Scaling Curve Research Articles

Research on the spatial quantity distribution of fibers in ultra high performance concrete slabs based on image recognition method

In this paper, the detection of fiber distribution in six UHPC slabs with fiber content of 1%, 2%, 3%, and horizontal pouring and vertical pouring is studied by image recognition method. The results show that: firstly, the fiber distribution can be identified by a series of processing steps, such as grayscale and binarization of fiber source image, extraction of initial detection area, extraction of fiber contour and segmentation of fiber adhesion area. Furthermore, it is proposed that the scaling curve of n-e detection area per pixel of fiber number can anchor the effective recognition area range of fiber. Secondly, the statistical analysis of the identification results can visually draw the fiber distribution maps of different types of UHPC slabs in different directions, and quantitatively characterize the fiber distribution uniformity of UHPC slabs by quoting the evaluation index of fiber distribution coefficient. Finally, the cross-sectional fiber Scatter Density Plot can perceptually show the degree of fiber dispersion and its position distribution. The more uniform the color, the more uniform the fiber distribution in the cross-section of the specimen.

Time-dependent properties of run-and-tumble particles. II. Current fluctuations.

We investigate steady-state current fluctuations in two models of hardcore run-and-tumble particles (RTPs) on a periodic one-dimensional lattice of L sites, for arbitrary tumbling rate γ=τ_{p}^{-1} and density ρ; model I consists of standard hardcore RTPs, while model II is an analytically tractable variant of model I, called a long-ranged lattice gas (LLG). We show that, in the limit of L large, the fluctuation of cumulative current Q_{i}(T,L) across the ith bond in a time interval T≫1/D grows first subdiffusively and then diffusively (linearly) with T: 〈Q_{i}^{2}〉∼T^{α} with α=1/2 for 1/D≪T≪L^{2}/D and α=1 for T≫L^{2}/D, where D(ρ,γ) is the collective- or bulk-diffusion coefficient; at small times T≪1/D, exponent α depends on the details. Remarkably, regardless of the model details, the scaled bond-current fluctuations D〈Q_{i}^{2}(T,L)〉/2χL≡W(y) as a function of scaled variable y=DT/L^{2} collapse onto a universal scaling curve W(y), where χ(ρ,γ) is the collective particle mobility. In the limit of small density and tumbling rate, ρ,γ→0, with ψ=ρ/γ fixed, there exists a scaling law: The scaled mobility γ^{a}χ(ρ,γ)/χ^{(0)}≡H(ψ) as a function of ψ collapses onto a scaling curve H(ψ), where a=1 and 2 in models I and II, respectively, and χ^{(0)} is the mobility in the limiting case of a symmetric simple exclusion process; notably, the scaling function H(ψ) is model dependent. For model II (LLG), we calculate exactly, within a truncation scheme, both the scaling functions, W(y) and H(ψ). We also calculate spatial correlation functions for the current and compare our theory with simulation results of model I; for both models, the correlation functions decay exponentially, with correlation length ξ∼τ_{p}^{1/2} diverging with persistence time τ_{p}≫1. Overall, our theory is in excellent agreement with simulations and complements the prior findings [T. Chakraborty and P. Pradhan, Phys. Rev. E 109, 024124 (2024)1539-375510.1103/PhysRevE.109.024124].

Assessment of reservoir heterogeneities and hydrocarbon potential zones using wavelet-based fractal and multifractal analysis of geophysical logs of Cambay basin, India

