Elementary Model Research Articles

Parametric dependence and elementary modelling for compressor disc cavity heat transfer

A wall-modelled large-eddy simulation (WMLES) method previously validated against laboratory experiments is applied to a real engine disc cavity geometry with surface and air temperatures representative of maximum power conditions. An appropriate set of independent nondimensional parameters describing the problem is defined and the sensitivities to these parameters are investigated by independently varying each parameter in the WMLES. Effects of a change in disc temperature distribution and a reduction in radius of the downstream disc are also investigated. The flow and heat transfer mechanisms predicted are very similar to those found for the research rig configuration although rotating cavity flow structures with two or more lobes are found for the engine geometry rather than the single-lobed structures shown in previous work. The simulations are compared with an elementary model giving further insight into scaling of the flow and heat transfer with operating conditions. The model assumes a well-mixed core flow in the cavity with constant rothalpy, relates shroud heat transfer to that of natural convection under gravity for high Rayleigh number, and relates disc heat transfer to conduction across unsteady Ekman layers. An energy balance for the cavity is used to obtain an effective mass flow exchange rate between the axial throughflow and the disc cavity to the disc and shroud heat transfer and core temperature. The model is considered to give a useful basis for engineering calculations involving correlation and extrapolation of WMLES and experimental results.

Δ-Learning applied to coarse-grained homogeneous liquids.

Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery applications that density functional theory plays for electronic structure. This gap might be filled by machine learning (ML)-based CGMD potentials that simplify model development, but these methods are still in their early stages and have yet to demonstrate a significant advantage over existing physics-based CGMD methods. Here, we explore the potential of Δ-learning models to leverage the advantages of these two approaches. This is implemented by using ML-based potentials to learn the difference between the target CGMD variable and the predictions of physics-based potentials. The Δ-models are benchmarked against the baseline models in reproducing on-target and off-target atomistic properties as a function of CG resolution, mapping operator, and system topology. The Δ-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models manage to minimize training errors while still producing qualitatively incorrect dynamics, which is corrected by the Δ-models. Given their negligible added cost, Δ-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the Δ-learning models nor the ML-only models significantly outperform the elementary pairwise models in reproducing atomistic properties. This fundamental failure is attributed to the relatively large irreducible force errors associated with coarse-graining that produces little benefit from using more complex potentials.

Elementary models of three-dimensional topological insulators with chiral symmetry

Elementary models of three-dimensional topological insulators with chiral symmetry

Effect of the central gravitational field on self-accelerating outward propagating flames subjected to the Rayleigh-Taylor instability: Transition to detonation

Within the Boussinesq approximation an elementary model for the deflagration-to-detonation transition triggered by self-acceleration of an expanding flame in a central gravitational field is formulated and explored numerically. The self-acceleration is sustained by the intrinsic Rayleigh-Taylor instability until the Deshaies-Joulin deflagrability threshold is reached, followed by an abrupt transition to detonation. Emergence of the threshold is caused by positive feedback between the accelerating flame and the flame-driven pressure shock that results in the thermal runaway when the flame speed reaches a critical level. The model offers a simple mechanism that may be responsible for the transition to detonation in thermonuclear supernovae. The present study is an extension of the preceding models dealing with constant gravitational fields.

Feynman checkers: Number-theoretic properties

We study Feynman checkers, an elementary model of electron motion introduced by Feynman. In this model, a checker moves on a checkerboard, and we count the turns. Feynman checkers are also known as a one-dimensional quantum walk. We prove some new number-theoretic results in this model, for example, sign alternation of the real and imaginary parts of the electron wave function in a specific area. All our results can be stated in terms of Young diagrams, namely, we compare the number of Young diagrams with an odd and an even number of steps.

