Initial Conditions Research Articles

On a biharmonic coupled system with non-standard nonlinearity: Existence, blow up and numerics

In this paper, we consider a coupled system of two biharmonic equations with damping and source terms of variable-exponent nonlinearities, supplemented with initial and mixed boundary conditions. We establish an existence and uniqueness result of a weak solution, under suitable assumptions on the variable exponents. Then, we show that solutions with negative-initial energy blow up in finite time. To illustrate our theoritical findings, we present two numerical examples.

Characterizing structure formation through instance segmentation

Dark matter haloes form from small perturbations to the almost homogeneous density field of the early universe. Although it is known how large these initial perturbations must be to form haloes, it is rather poorly understood how to predict which particles will end up belonging to which halo. However, it is this process that determines the Lagrangian shape of proto-haloes and it is therefore essential to understand their mass, spin, and formation history. We present a machine learning framework to learn how the proto-halo regions of different haloes emerge from the initial density field. We developed one neural network to distinguish semantically which particles become part of any halo and a second neural network that groups these particles by halo membership into different instances. This instance segmentation is done through the Weinberger method, in which the network maps particles into a pseudo-space representation where different instances can easily be distinguished through a simple clustering algorithm. Our model reliably predicts the masses and Lagrangian shapes of haloes object by object, as well as other properties such as the halo-mass function. We find that our model extracts information close to optimally by comparing it to the degree of agreement between two N-body simulations with slight differences in their initial conditions. We publish our model open source and suggest that it can be used to inform analytical methods of structure formation by studying the effect of systematic manipulations of the initial conditions.

Dynamical Modeling and Qualitative Analysis of a Delayed Model for CD8 T Cells in Response to Viral Antigens.

Although the immune effector CD8 T cells play a crucial role in clearance of viruses, the mechanisms underlying the dynamics of how CD8 T cells respond to viral infection remain largely unexplored. Here, we develop a delayed model that incorporates CD8 T cells and infected cells to investigate the functional role of CD8 T cells in persistent virus infection. Bifurcation analysis reveals that the model has four steady states that can finely divide the progressions of viral infection into four states, and endows the model with bistability that has ability to achieve the switch from one state to another. Furthermore, analytical and numerical methods find that the time delay resulting from incubation period of virus can induce a stable low-infection steady state to be oscillatory, coexisting with a stable high-infection steady state in phase space. In particular, a novel mechanism to achieve the switch between two stable steady states, time-delay-based switch, is proposed, where the initial conditions and other parameters of the model remain unchanged. Moreover, our model predicts that, for a certain range of initial antigen load: 1) under a longer incubation period, the lower the initial antigen load, the easier the virus infection will evolve into severe state; while the higher the initial antigen load, the easier it is for the virus infection to be effectively controlled and 2) only when the incubation period is small, the lower the initial antigen load, the easier it is to effectively control the infection progression. Our results are consistent with multiple experimental observations, which may facilitate the understanding of the dynamical and physiological mechanisms of CD8 T cells in response to viral infections.

A Precision Benchmark Suite for Nuclear Reactor Point Kinetics Equations via Converged Accelerated Taylor Series (CATS)

Extreme benchmarks of 10 or more places for the point kinetics equations for time-dependent nuclear reactor power transients are rare. Therefore, to establish an extreme benchmark, we employ a Taylor series (TS) with continuous analytical continuation to solve the ordinary differential equations of point kinetics including feedback. Nonlinear Wynn-epsilon convergence acceleration confirms the highly precise solutions for neutron and precursor densities. Through adaptive partitioning of time intervals, the proposed Converged Accelerated Taylor Series, or CATS algorithm in double precision, automatically performs successive mesh refinement to obtain high-precision initial conditions for each subinterval, with the intent to reduce propagation error. Confirmation of 10 to 12 places comes from comparison to the BEFD (Backward Euler Finite Difference) algorithm in quadruple precision also developed by the author. We report benchmark results for common cases found in the literature including step, ramp, zigzag, and sinusoidal prescribed reactivity insertions and insertions with nonlinear adiabatic Doppler feedback. We also establish a suite of new prescribed reactivity insertions and insertions with feedback, based on reactivities with Taylor series representations as suggested by the CATS algorithm.