The scaling behaviour of physical properties of the earth's subsurface e.g. porosity, permeability, density, etc significantly influence reservoir characteristics. The characterization of the parameters, by analysing nonlinear geophysical logs using the application of linear and traditional empirical relations, is very tedious, complicated, and yields less accurate results. In this perspective, wavelet-based fractals and multifractals techniques provide effective insights into local and global characteristics of heterogeneity of reservoir parameters. The results from the numerical experiment on simulated synthetic data reveal that the wavelet-based fractal and multifractals are more useful to delineate the shorter fluctuations in signal and heterogeneities of the reservoir. The scaling exponent and fractal density, a component of fractal analysis, have been estimated for each sample from power law relationship that facilitates the understanding of the local characteristics of the subsurface formation. On the other hand, the multifractal spectrum, a generalization curve of the hurst exponents, provides the global nature of heterogeneities corresponding to reservoir parameters. We analyse gamma ray, density, and neutron logs of three wells (well-204, 203, and 172) of the Limbodara oil field of Cambay Basin, India. The fractal density curve of gamma ray log is found like the low-pass filtered version of the original signal, and map the lithologies by clustering their similar values while the scaling exponent curve on either side of 0.5 of scaling exponent demarcates the lithological boundaries at the intersection points. The scaling exponent curve highlights the finer variations embedded in the signal and is feasible to enhance the thin layers of the reservoir. The multifractal spectrum of gamma-ray logs across three wells encompasses three major formations emphasizing that the Kalol formation exhibits higher degree of heterogeneity than other formations (Tarapur and Olpad). The multifractal spectrum of neutron and gas corrected log for each well of Kalol formation shows the greater influence on well-203 and lowest influence on well-172, indicating the effective hydrocarbon bearing zones of the former well than the latter. This evidence that an increase in lithological heterogeneity enhances the quality of hydrocarbon reservoir as well as reduces the multifractality of porosity logs.

Effect of ecoregion and river type on neotropical Chironomidae (Diptera) from humid mountain to semiarid lowland

Introduction: Chironomidae (Diptera) is the most widespread and abundant aquatic insect family in freshwater ecosystems. Chironomids are considered good indicators of water quality but are seldom identified at the genus level in broad spatial scale studies. Objective: To identify environmental conditions associated with chironomids in an altitudinal gradient. Methods: We compared ecoregions, river types, and seasons, for chironomids in neotropical streams and rivers (18 river sites; 2014-2018; Yungas rainforest and Western Chaco dry forest, Argentina). We used non-metric multidimensional scaling, dissimilarity, envfit analysis and rank-abundance curves. Results: Chironomic “assemblages” matched both ecoregions and river types. However, ecoregions presented a better fit with species composition. The stenothermal taxa of Orthocladiinae were dominant at high elevations and the eurythermal Chironominae in lowland rivers. Altitude, water temperature and conductivity were important. Seasonal differences were smaller than ecoregional differences. Conclusions: Ecoregions, altitude, water temperature and conductivity correlated with chironomid communities. Orthocladiinae were dominant at high elevations and Chironominae in lowland rivers.

Electrical transport properties of nanocrystalline and bulk nickel ferrite using complex impedance spectroscopy: a comparative study

In this work, the comparative study on the electrical transport properties of nanocrystalline nickel ferrite (NiFe2O4) and its bulk counterpart has been carried out in detail by using complex impedance spectroscopy in a wide range of frequencies (100 Hz–1 MHz) and temperatures (40 °C–320 °C). The dispersive nature of the dielectric constant and loss factor is explained by the Maxwell-Wagner model and Koop’s phenomenological theory. The value of the dielectric constant for nanocrystalline nickel ferrite is found to be more as compared to its bulk counterpart. The frequency variation dielectric permittivity is well fitted with the modified Debye formula, which suggests the presence of multiple relaxation processes. The temperature dependent ac conductivity follows Jonscher’s universal power law and reveals the presence of multiple transport mechanisms from small polaron hopping (SPH) to correlated barrier hopping (CBH) mechanism near 200 °C. The estimated values of Mott parameters are found to be satisfactory. Thermally activated relaxation phenomena have been confirmed by scaling curves of imaginary impedance () and modulus (). The comparison between the and spectra indicates that both long-range and short-range movement of charge carriers contribute to dielectric relaxation with short-range charge carriers predominating at low temperatures while long-range charge carriers are dominating at high temperatures. Analysis of the semicircular arcs of Nyquist plot indicates the presence of grain boundary contribution to the electrical conduction process for the nanocrystalline sample at high temperatures. The non-Debye type of relaxation has been examined by stretching exponential factor (β) which has been estimated by fitting the modified KWW (Kohlrausch-Williams-Watts) equation to the imaginary electric modulus curve. The value of β is found to be strongly temperature dependent and its value for the nanocrystalline sample is less than that of the bulk system which is explained on the basis of dipole-dipole interaction.