Demersal fish biomass declines with temperature across productive shelf seas

AbstractAimTheory predicts fish community biomass to decline with increasing temperature due to higher metabolic losses resulting in less efficient energy transfer in warm‐water food webs. However, whether these metabolic predictions explain observed macroecological patterns in fish community biomass is virtually unknown. Here, we test these predictions by examining the variation in demersal fish biomass across productive shelf regions.LocationTwenty one continental shelf regions in the North Atlantic and Northeast Pacific.Time Period1980–2015.Major Taxa StudiedMarine teleost fish and elasmobranchs.MethodsWe compiled high‐resolution bottom trawl survey data of fish biomass containing 166,000 unique tows and corrected biomass for differences in sampling area and trawl gear catchability. We examined whether relationships between net primary production and demersal fish community biomass are mediated by temperature, food‐web structure and the level of fishing exploitation, as well as the choice of spatial scale of the analysis. Subsequently, we examined if temperature explains regional changes in fish biomass over time under recent warming.ResultsWe find that biomass per km2 varies 40‐fold across regions and is highest in cold waters and areas with low fishing exploitation. We find no evidence that temperature change has impacted biomass within marine regions over the time period considered. The biomass variation is best explained by an elementary trophodynamic model that accounts for temperature‐dependent trophic efficiency.Main ConclusionsOur study supports the hypothesis that temperature is a main driver of large‐scale cross‐regional variation in fish community biomass. The cross‐regional pattern suggests that long‐term impacts of warming will be negative on biomass. These results provide an empirical basis for predicting future changes in fish community biomass and its associated services for human wellbeing that is food provisioning, under global climate change.

On the modal characteristics and stability of translating cable: Application to an overall line of cable transport systems

Moving cables are used for many applications in engineering structures such as cable-supported transport systems that involves mobile cables suspended between supports for moving attached vehicles. Advantages of such systems in terms of cost, footprint and energy efficiency make its urban integration as one of the solutions to meet the need for new mobilities. Because of the inherent cable’s flexibility and the modification of dynamic properties during the cable movement, such systems are sensitive to the effects of perturbations and self-sustaining oscillations. The methods commonly used to describe cable dynamics are restricted to small static sag case with a predominant vertical dynamics. To accurately analyse a multi-segment mobile cable, this paper proposes an hybrid analytical–numerical strategy to deal with a general mechanical elementary model of a cable in unsteady translation formulated in curvilinear abscissa derived from the formalism of continuum mechanics. An assembly procedure permits to deal with a more complex system considering a cable loop in contact with intermediary supports and punctual loads according to different boundary conditions, considering the coupling between the vertical dynamics activated at the local scale of a span and the longitudinal dynamics activated at the scale of the whole cable loop. The translation speed and acceleration gyroscopic effects on the system’s complex modes are studied and design rules are provided to guide an optimal conception avoiding strong oscillations of the system.

Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic kidney disease

Clinicians need improved prediction models to estimate time to kidney replacement therapy (KRT) for children with chronic kidney disease (CKD). Here, we aimed to develop and validate a prediction tool based on common clinical variables for time to KRT in children using statistical learning methods and design a corresponding online calculator for clinical use. Among 890 children with CKD in the Chronic Kidney Disease in Children (CKiD) study, 172 variables related to sociodemographics, kidney/cardiovascular health, and therapy use, including longitudinal changes over one year were evaluated as candidate predictors in a random survival forest for time to KRT. An elementary model was specified with diagnosis, estimated glomerular filtration rate and proteinuria as predictors and then random survival forest identified nine additional candidate predictors for further evaluation. Best subset selection using these nine additional candidate predictors yielded an enriched model additionally based on blood pressure, change in estimated glomerular filtration rate over one year, anemia, albumin, chloride and bicarbonate. Four additional partially enriched models were constructed for clinical situations with incomplete data. Models performed well in cross-validation, and the elementary model was then externally validated using data from a European pediatric CKD cohort. A corresponding user-friendly online tool was developed for clinicians. Thus, our clinical prediction tool for time to KRT in children was developed in a large, representative pediatric CKD cohort with an exhaustive evaluation of potential predictors and supervised statistical learning methods. While our models performed well internally and externally, further external validation of enriched models is needed.

Sifting composite from elementary models at FCCee and CePC

New Physics models with either an elementary or composite origin are often associated with a similar imprint in a direct search at colliders, case in point being the production of a light pseudoscalar in association with a monochromatic photon from the decay of a Z boson at future e+e-e+e− colliders. We exploit the correlation between the discovery of a signal in the Z decays and electroweak precision measurements as a tool to distinguish a composite model from an elementary scalar one. Our results offer an appealing and rich physics case for future colliders and demonstrate how a lepton collider at the Z mass can be a discovery machine for new physics in the Higgs sector.