Initiation and development of tectonic stylolite – Vein system in micritic limestone (Les Matelles, France)

In this study, we describe the characteristics of tectonic stylolites and related veins affecting a low-porosity micritic limestone (Jurassic carbonates, Les Matelles, South of France) in order to unravel the conditions of initiation and interaction between pressure-solution and fracturing in such rock. Field description, various petrographic and microstructural investigations (cathodoluminescence, SEM imaging, EBSD analysis), and petrophysical/geochemical analyses (Hg porosimetry, XRD, EPMA) are used. We document that pressure-solution initiates at micropores and propagates along calcite grain contacts, connecting surrounding stylolite micro-segments, and progressively concentrates insoluble material such as clays and siliceous particles. The dissolved material is evacuated to the veins where the newly-formed porous space is progressively filled by calcite cement. These deformation processes are strictly restricted to the stylolitic interface and veins, as no modification of porosity or grain deformation is detected in the neighboring host rock. This is due to the low-permeability of the surrounding host rock impeding the evacuation of dissolved material and fluids through interstitial porosity around the pressure-solution zone, leading to overpressure and veins formation. The water release and microporosity caused by diagenesis of the clay fraction (smectite-illite transformation) are discussed as key diagenetic processes instigating the conditions of pressure-solution initiation, then tectonic stylolite formation in low-porosity limestones.

2D unified atmosphere and wind simulations of O-type stars

Context. Massive and luminous O-type star (O star) atmospheres with winds have been studied primarily using one-dimensional (1D), spherically symmetric, and stationary models. However, observations and theory have suggested that O star atmospheres are highly structured, turbulent, and time-dependent. As such, when making comparisons to observations, present-day 1D modeling tools require the introduction of ad hoc quantities such as photospheric macro- and microturbulence, wind clumping, and other relevant properties. Aims. We present a series of multi-dimensional, time-dependent, radiation-hydrodynamical (RHD) simulations for O stars that encapsulate the deeper sub-surface envelope (down to T ~ 450 kK), as well as the supersonic line-driven wind outflow in one unified approach. Our overarching aim is to develop a framework that is free from the ad-hoc prescriptions that plague present-day 1D models. Here, we start with an analysis of a small set of such multi-dimensional simulations and then compare them to atmospheric structures predicted by their 1D counterparts. Methods. We performed time-dependent, two-dimensional (2D) simulations of O star atmospheres with winds using a flux-limiting RHD finite volume modelling technique. Opacities are computed using a hybrid approach combining tabulated Rosseland means with calculations (based on the Sobolev approximation) of the enhanced line opacities expected for supersonic flows. The initial conditions and comparison models were derived using similar procedures as those applied in standard 1D stationary model atmosphere with wind codes. Results. Structure starts appearing in our simulations just below the iron-opacity peak at ~200 kK. Local pockets of gas with radiative accelerations that exceed gravity then shoot up from these deep layers into the upper atmosphere, where they interact with the line-driven wind outflow initiated around or beyond the variable photosphere. This complex interplay creates large turbulent velocities in the photospheric layers of our simulations, on the order of ~30–100km s−1, with higher values for models with higher luminosity-to-mass ratios. This offers a generally good agreement with observations of large photospheric ‘macroturbulence’ in O stars. When compared to 1D models, the average structures in the 2D simulations display less envelope expansion and no sharp density-inversions, along with density and temperature profiles that are significantly less steep around the photosphere, and a strong anti-correlation between velocity and density in the supersonic wind. Although the wind initiation region is complex and highly variable in our simulations, our average mass-loss rates agree well with stationary wind models computed by means of full co-moving frame radiative transfer solutions. Conclusions. The different atmospheric structures found in 2D and 1D simulations are likely to affect the spectroscopic determination of fundamental stellar and wind parameters for O stars as well as the empirical derivation of their chemical abundance patterns. To qualitatively match the different density and temperature profiles seen in our multi-dimensional and 1D models, we need to add a modest amount of convective energy transport in the deep sub-surface layers and a large turbulent pressure around the photosphere to the 1D models.

Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms

Abstract The differential reflectivity (ZDR) column is a notable polarimetric signature related to updrafts in deep moist convection. In this study, pseudo–water vapor (qυ) observations are retrieved from observed ZDR columns under the assumption that humidity is saturated within the convection where ZDR columns are detected, and are then assimilated within the 3DVar framework. The impacts of assimilating pseudo-qυ observations from ZDR columns on short-term severe weather prediction are first evaluated for a squall-line case. Radar data analysis indicates that the ZDR columns are mainly located on the inflow side of the high-reflectivity region. Assimilation of the pseudo-qυ observations leads to an enhancement of qυ within the convection, while concurrently reducing humidity in no-rain areas. Sensitivity experiments indicate that a tuned smaller observation error and a shorter horizontal decorrelation scale are optimal for a better assimilation of pseudo-qυ from ZDR columns, resulting in more stable rain rates during short-term forecasts. Additionally, a 15-min cycling assimilation frequency yields the best performance, providing the most accurate reflectivity forecast in terms of both location and intensity. Analysis of thermodynamic fields reveal that assimilating ZDR columns provides more favorable initial conditions for sustaining convection, including sustainable moisture condition, a strong cold pool, and divergent winds near the surface, consequently enhancing reflectivity and precipitation. With the optimal configuration determined from the sensitivity tests, a quantitative evaluation further demonstrates that assimilating the pseudo-qυ observations from ZDR columns using the 3DVar method can improve the 0–3-h reflectivity and accumulated precipitation predictions of convective storms.