(Digital Presentation) Tuning Oxygen Reduction on Monoclinic and Tetragonal Zirconia Surfaces Using Oxygen Vacancy and Nitrogen Doping: A Density-Functional Study

The proton exchange membrane fuel cell (PEMFC) shows the interesting properties,1 where oxygen reduction reaction (ORR) is rate-determining.2 For the PEMFC, Pt-based materials are effective to be its ORR catalyst but their expensive cost and the limited availability are unavoidable,3 motivating to searching non-Pt materials. Zirconia (ZrO2) becomes a considerable candidate for the ORR catalyst since it has a higher chemical stability than that of Pt in an acidic environment,4 where tetragonal ZrO2 (t-ZrO2) (101) and monoclinic ZrO2 (m-ZrO2) (-111) surfaces have been observed to be the most stable.5 In this study, oxygen reduction reaction (ORR) on m-ZrO2 (-111), (-101), (110) and t-ZrO2 (101) surfaces is investigated using the density-functional theory. Each surface is divided into two regimes, that are pristine and defective surfaces, containing defects as two nitrogen impurities and an oxygen vacancy (2NO + VO). Then, the effect of the defects on free energy diagrams and bonding characteristics of an adsorbed oxygen (O*) is investigated. The free energy diagrams correspond to elementary ORR steps involving four intermediates, that are O2*, O2H*, O*, and OH*.As the results, the free energy diagrams show that the first electron transfer, corresponding to the formation of O2H*, is rate-determining on the pristine surfaces. It is modified to the third and fourth electron transfers, corresponding to the formation and removal of OH*, on the defective t-ZrO2 and m-ZrO2 surfaces, respectively. Furthermore, the defects provide a significant effect on the bonding of O*, leading to a slight improvement of ORR catalytic activity on t-ZrO2 but an apparent decrease on m-ZrO2 surfaces. Moreover, due to the defects, adsorption free energies for the defective m-ZrO2 surfaces, ΔG O, ΔG OH, and ΔG OOH, are moving away from the universal scaling curve and the ideal catalyst point. On the other hand, for the defective t-ZrO2 surface, ΔG O, ΔG OH, and ΔG OOH are off the universal scaling curve, heading to the ideal catalyst point. Our result presents the significant effect of the defects, providing a new insight into the oxide-based catalysts. References E. Brightman et al., J. of Power Sources, 242, 244–254 (2013)M. Beltrán-Gastélum et al., Energy, 181, 1225–1234 (2019)X. Sun et al., Electrochim. Acta, 332, 135474 (2020)A. Ishihara et al., J. Phys. Chem. C, 123(30), 18150–18159 (2019)C. Morterra et al., Mater. Chem. Phys., 37(3), 243–257 (1994)

Field-effect conductivity scaling for two-dimensional materials with tunable impurity density

A scaling law is demonstrated in the conductivity of gated two-dimensional (2D) materials with tunable concentrations of ionized impurity scatterers. Experimental data is shown to collapse onto a single 2D conductivity scaling (2DCS) curve when the mobility is scaled by r, the relative impurity-induced scattering, and the gate voltage is shifted by , a consequence of impurity-induced doping. This 2DCS analysis is demonstrated first in an encapsulated 2D black phosphorus multilayer at with charge trap densities programmed by a gate bias upon cooldown, and next in a Bi2Se3 2D monolayer at room temperature exposed to varying concentrations of gas adsorbates. The observed scaling can be explained using a conductivity model with screened ionized impurity scatterers. The slope of the r vs. plot defines a disorder-charge specific scattering rate equivalent to a scattering strength per unit impurity charge density: indicates a preponderance of positively charged impurities with for negatively charged. This 2DCS analysis is expected to be applicable in arbitrary 2D materials systems with tunable impurity density, which will advance 2D materials characterization and improve performance of 2D sensors and transistors.

Why bears hibernate? Redefining the scaling energetics of hibernation.

Hibernation is a natural state of suspended animation that many mammals experience and has been interpreted as an adaptive strategy for saving energy. However, the actual amount of savings that hibernation represents, and particularly its dependence on body mass (the 'scaling') has not been calculated properly. Here, we estimated the scaling of daily energy expenditure of hibernation (DEEH), covering a range of five orders of magnitude in mass. We found that DEEH scales isometrically with mass, which means that a gram of hibernating bat has a similar metabolism to that of a gram of bear, 20 000 times larger. Given that metabolic rate of active animals scales allometrically, the point where these scaling curves intersect with DEEH represents the mass where energy savings by hibernation are zero. For BMR, these zero savings are attained for a relatively small bear (approx. 75 kg). Calculated on a per cell basis, the cellular metabolic power of hibernation was estimated to be 1.3 × 10-12 ± 2.6 × 10-13 W cell-1, which is lower than the minimum metabolism of isolated mammalian cells. This supports the idea of the existence of a minimum metabolism that permits cells to survive under a combination of cold and hypoxia.