“Making teaching cheap”: secondary employment and feminisation in elementary schools in the Uppsala region in central Sweden, 1861–1910

ABSTRACT The introduction of elementary schools in Sweden (and elsewhere) in the mid-nineteenth century required the quick hiring of many new teachers, which in turn posed challenges about their financing. This paper analyses school inspectors’ reports and teachers’ journals to illuminate local strategies to provide affordable teaching in the Uppsala region in central Sweden from 1861 to 1910. The feminisation of the teacher’s role and the secondary occupations undertaken by (usually) male teachers during this time have not previously been studied together in this context. The main aim of studying these areas together was to contribute to social and economic research in the history of education. The methods were historical and statistical analyses of source materials, with a particular focus on local and regional school developments. The results show that hiring more women to teach in the lower school forms and providing or encouraging side occupations for male teachers were common strategies to make teaching affordable in Sweden during the period studied. In conclusion, it would have been very difficult to implement the elementary school model in Sweden without these strategies. The study confirms the notion of the local character of school funding and provides new empirical evidence of the limitations of state intervention during this period.

Generalizing the Wells-Riley Infection Probability: A Superstatistical Scheme for Indoor Infection Risk Estimation.

Recent evidence supports that air is the main transmission pathway of the recently identified SARS-CoV-2 coronavirus that causes COVID-19 disease. Estimating the infection risk associated with an indoor space remains an open problem due to insufficient data concerning COVID-19 outbreaks, as well as, methodological challenges arising from cases where environmental (i.e., out-of-host) and immunological (i.e., within-host) heterogeneities cannot be neglected. This work addresses these issues by introducing a generalization of the elementary Wells-Riley infection probability model. To this end, we adopted a superstatistical approach where the exposure rate parameter is gamma-distributed across subvolumes of the indoor space. This enabled us to construct a susceptible (S)-exposed (E)-infected (I) dynamics model where the Tsallis entropic index q quantifies the degree of departure from a well-mixed (i.e., homogeneous) indoor-air-environment state. A cumulative-dose mechanism is employed to describe infection activation in relation to a host's immunological profile. We corroborate that the six-foot rule cannot guarantee the biosafety of susceptible occupants, even for exposure times as short as 15 min. Overall, our work seeks to provide a minimal (in terms of the size of the parameter space) framework for more realistic indoor SEI dynamics explorations while highlighting their Tsallisian entropic origin and the crucial yet elusive role that the innate immune system can play in shaping them. This may be useful for scientists and decision makers interested in probing different indoor biosafety protocols more thoroughly and comprehensively, thus motivating the use of nonadditive entropies in the emerging field of indoor space epidemiology.

NH3-Fed Patterned Electrode Solid Oxide Fuel Cell: Experimental Performance Characterization and Elementary Reaction Modeling

Ammonia-fueled solid oxide fuel cells (SOFCs) have attracted the focus of researchers due to no carbon emissions in utilization. Understanding the reaction mechanism is vital for the design and optimization of NH3-fed SOFCs. However, the catalytic decomposition reactions involved in porous electrodes led to difficulty in distinguishing the anode reaction mechanism. In the present study, we utilized a patterned anode to obtain a designed triple phase boundary (TPB) and avoid the effects of porous electrodes. In the performance test, we found that the exchange current density of the NH3-fed SOFC was approximately 5% of that of the H2-fed SOFC under a similar working position at 700℃. The difference may mainly result from the limit of catalytic decomposition of NH3 caused by the narrow reactive area in the patterned electrode. A one-dimensional elementary reaction model was further developed to validate this hypothesis and to provide additional evidence for the reaction mechanisms of the NH3-fed SOFC.

Kinetic modeling of dynamically operated heterogeneously catalyzed reactions: Microkinetic model reduction and semi-mechanistic approach on the example of the CO[formula omitted] methanation

As the production of green hydrogen from fluctuating resources and the use of Power-to-X technologies become more and more important in the energy sector and the chemical process industries, the interest in dynamically operated reactors is increasing. In order to apply model-based optimization methods for process intensification the dynamic reactor behavior needs to be predicted sufficiently accurate by the underlying model equations, in which the reaction kinetics play a key role. Microkinetic elementary step models are able to describe the dynamic behavior on the catalyst surface. However, these models are laborious to derive, parameterize and require comparably high computational effort to solve. For these reasons, simpler semi-mechanistic kinetic models based on a rate determining step at steady state conditions are much more frequently applied in numerical studies. However, the underlying assumptions of these models are questionable for dynamic operation since sorption processes and possible changes in the reaction mechanisms are not considered. We present a semi-mechanistic kinetic model approach based on so called rate-affecting steps and the dynamic change in surface coverages, which is able to describe the dynamic behavior of the catalyst surface. The applicability of our approach is demonstrated for model reduction of an elementary step mechanism developed for the COx methanation and for kinetic model development with synthetic experimental data. Both methods result in a kinetic expression that can accurately reproduce the dynamic kinetic behavior predicted by the elementary step model, with a significant reduction in computational effort for our new semi-mechanistic model, making this new class of model approach attractive for model-based optimization applications.