Emulating the interstellar medium chemistry with neural operators

Context. The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities (n), abundances of different species (ni/n) and temperature (T), and plausible evolution scenarios of the ISM under the action of a radiation field (F) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods. We emulated a non-equilibrium chemical network up to H2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 < log(n/cm−3) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log(ni/n) < 0, and adopting an incident radiation field, F, sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each ni for ≃4.34 GPUhrs. Results. Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10−2, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F. DeepONet also performs well for T and ni outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.

The Allen–Cahn model with a time-dependent parameter for motion by mean curvature up to the singularity

In this article, we investigate the temporal evolution of arbitrary, simple, closed two-dimensional (2D) and three-dimensional (3D) interfaces under motion driven by mean curvature up to a singularity. To facilitate this investigation, we propose a novel Allen–Cahn (AC) model with a time-dependent interfacial thickness parameter. The original AC equation was developed to model the phase separation of a binary mixture. It is well known that a level set or interface of the solution of the AC equation obeys the dynamics of motion by curvature as the interfacial thickness parameter approaches zero. Generally, it is difficult to find a closed-form analytic solution of the AC equation with any initial condition. Therefore, we need to estimate the solution of the AC equation through computational approaches such as finite difference method (FDM), finite element method (FEM), Fourier-spectral method (FSM), and finite volume method (FVM). Any simple, closed curves and convex surfaces eventually shrink to a point due to motion by mean curvature. Therefore, it becomes necessary to use adaptive mesh techniques to resolve this small size problem. However, even though we use adaptive mesh techniques, we still confront the relatively thick interfacial transition layer when the curves or interfaces become very small. To avoid this problem, we can start with a very small mesh size for a small value of the interfacial parameter, which results in an extremely high computational cost even when using adaptive mesh techniques. To resolve these issues, we present the AC equation with a time-dependent interfacial parameter and develop an adaptive mesh refinement system. To show the superior performance of the proposed mathematical equation and its computational algorithm, we present various numerical experiments and investigate the motion by mean curvature up to the singularity of curves in 2D space and interfaces in 3D space.

Tacheometric Survey and 3D-Model Building of Fortification Objects in the South of Western Siberia

Purpose. The most objective planigraphy of the earthen fortifications is one of the initial conditions for the reliability of the description of these objects and the subsequent analysis of their defensive capabilities. One of the most common devices for the instrumental surveys in modern archaeological research is a total station. The purpose of the tacheometric survey was to build 3D-models of the earthern fortifications of various types (cape settlements – Chultukov Log-9, ostrogs and redoubts (Umrevinsky, Salt Turn). The work at these objects included two stages – an instrumental survey of the monument and subsequent processing of the survey results with the 3D-models buildings and objects indication.Results. The tacheometric survey was carried out in various landscape zones (the Altai mountains, the northern forest-steppe of the Upper Ob, the steppe zone of the Middle Irtysh) within the river valleys of the Katun, Ob and Irtysh. The wide chronological framework of the sites (the Chultukov Log-9 settlement, the Umrevinsky ostrog, the Salt Turn redoubt) corresponds to the period from the beginning of the 1st millennium AD up to the first quarter of the 18th century and provide an opportunity for the most objective assessment of the tacheometric survey results.Conclusions. The result of the work was the building of the 3D-models of various earthen fortification objects (hillforts, ostrogs, redoubts). Both previously untraceable defensive structures (ditch), and the characteristics of the fenced areas of these structures, previously recorded in written sources, have been identified.