Fractional stochastic Loewner evolution and scaling curves

In two-dimensional statistical mechanics, the Stochastic Loewner equation, introduced by Oded Schramm, provides a powerful technique to study and categorize critical random curves and interfaces. For the first time, a new type of stochastic Loewner equation entitled fractional stochastic Loewner evolution (FSLE) has been proposed. We introduce a wide class of fractal curves using the fractional time series as the driving function of the Loewner equation and local fractional integrodifferential operators. We suggest that, in addition to fractal dimension estimations, the Hurst index of the driving function leads significant differences in the FSLE curves. This modification introduces a new approach to categorize different types of scaling curves based on the Hurst index of the driving function. Such formalizations appear to be appropriate for studying a wide range of two-dimensional curves seen in statistical mechanics and natural phenomena.

A study on the isolated photon production in nuclear collisions at the CERN-LHC energies

An analysis of prompt photon production in high energy nuclear collisions at the Large Hadron Collider is performed within the parton saturation picture taking into account the updated phenomenological color dipole models. Comparison between ⟨N coll⟩ scaling for hard scattering in heavy-ion collisions and the N part-scaling based on geometric scaling arguments has been done. The predictions are parameter free in the first case whereas a dependence on the constant of proportionality κ between the number of participants and the nuclear saturation scale appears in the second case. This parameter has been analyzed in the prompt photon spectrum at small transverse momentum even though no fitting procedure was performed. Results are confronted with the measurements made by the ALICE, ATLAS, and CMS experiments in terms of photon transverse momentum at different rapidity bins. We show that the prompt photon production exhibits distinct scalings in AA events associated to geometrical properties of the collision and can be properly addressed in the color dipole formalism. Based on the N part-scaling, an analytical parametrization for the invariant cross section is provided and employed to predict the x T-scaling in measurements. For κ of order of unit the theoretical scaling curve correctly describes data in the range x T ⩽ 5 × 10−2.

Geomagnetically Induced Current Model in New Zealand Across Multiple Disturbances: Validation and Extension to Non‐Monitored Transformers

AbstractGeomagnetically induced currents (GICs) produced during geomagnetic disturbances pose a risk to the safe operation of electrical power networks. One route to determine the hazard of large and extreme geomagnetic disturbances to national electrical networks is a validated model to predict GIC across the entire network. In this study, we improve upon an earlier model for New Zealand, expanding the approach to cover transformers nationwide by making use of multiple storms to develop national scaling factors. We exploit GIC observations which have been made and archived by Transpower New Zealand Ltd, the national grid operator. For some transformers the GIC observations span nearly 2 decades, while for others only a few years. GICs can vary wildly between transformers, particularly due to differences in the electrical network characteristics, transformer properties, and ground conductivity. Modeling these individual transformers is required if an accurate representation of the GIC distribution throughout the network is to be produced. Here we model the GIC during 25 disturbed periods, ranging from large geomagnetic storms to weakly active periods. We adopt the approach of scaling model output using observed GIC power spectra, finding that it improves the correlations between the maximum model and observed GIC by between 10% and 40% depending on the transformer. The modeled GIC at the 73 transformers which have measured GIC are analyzed to create local and national scaling curves. These are used to allow modeling for transformers without in‐situ GIC. We present approaches to utilize this technique for future storms, including non‐monitored transformers.

From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.

