an in-house developed pipeline in R (R core Team, Vienna, Austria), maximal HR and TL were calculated from training data. The maximal HR measured in the field (Tangent MaxHR) was then compared with the lab-measured maximal HR (Lab MaxHR). Both Lucia training impulse (LuTRIMP) and Edwards TRIMP (eTRIMP) were compared as measures of TL based on HR-based intensity zones. LuTRIMP, based on individually based exercise intensity zones, and eTRIMP based on the Tangent MaxHR. Participants filled in a standard exercise questionnaire to determine the amount of training sessions and training hours per week. Results Lab MaxHR was lower than Tangent MaxHR (183 ± 12.2bpm vs. 188.3 ± 13.0bpm; Figure 1 A), but correlated strongly (r=0.81, P<0.01; Figure 1 B) with low mean bias (-5.3bpm, LOA±15.4; Figure 1 C). LuTRIMP and eTRIMP correlated very strong (r=0.93, P<0.01). Pro@Heart athletes performed a higher TL (1730 ± 644 vs. 1190 ± 314 AU/week, P<0.01; Figure 2 A), duration (12.2 ± 4.6 hours per week vs. 7.9 ± 2.4, P<0.01; Figure 2 B) and trained more frequent (5.9 ± 1.7 vs. 4.0 ± 1.0 sessions per week, P<0.01; Figure 2 C) than Master@Heart athletes. Neither the reported training sessions nor the training hours in the questionnaires correlated with the measured training sessions (r=0.20) and training hours respectively (r=0.12). Conclusion Our tool allows to semi-automatically quantify different TL parameters in endurance athletes from a wide age range. The use of questionnaires to determine TL is inaccurate. TL calculated using eTRIMP is comparable to TL calculated using LuTRIMP. Since eTRIMP does not require an exercise test, it is more easily implementable in large studies.

PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

Computational framework for complex flow and transport in heterogeneous porous media

We present a flexible scalable open-source computational framework, named SECUReFoam, based on the finite-volume library OpenFOAM®, for flow and transport problems in highly heterogeneous geological media and other porous materials. The framework combines geostatistical pre- and post-processing tools with specialised partial differential equations solvers. Random fields, for permeability and other physical properties, are generated by means of continuous or thresholded Gaussian random fields with various covariance/variogram functions. The generation process is based on an explicit spectral Fourier decomposition of the field which, although more computationally intensive than Fast Fourier Transform methods, allows a more flexible choice of statistical parameters and can be used for general geometries and grids. Flow and transport equations are solved for single-phase and variable density problems, with and without the Boussinesq approximation, and for a wide range of density, viscosity, and dispersion models, including dual-continuum (dual permeability or dual porosity) formulations. The mathematical models are here presented in details and the numerical strategies to deal with heterogeneities, equation coupling, and boundary conditions are discussed and benchmarked for the heterogeneous Henry and Horton–Rogers–Lapwood problems, and other test cases. We show that our framework is capable of dealing with large permeability variances, viscous instabilities, and large-scale three-dimensional transport problems.

ChemicalDrift 1.0: an open-source Lagrangian chemical-fate and transport model for organic aquatic pollutants

Abstract. A new model for transport and fate of chemicals in the aquatic environment is presented. The tool, named ChemicalDrift, is integrated into the open-source Lagrangian framework OpenDrift and is hereby presented for organic compounds. The supported chemical processes include the degradation, the volatilization, and the partitioning between the different phases that a target chemical can be associated with in the aquatic environment, e.g. dissolved, bound to suspended particles, or deposited to the seabed sediments. The dependencies of the chemical processes on changes in temperature, salinity, and particle concentration are formulated and implemented. The chemical-fate modelling is combined with wide support for hydrodynamics by the integration within the Lagrangian framework which provides e.g. advection by ocean currents, diffusion, wind-induced turbulent mixing, and Stokes drift generated by waves. A flexible interface compatible with a wide range of available metocean data is made accessible by the integration, making the tool easily adaptable to different spatio-temporal scales and fit for modelling of complex coastal regions. Further inherent capabilities of the Lagrangian approach include the seamless tracking and separation of multiple sources, e.g. pollutants emitted from ships or from rivers or water treatment plants. Specific interfaces to a dataset produced by a model of emissions from shipping and to an unstructured-grid oceanographic model of the Adriatic Sea are provided. The model includes a database of chemical parameters for a set of poly-aromatic hydrocarbons and a database of emission factors for different chemicals found in discharged waters from sulfur emission abatement systems in marine vessels. A post-processing tool for generating mean concentrations of a target chemical, over customizable spatio-temporal grids, is provided. Model development and simulation results demonstrating the functionalities of the model are presented, while tuning of parameters, validation, and reporting of numerical results are planned as future activities. The ChemicalDrift model flexibility, functionalities, and potential are demonstrated through a selection of examples, introducing the model as a freely available and open-source tool for chemical fate and transport that can be applied to assess the risks of contamination by organic pollutants in the aquatic environment.