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

내용언어통합학습 기반 초등영어수업 모형 개발 및 적용

The purpose of this study was to develop and apply an elementary English instructional model based on CLIL(Content and Language Integrated Learning). It attempted to analyze students’ perceptions of CLIL-based elementary English classes. The research was carried out with two classes of fifth graders at an elementary school in Seoul.
 The speech between the teacher and students was collected and transcribed. Also, a semi-structured student interview was conducted to probe the learners’ perceptions.
 The result showed that the students had a high degree of satisfaction with the classes based on CLIL. According to the student responses, CLIL motivated them and increased their chances of using English. However, there were also drawbacks for participating in the CLIL classes. For example, some students needed more time or materials to comprehend the lessons. In addition, some pedagogical implications and suggestions for developing CLIL-based elementary English instructional model are included.

Методы расчета и оценки термоэмиссионного плазменно-капельного генератора с магнитной сепарацией заряда

The development of design solutions and preliminary calculation substantiation of the characteristics of a high-voltage plasma-drop generator with magnetic charge separation have been completed. The aim of the study was to confirm the possibility of creating a current source with an operating voltage of about 107 V. The proposed energy conversion method is based on the separation of charged drops from dusty plasma in a gradient magnetic field. Droplets are charged due to thermal emission from their surface. A schematic description of the generator is presented and an elementary calculation model is proposed. This model is divided into 4 blocks that describe the various processes taking place in the device. The assumptions and simplifications necessary for the evaluation are discussed. Preliminary calculation data on the efficiency of a plasma-drop generator with magnetic charge separation have been obtained under conditions close to those actually achievable by manufacturing technologies. The main parameters for evaluation were power, efficiency, voltage and magnetic field induction. As a result of the calculations performed, the fundamental possibility of creating a workable plasma-drop generator based on the proposed solutions is shown. The results obtained are not accurate and require further improvement of the model and study of the processes taking place in the generator.

초등 사회과교육에서의 K-평균 군집화 알고리즘 기반 수업의 효과성

Objectives The purpose of this study is to check whether elementary social studies class is effective in cultivating learners' accountability and information utilization skill using the K-means clustering algorithm and to propose a new elementary social studies class model suitable for the era of the 4th Industrial Revolution. Methods Using various tools, the procedure of the K-means clustering algorithm was reconstructed into an unplugged class model, and the class was constructed by fusing it with the climate environment among the topics of elementary social studies education. In addition, the effectiveness was analyzed qualitatively and quantitatively by applying it to actual elementary school classroom. Results After executing the class applying the K-means clustering algorithm, it was found that accountability was cultivated by qualitative analysis of the learning material written by students. In addition, when quantitatively analyzing and comparing students' information utilization skill before and after class, it was found that the information utilization skill after class was further improved. Conclusions Through this study, the possibility of convergence of elementary social studies education and K-means clustering algorithm was confirmed, and the effectiveness of algorithm-based class in elementary schools was verified. In addition, a new elementary social studies class that can be used in the era of the 4th Industrial Revolution was proposed.