A ChatGPT-MATLAB framework for numerical modeling in geotechnical engineering applications

ChatGPT has recently emerged as a representative of Large Language Models (LLMs) that have brought evolutionary changes to our society, and the effectiveness of ChatGPT in various applications has been increasingly reported. This study aimed to explore the potential of employing programming performance driven by ChatGPT responses to conversational prompts in the field of geotechnical engineering. The tested examples included the analysis of seepage flow and slope stability, and the image processing of X-ray computed tomographic image for partially saturated sand. For each case, the prompt was initially fed by a narrative explanation of the problem attributes such as geometry, initial conditions, and boundary conditions to generate the MATLAB code that was in turn executed to evaluate the correctness and functionality. Any errors and unanticipated results were further refined by additional prompts until the correct outcome was achieved. ChatGPT was able to generate the numerical code at a considerable level, demonstrating creditable awareness of the refining process, when meticulous prompts were provided based on a comprehensive understanding of given problems. While ChatGPT may not be able to replace the entire process of programming, it can help minimize sloppy syntax errors and assist in designing a basic framework for logical programming.

Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. This work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. The results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: (1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, (2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

Comprehensive evaluation of factors influencing selenium fertilization biofortification.

Dietary selenium (Se) deficiency, stemming from low Se concentrations in agricultural products, threatens human health. While Se-containing fertilizers can enhance the Se content in crops, the key factors governing Se biofortification with Se fertilization remain unclear. This study constructed a global meta-analysis dataset based on field experiments comprising 364 entries on Se content in agricultural products and 271 entries on their yield. Random forest models and mixed effects meta-analyses revealed that plant types (i.e., cereals, vegetables, legumes, and forages) primarily influenced Se biofortification, with Se fertilization rates being the next significant factor. The random forest model, which included variables like plant types, Se fertilization rates, methods and types of Se application, initial soil conditions (including Se content, organic carbon content, and pH), soil types, mean annual precipitation, and temperature, explained 82.14% of the variation in Se content and 48.42% of the yield variation in agricultural products. For the same agricultural products, the increase in Se content decreased with higher rates of Se fertilization. The increase in Se content in their edible parts will be negligible for cereals, forages, legumes, and vegetable crops, when Se fertilization rates were 164, 103, 144, and 147 g Se ha-1 , respectively. Conversely, while low Se fertilization rates enhanced yields, high rates led to a yield reduction, particularly in cereals. Our findings highlight the need for balanced and precise Se fertilization strategies to optimize Se biofortification benefits and minimize the risk of yield reduction. © 2024 Society of Chemical Industry.

Review of: "The amount of order and dimensions of the nanowires produced using this set of templates is determined and controlled by the initial conditions of the anodizing process"

Review of: "The amount of order and dimensions of the nanowires produced using this set of templates is determined and controlled by the initial conditions of the anodizing process"

The Analysis of Sub-Synchronous Resonance in a Wind Farm for a Doubly-Fed Induction Generator Using Modern Analytical Method

The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second-order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.

Determination of the effectiveness of the electrocoagulation process in the treatment of leachate from the controlled landfill site in Francisco de Orellana canton

The inadequate management of leachate produced in landfill sites, sanitary dumps, or its incomplete treatment generates significant environmental and public health impacts. These conditions are expected in developing countries and are a major concern, especially in sensitive areas like the Amazon. This study investigated the efficiency of electrocoagulation for removing BOD5, COD, TSS, turbidity, and color using a laboratory-scale reactor. Samples of raw leachate from the controlled landfill site in Francisco de Orellana canton, located in the Ecuadorian Amazon, were used. First, the initial conditions of the leachate were determined through a physicochemical characterization, where a reduced presence of heavy metals and high biodegradability were identified, suggesting that it is old leachate. In turn, a reactor with 5 electrodes was installed, where aluminum was used as a cathode and iron as an anode. Finally, electrocoagulation was employed with various operational combinations, where a run using 2.5 V and 20 minutes showed the highest removal efficiency on average, with reductions of 85.23% of BOD5, 98.20% of COD, 11.30% of TSS, 96.52% of turbidity, and 90.73% of color. Keywords: Electrocoagulation; Leachate; Leachate treatment.

Application of TiH2 dehydrogenation for vapour layer formation under boiling crisis conditions