MOTION CAPTURE ANALYSIS OF EX VIVO KNEE KINEMATIC SIMULATOR: A COMPREHENSIVE EXPERIMENTAL PROTOCOL

Preface: Understanding knee kinematics is a fundamental prerequisite to address restoration in pathological joints; these performances represent the goal to achieve after the treatment. Experimental activities addressing knee kinematics often involve Motion Capture systems to acquire information, both for in vivo and ex vivo studies. This technique allows a complete analysis of the joint kinematics and is able to provide useful results for the comparison mentioned above. Objectives: The definition of a reproducible and straightforward protocol represents a beneficial factor to improve both reliability and the time required: in this study a new protocol for kinematics experimental activities on ex vivo knee specimens was developed, validated and tested. Methods: Synthetic bones were chosen for the analysis and different Total Knee Arthroplasties models were selected (a Cruciate Retaining, a Posterior Stabilized, a Constrained Condylar Knee and a Rotating Hinge). A dedicated frame was used to support and secure the knee specimens and pair the extensor mechanism to a motor. The Motion Capture System was assembled and paired with dedicated marker-sets to be fixed to the specimens. A post-processing tool was developed to analyze the outputs and was validated with a goniometer. A series of force-driven tasks were defined, implemented and run in the motor system for all the different prosthesis configurations and kinematics output were analyzed, comparing the outputs with the expected results. Results: The validation of the system returned satisfying results in terms of correspondence between the angles imposed and the ones measured, with an average error below [Formula: see text] and a standard deviation below [Formula: see text] for each kind of rotation. The results from the different testing were coherent with the type of specimens and prostheses tested. Conclusions: This study proved that the protocol and testing set-up developed for knee kinematics in vitro analysis are able to provide reliable and coherent data, as proven by the post-processing validation and by the testing campaign on synthetic specimens.

A parameterized iterative synchrosqueezing transform for the analysis of noise contaminated non-stationary signals

Synchrosqueezing transform (SST) is a powerful post-processing tool in the analysis of non-stationary signals, though it has limited capacity in the handling of strong time-varying signals or signals with low signal-to-noise ratio (SNR). In this study, a parameterized iterative SST is proposed for an accurate machine fault diagnosis. Firstly, a parameterized chirplet transform is used to capture the fast-varying instantaneous frequencies (IF) of a strong time varying signal. Secondly, the TF coefficients are reassigned to the IF trajectory by an iterative SST technique. Finally, the blurry transition between two neighboring TF points is eliminated by the construction of a local maxima operator. The effectiveness of the current technique is examined using two simulated non-stationary signals and two sets of machine defect data acquired under varying speed condition. The results show that the current technique can produce a highly energy concentrated time frequency result for an accurate mechanical fault diagnosis under either strong speed variation or/and noise contamination conditions. The effectiveness of the technique is also validated by comparing the result with those obtained via other TFA techniques. The result showed that the current technique can render the most accurate result within the TFA techniques used in this study.

Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research

Abstract Purpose: De-identification of cancer imaging data is vitally important for data sharing and the advancement of research, however it is a time consuming and complex process that limits access to new cancer data sets such as those shared through NCI's Imaging Data Commons (IDC), built on the Google Cloud Platform (GCP). Our research demonstrates how this process can be automated using GCP-native services. Methods: We configured the Medical Image De-Identification (MIDI) pipeline to automate de-identification of cancer imaging data. De-identification is performed using an alpha release of GCP’s Healthcare API which was configured to scrub all Protected Health Information (PHI) from both Digital Imaging and Communications in Medicine (DICOM) headers and burnt-in text in pixel data. A dataset containing 216 patients and 23,921 images was prepared to test the de-identification algorithm by placing synthetic PHI in both DICOM headers and pixel data. The synthetic data matched real data seen during curation at The Cancer Imaging Archive (TCIA) and included data difficult for an algorithm to detect. Accuracy of the MIDI pipeline was measured against TCIA’s standard tools and procedures for de-identification. Measures included correct detection of all PHI data and correct action taken (e.g., remove, encrypt, or otherwise obscure). Throughput was also measured. Results: Throughput was measured at 22.0 images per second over 10 runs. The MIDI pipeline’s accuracy for DICOM headers was 98.7%, accurately detecting dates, addresses, phone numbers, unique identifiers, names, and other common PHI. The most common PHI failed to remove were special cases that included uncommon names or names with symbols, dates in string data types that were mistaken for other IDs, patient IDs, and abbreviated institution names. Private Creator data elements were consistently failed to be retained. These errors were due to options not currently available, and algorithms not trained on specific PHI, such as abbreviated institution names. UIDs were correctly replaced. PHI burnt-in the pixel data was successfully detected and removed, with one false positive. Conclusion: We demonstrate the current capability and performance of automated cancer image de-identification. Our results show that while full automation is within grasp, a semi-automated pipeline is now feasible. A human expert in the loop can be used for final verification. This will lead to a much-needed acceleration of image de-identification, to handle the rapidly growing volume of data and provide rapid timely access in support of cancer research. Future work will focus on including pre- and post-processing tools to aid the human expert in the loop, such as identifying and flagging questionable images for manual review. These tools will also be used to catch the errors mentioned in results. Citation Format: Benjamin P. Kopchick, Laura K. Opsahl-Ong, Qinyan Pan, Michael W. Rutherford, Ulrike Wagner, Bhavani S. Singh, Scott Gustafson, Fred W. Prior, David A. Clunie, Juergen A. Klenk, Keyvan Farahani. Accelerating de-identification of images with cloud services to support data sharing in cancer research. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6579.

Refining the time–frequency characteristic of non-stationary signal for improving time–frequency representation under variable speeds

Time–frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time–frequency domain for accurate detection. In this article, an adaptive weighted smooth model is presented as a post-processing tool to refine the time–frequency ridge which is based on the coarse estimated time–frequency ridge using newly emerging time–frequency methods. Firstly, the coarse ridge is estimated by using multi-synchrosqueezing transform for vibration signal under variable speed conditions. Secondly, an adaptive weighted method is applied to enhance the large time–frequency energy value location of the estimated ridge. Then, the reasonable smooth regularization parameter associated with the vibration signal is constructed. Thirdly, the majorization–minimization method is developed for solving the adaptive weighted smooth model. Finally, the refined time–frequency characteristic is obtained by utilizing the stop criterion of the optimization model. Simulation and experimental signals are given to validate the performance of the proposed method by average absolute errors. Compared with other methods, the proposed method has the highest performance in refinement accuracy.

Sizing of particles and droplets using 3D-PTV, an OpenPTV post-processing tool

A methodology to obtain particle sizes from images was created, this tool can be used as a post-processing tool to the open source software OpenPTV. The proposed methodology uses the same images and information obtained during the 3D calibration of OpenPTV to estimate the real sizes of the observed particles. The objective of this paper is to introduce the followed methodology to create a sizing tool and validate the technique with different solid particles and water droplets. The method requires an extra calibration with a flat target containing circles of different sizes, but if the calibration is successful, the sizes of particles can be estimated reliably with a maximum relative error of 10%, as long as the particle diameter correspond to at least three pixels width in the obtained images.

Assessment of normal pulmonary development using functional magnetic resonance imaging techniques

The mainstay of assessment of the fetal lungs in clinical practice is via evaluation of pulmonary size, primarily using 2D ultrasound and more recently with anatomical magnetic resonance imaging.The emergence of advanced magnetic resonance techniques such as T2* relaxometry in combination with the latest motion correction post-processing tools now facilitates assessment of the metabolic activity or perfusion of fetal pulmonary tissue in vivo. This study aimed to characterize normal pulmonary development using T2* relaxometry, accounting for fetal motion across gestation. Datasets from women with uncomplicated pregnancies that delivered at term, were analyzed. All subjects had undergone T2-weighted imaging and T2* relaxometry on a Phillips 3T magnetic resonance imaging system antenatally. T2* relaxometry of the fetal thorax was performed using a gradient echo single-shot echo planar imaging sequence. Following correction for fetal motion using slice-to-volume reconstruction, T2* maps were generated using in-house pipelines. Lungs were manually segmented and mean T2* values calculated for the right and left lungs individually, and for both lungs combined.Lung volumes were generated from the segmented images, and the right and left lungs, as well as both lungs combined were assessed. Eighty-seven datasets were suitable for analysis. The mean gestation at scan was 29.9±4.3 weeks (range: 20.6-38.3) and mean gestation at delivery was 40±1.2 weeks (range: 37.1-42.4). Mean T2* values of the lungs increased over gestation for right and left lungs individually and for both lungs assessed together (P=.003; P=.04; P=.003, respectively). Right, left, and total lung volumes were also strongly correlated with increasing gestational age (P<.001 in all cases). This large study assessed developing lungs using T2* imaging across a wide gestational age range. Mean T2* values increased with gestational age, which may reflect increasing perfusion and metabolic requirements and alterations in tissue composition as gestation advances. In the future, evaluation of findings in fetuses with conditions known to be associated with pulmonary morbidity may lead to enhanced prognostication antenatally, consequently improving counseling and perinatal care planning.