Inventory scaling, life cycle impact assessment and design optimization of distributed energy plants

Distributed energy plants can be relevant to mitigate global energy consumption and carbon emissions. The energy and environmental advantages of these systems can be achieved through an optimal design by considering their life cycle energy consumption and environmental impacts. However, the high technical complexity and the scarcity of life cycle inventory data about distributed energy plants makes their design optimization very challenging. This paper proposes a design optimization methodology for distributed energy plants by accounting for life cycle impacts. The plant comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. The life cycle inventory data are scaled at different sizes by using scaling laws which are obtained by gathering data of commercially available systems of various sizes and from different manufacturers. In this manner, impact scaling curves for the quantification of energy and environmental impacts like fossil cumulative energy demand, global warming potential and abiotic resource depletion are obtained. The validity of the proposed methodology is demonstrated by considering the campus of the University of Parma (Italy) as a case study. A distributed energy plant is optimally designed by using a mixed-integer genetic algorithm to minimize life cycle fossil cumulative energy demand. Moreover, an economic assessment of the optimal configurations is also performed. Compared to a conventional plant, the configuration with combined heat and power systems allows a primary energy saving of about 15% and a reduction of total costs of about 12%, while the configuration with reversible heat pumps is the most expensive.

Generalized extreme value statistics, physical scaling and forecasts of gas production in the Haynesville shale

We present a hybrid, data-driven and physics-based method of forecasting play-wide gas production in the Haynesville Shale, which is currently the second-largest shale gas producer in the US. We first define several statistical well cohorts, one for each reservoir quality and each well completion technology in the Haynesville. For each cohort, we use the Generalized Extreme Value (GEV) statistics to obtain the historical average well prototypes. The cumulative production of each well prototype is matched with a physics-based scaling curve, and its production is then extrapolated for up to two more decades. The resulting well prototypes are exceptionally robust. If we replace individual production rates from all existing wells with their corresponding well prototypes and sum them up, the total rate will match remarkably the past gas field rate, and – in this case – we obtain a base or do nothing forecast. Next, we calculate the number of potential infill wells per square mile and schedule future drilling programs to obtain plausible production forecasts in the Haynesville. Because Haynesville is the most active shale play in North America in terms of refracturing, we also propose a novel approach to identify refracturing candidates among the old Haynesville wells and deliver another forecast scenario of future refracs. We predict that Haynesville will ultimately produce 30 Tscf of natural gas from the 4684 existing wells. Most likely, by drilling 923 new wells in the core (sweet spot) areas by 2023, EUR will increase to 40 Tscf. Additional 5023 wells in the noncore areas are forecasted to be drilled by 2032, increasing EUR to 90 Tscf. We also show that refracturing old Haynesville wells is more cost-effective than drilling new wells in the poor quality reservoir, especially in the era of low oil and gas prices.

Naturalness and aesthetics of colors – Preference for color compositions perceived as natural

What makes a colored image, e.g. an abstract painting or a landscape, look pleasing? We hypothesized that a preference for complex color compositions, such as paintings and images of natural scenes, might be related to how natural the colors are perceived. We tested this possibility with two experiments in which the degree of naturalness of images was manipulated by rotating their color gamut rigidly in the color space CIELAB. This changed just the hue composition, but preserved saturation and lightness. In the first experiment we obtained individual scaling curves for perceived naturalness and for preference as a function of the angle of gamut rotation for a small set of images. The naturalness and preference scaling curves were found to be largely similar and their maxima were close to the original image. In the second experiment, we tested whether this effect generalized to a larger set of images. We used a simultaneous 5AFC procedure where in each trial participants had to select the most natural or the most preferred image from five different rotations of the color gamut. The results confirmed the first experiment and showed that, in general, the images perceived as the more natural tend to be the ones that are preferred. Together these results show that perceived naturalness and preference are indeed perceptually closely related and may be driven by related mechanisms.

From nanotubes to nanoholes: Scaling of selectivity in uniformly charged nanopores through the Dukhin number for 1:1 electrolytes.

Scaling of the behavior of a nanodevice means that the device function (selectivity) is a unique smooth and monotonic function of a scaling parameter that is an appropriate combination of the system's parameters. For the uniformly charged cylindrical nanopore studied here, these parameters are the electrolyte concentration, c, voltage, U, the radius and the length of the nanopore, R and H, and the surface charge density on the nanopore's surface, σ. Due to the non-linear dependence of selectivities on these parameters, scaling can only be applied in certain limits. We show that the Dukhin number, Du=|σ|/eRc∼|σ|λD 2/eR (λD is the Debye length), is an appropriate scaling parameter in the nanotube limit (H → ∞). Decreasing the length of the nanopore, namely, approaching the nanohole limit (H → 0), an alternative scaling parameter has been obtained, which contains the pore length and is called the modified Dukhin number: mDu ∼ Du H/λD ∼ |σ|λDH/eR. We found that the reason for non-linearity is that the double layers accumulating at the pore wall in the radial dimension correlate with the double layers accumulating at the entrances of the pore near the membrane on the two sides. Our modeling study using the Local Equilibrium Monte Carlo method and the Poisson-Nernst-Planck theory provides concentration, flux, and selectivity profiles that show whether the surface or the volume conduction dominates in a given region of the nanopore for a given combination of the variables. We propose that the inflection point of the scaling curve may be used to characterize the transition point between the surface and volume conductions.