Predicting DNA kinetics with a truncated continuous-time Markov chain method

Predicting the kinetics of reactions involving nucleic acid strands is a fundamental task in biology and biotechnology. Reaction kinetics can be modeled as an elementary step continuous-time Markov chain, where states correspond to secondary structures and transitions correspond to base pair formation and breakage. Since the number of states in the Markov chain could be large, rates are determined by estimating the mean first passage time from sampled trajectories. As a result, the cost of kinetic predictions becomes prohibitively expensive for rare events with extremely long trajectories. Also problematic are scenarios where multiple predictions are needed for the same reaction, e.g., under different environmental conditions, or when calibrating model parameters, because a new set of trajectories is needed multiple times. We propose a new method, called pathway elaboration, to handle these scenarios. Pathway elaboration builds a truncated continuous-time Markov chain through both biased and unbiased sampling. The resulting Markov chain has moderate state space size, so matrix methods can efficiently compute reaction rates, even for rare events. Also the transition rates of the truncated Markov chain can easily be adapted when model or environmental parameters are perturbed, making model calibration feasible. We illustrate the utility of pathway elaboration on toehold-mediated strand displacement reactions, show that it well-approximates trajectory-based predictions of unbiased elementary step models on a wide range of reaction types for which such predictions are feasible, and demonstrate that it performs better than alternative truncation-based approaches that are applicable for mean first passage time estimation. Finally, in a small study, we use pathway elaboration to optimize the Metropolis kinetic model of Multistrand, an elementary step simulator, showing that the optimized parameters greatly improve reaction rate predictions. Our framework and dataset are available at https://github.com/DNA-and-Natural-Algorithms-Group/PathwayElaboration.

An EIM-based compression-extrapolation tool for efficient treatment of homogenized cross-section data

Nuclear reactor simulators implementing the widespread two-steps deterministic calculation scheme tend to produce a large volume of intermediate data at the interface of their two subcodes – up to dozens or even hundred of gigabytes – which can be so cumbersome that it hinders the global performance of the code. The vast majority of this data consists of “few-groups homogenized cross-sections”, nuclear quantities stored in the form of tabulated multivariate functions which can be precomputed to a large extent.It has been noticed in Tomatis (2021) that few-groups homogenized cross-sections are highly redundant — that is, they exhibit strong correlations, which paves the way for the use of compression techniques. We here pursue this line of work by introducing a new coupled compression/surrogate modeling tool based on the Empirical Interpolation Method, an algorithm originally developed in the framework of partial differential equations (Barrault et al., 2004). This EIM-compression method is based on the infinite norm ∥⋅∥∞, and proceeds in a greedy manner by iteratively trying to approximate the data and incorporating the chunks of information which cause the largest error. In the process, it generates a vector basis and a set of interpolation points, which provide an elementary surrogate model that can be used to approximate future data from little information. The algorithm is also very suitable for parallelization and out-of-core computation (processing of data too large for the computer RAM) and very easy to apprehend and implement.This method enables us to both efficiently compress cross-sections and spare a large fraction of the required lattice calculations. We investigate its performance on large realistic nuclear data replicating the notorious VERA benchmark (Godfrey, 2014) (20 energy groups, pin-by-pin homogenization, 10 particularized isotopes). Compression loss, memory savings and speed are analyzed both from a data-centric point of view in the perspective of applications in neutronics, and by comparison with an existing and widely-used method – stochastic truncated SVD – to assess mathematical efficiency. We discuss the usage of our surrogate model and its sensitivity to the choice of the training set. The method is shown to be competitive in terms of accuracy and speed, provide important memory savings and spare a large amount of physics code computation; all this could facilitate the adoption of fine-grain modelization schemes (pin-by-pin and many-groups homogenization, particularization of many isotopes) in industrial setups. A Github repository is available,11Link: https://github.com/Otruffinet/EIM_surrogates.git. which contains all the methods used for the article.

Acceleration of Nuclear Reactor Simulation and Uncertainty Quantification Using Low-Precision Arithmetic

In recent years, interest in approximate computing has been increasing significantly in many disciplines in the context of saving energy and computation cost by trading off on the quality of numerical simulation. The hardware acceleration based on the low-precision floating-point arithmetic is anticipated by the upcoming generation of microprocessors and code compilers and has already proven to be beneficial for weather and climate modelling and neural network training. The present work illustrates the application of low-precision arithmetic for the nuclear reactor core uncertainty analysis. We studied the performance of an elementary transient reactor core model for the arbitrary precision of the floating-point multiplication in a direct linear system solver. Using this model, we calculated the reactor core transients initiated by the control rod ejection taking into account the uncertainty of the model input parameters. Then, we evaluated the round-off errors of the model outputs for different precision levels. The comparison of the round-off errors and the model uncertainty showed the model could be run using a 15-bit floating-point number precision with an acceptable degradation of the result’s accuracy. This precision corresponds to a gain of about 6× in the bit complexity of the linear system solution algorithm, which can be actualized in terms of reduced energy costs on low-precision hardware.