Application of 3D-scanning for structural and geometric assessment of aerospace structures

The geometric assessment of physical demonstrators are an integral part of several research projects conducted at the Chair of Aircraft Design at the Technical University of Munich. The projects range from several research UAVs, a sailplane morphing wing to propellers. There are different project objectives like the assessment of manufacturing deviations, design and function validation as well as reverse engineering of aerodynamic surfaces for model adaptation and simulation in the projects. Nevertheless, mutual approaches and solutions have been identified. Therefore joint development efforts are undertaken using 3D-scanning technology for data collection and evaluation. This technology captures the surface of a given object typically as a point cloud with comparably high accuracy. Since a manual evaluation process bears disadvantages in terms of reproducibility, custom post-processing software tools are developed. Global geometry data, like wing platform data, as well as airfoils can be extracted from a surface point cloud to analyze UAV wings or propellers. Airfoils can be derotated, normed and smoothed for aerodynamic analysis with low-fidelity aerodynamic tools, such as XFLR5 or XFOIL. For the analysis of morphing airfoil structures, the scanned geometry is aligned with the desired design airfoil shape so they can be compared. In this paper, analysis methods and several example results are presented.

E-POD investigations of turbulent premixed flame dynamics approaching lean blow-out conditions

Thanks to the continuous computational power increase, the use of high-fidelity computational fluid dynamics (CFD) simulations is nowadays customary, especially in the gas turbines design process. The extraordinary temporal and spatial detail of such analyses generate large datasets, which must be carefully studied to correlate different quantities and gain information to characterize the behavior of combustor designs. Several advanced post-processing tools have been proposed; however, the Extended-POD (E-POD) holds the greatest potential for turbulent combustion applications when the mutual influence of different quantities is the main goal of the investigation. The present work investigates the application of the E-POD to an LES model of a perfectly-premixed, swirl-stabilized, methane-air flame approaching Lean-Blow-Out. Leveraging the validation against the experimental data at two different operating conditions on a laboratory test case, the numerical model has been used to collect several quantities of interest for shedding light on the flow-flame interaction near the blow-out. The post-processing algorithm has been used to highlight the differences between two conditions approaching the extinction at distinct air-flow velocities. It has been found that, when the burner is operated with a higher velocity, the flame is subjected to a cyclic low-frequency breakdown around the internal recirculation zone, leading to an ingestion of cold products from the external parts of the combustor toward the center. Although other local effects acting on the flame brush have been found in both conditions, they are related mainly to higher order coherent structures with a lower energy content. As a result, their impact onto flame stability is found to be of secondary importance since their limited interaction with flame stabilization. The work shows that E-POD represents a powerful tool for investigating the key features of flame dynamics even at near-blow-out conditions, constituting a valid algorithm for interpreting the results of CFD analyses on gas turbines combustors.

MDSuite: comprehensive post-processing tool for particle simulations

Particle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal/macroscopic particle methods, and extension to experimental data.

Dycdtools: an R Package for Assisting Calibration and Visualising Outputs of an Aquatic Ecosystem Model

The high complexity of aquatic ecosystem models (AEMs) necessitates a large number of parameters that need calibration, and visualisation of their multifaceted and multi-layered simulation results is necessary for effective communication. Here we present an R package "dycdtools" that contains calibration and post-processing tools for a widely applied aquatic ecosystem model (DYRESM-CAEDYM). The calibration assistant function within the package automatically tests a large number of combinations of parameter values and returns corresponding values for goodness-of-fit, allowing users to narrow parameter ranges or optimise parameter values. The post-processing functions enable users to visualise modelling outputs in four ways: as contours, profiles, time series, and scatterplots. The "dycdtools" package is the first open-source calibration and post-processing tool for DYRESM-CAEDYM, and can also be adjusted for other AEMs with a similar structure. This package is useful to reduce the calibration burden for users and to effectively communicate model results with a broader community.