This study explores TiH2 dehydrogenation for vapor layer formation under boiling crisis [1] conditions, aiming to reduce friction in the maritime sector. Despite the rise of electric vehicles for urban transport, marine vessels lack alternatives to fossil fuels. The research aims to combine compliant surfaces with two-phase flow generation, focusing on testing TiH2 (TH-2). Moreover, the study highlights the need for hydrogen-generating materials and suggests that material choices influence two-phase flow initiation conditions. After conducting experiments, it was determined that TiH2 affects the cooling dynamics: at the initial time moment, the cooling of the samples coated in TiH2 was more intense compared to polished aluminum samples (AL-1) due to the hydrophilic properties of the surface. When the TiH2-coated sample temperature dropped to approx. 200 °C, the cooling process would slow down, or the process would temporarily become isothermic (in some cases, a slight increase in temperature was observed as well) due to the formation of TiO2 or Al2O3, both of which are exothermic reactions. Dehydrogenation likely only takes place at the initial time moment and when the conditions for the Leidenfrost effect are favorable, which would serve to sustain the high temperature of the sample. It was concluded that the amount of accumulated hydrogen gas in cases of TH-2 samples was insufficient to cause a significant effect on critical Leidenfrost temperature. Nevertheless, a significant difference in heat transfer coefficient between AL-1 samples and TH-2 was observed due to physical–chemical processes on the TiH2 film.

Simulating the impact of typhoons on air‐sea CO2 fluxes on the northern coastal area of the South China Sea

The South China Sea is a typhoon-prone region, and previous studies have shown that typhoons have significant impacts on air-sea CO2 fluxes. However, the effect of typhoons on the northern coastal area of the South China Sea is not well understood owing to limited observational data. In this study, we used a coupled model to simulate the impact of four typhoons (Hato, Mangkhut, Nida, and Merbok) on the partial pressure of CO2 in seawater (pCO2sea) and the CO2 fluxes in this area. Our results show that the coupled model effectively reproduces the spatial pattern of pCO2sea in this region. The response of pCO2sea to typhoons was determined by typhoon-induced vertical mixing and coastal upwelling, along with initial oceanic conditions. Typhoon Nida caused a decrease in pCO2sea with Total Alkalinity and Sea Surface Temperature being the primary factors. However, typhoons Hato, Mangkhut, and Merbok caused an increase in pCO2sea with Dissolved Inorganic Carbon playing a more prominent role. The average CO2 fluxes during the passage were approximately 6–14 times higher than those before typhoon passage. These results enhance our understanding of the effect of typhoons on air-sea CO2 fluxes over the northern coastal area of the South China Sea.

Stability analysis of WEST L-mode discharges with improved confinement from boron powder injection

WEST L-mode plasmas with dominant electron heating and no core torque source have observed improvements in confinement during boron (B) powder injection. These results are reminiscent of previous powder injection experiments on other devices and gaseous impurity seeding experiments on WEST. During powder injection, the stored energy increased up to 25% due to enhanced ion and electron heat and particle confinement. The improvements in confinement were not indicative of an L-H transition. To identify the dominant mechanisms and the causality chain behind these improvements in confinement, we employ interpretative modeling using METIS, predictive integrated modeling using a high-fidelity plasma simulator (HFPS), and stand-alone gyrokinetic simulations using quasi-linear gyrokinetic code. Interpretative modeling with METIS allowed for the estimation of missing data while maintaining good overall consistency with experiment. These results provided the initial conditions for fully predictive flux driven simulations using the HFPS. From these simulations, quasi-linear gyrokinetic analysis was performed at ρ=0.5 and ρ=0.65 . Collisionality was found to be a strong candidate for the turbulence suppression mechanism at ρ=0.5 , while a combination of collisionality and the Te/Ti ratio was found to be the likely mechanism at ρ=0.65 . The results additionally suggested that increased Zeff (through main ion dilution) could play a role in the improved confinement, but this could not be confirmed due to a lack of experimental measurements. The modeling framework established here can now be used to evaluate and exploit a variety of future powder injection experiments.

Sustainable Approach to Metal Coin Canceling Methods, Using 3D Modeling and Finite Element Method Analysis

Over time, many minted coins were withdrawn from circulation, being replaced with new ones. The returned, obsolete metal coins were melted in order to ensure sustainable reuse of the alloy for other purposes. Between the withdrawal and melting, some of the metal coins were canceled by the destruction of their original shape and dimensions using adequate tools. The first part of this paper is focused on presenting some insights into the canceling method used on old Romanian nickel coins; also, some examples are presented. The introduction also includes a literature review in the field of coin manufacturing, covering subjects such as metal behavior under striking load and aspects of 3D modeling and FEM analysis as well as explaining some striking errors. The main purpose of this paper is to study the particularities of canceling methods applied to coins, which is conducted on relatively valuable collection metal pieces. In the second part of the paper, an adequate 3D model is computed for the canceling dies and the coin. Then, the assembled models are introduced, corresponding to each canceling case, consisting of the obverse and reverse canceling dies with coins inside them. For each model, the finite element analysis is realized and is achieved for different initial conditions. The final part of the paper presents the analysis results as well as the discussion and conclusions.