Scaling lognormal water retention curves for dissimilar soils

ABSTRACT The main objective of the present research is scaling lognormal water retention curves for dissimilar soils. This method requires a reference curve and the amount of water content in a particular suction. The scaling factor in this method equals the log amount of moisture in a particular suction (e.g. ) in the reference soil to the log amount of water content in the same suction in the soil. The proposed method was evaluated for 14 different soil samples. The scaling factor obtained by this method was evaluated by the scaling factor obtained from the optimization method (). The results showed that the average scaling factor based on is closest to the average . The water retention curve obtained by the method presented in this research is fitted on the water retention curve obtained from the lognormal model with superior accuracy.

Classical hydraulic ram pump performance in comparison with modern hydro-turbine pumps for low drive heads

We present a unique comparison of two types of renewable hydro pumping devices, namely the ram and turbine pump having different working principles. This comparison is based on experiments with these pumping devices at a field station in a village in central-east India. Our work identifies the major gaps in the study of large ram pumps, covering the hydraulic scaling conformance to creation of a unified platform for the investigation of such pumping devices with respect to operating parameters like head, flow, efficiency and, most importantly, size. The field tests of ram revealed that its non-dimensional performance parameters fit perfectly on the theoretical scaling curves. The investigations further showed that while rams had more efficiency (53–62%), they could not handle more than 40–50 l/s of discharge. In comparison, the turbine pumps, tested in laboratory conditions, were more compact for flows beyond 100 l/s, but they settled for overall efficiencies between 49% and 55%. The study recommends a limiting design flow line for rams to be 50 l/s because of the size-factor. It further finds that for absolute flows between 50 and 100 l/s within the low-head range of 3–5 meters, the design of geometrical shapes for turbine pumps could be a challenge for optimized efficiencies. The head-discharge-diameter (H-Q-D) chart developed here is a useful way of interpreting the technology with respect to boundary conditions and it creates a roadmap for future inventions in such renewable hydro-pumping devices. The paper makes a case for axial flow pumps as turbines. It concludes with a call for the propagation of research on such nature-friendly pumping technologies, where most (90–95%) of the water is returned to the stream, paving the way for a new era of sustainability.

Convective rain cell characteristics and scaling in climate projections for Germany

AbstractExtreme convective precipitation is expected to increase with global warming. However, the rate of increase and the understanding of contributing processes remain highly uncertain. We investigated characteristics of convective rain cells like area, intensity, and lifetime as simulated by a convection‐permitting climate model in the area of Germany under historical (1976–2005) and future (end‐of‐century, RCP8.5 scenario) conditions. To this end, a tracking algorithm was applied to 5‐min precipitation output. While the number of convective cells is virtually similar under historical and future conditions, there are more intense and larger cells in the future. This yields an increase in hourly precipitation extremes, although mean precipitation decreases. The relative change in the frequency distributions of area, intensity, and precipitation sum per cell is highest for the most extreme percentiles, suggesting that extreme events intensify the most. Furthermore, we investigated the temperature and moisture scaling of cell characteristics. The temperature scaling drops off at high temperatures, with a shift in drop‐off towards higher temperatures in the future, allowing for higher peak values. In contrast, dew point temperature scaling shows consistent rates across the whole dew point range. Cell characteristics scale at varying rates, either below (mean intensity), at about (maximum intensity and area), or above (precipitation sum) the Clausius–Clapeyron rate. Thus, the widely investigated extreme precipitation scaling at fixed locations is a complex product of the scaling of different cell characteristics. The dew point scaling rates and absolute values of the scaling curves in historical and future conditions are closest for the highest percentiles. Therefore, near‐surface humidity provides a good predictor for the upper limit of for example, maximum intensity and total precipitation of individual convective cells. However, the frequency distribution of the number of cells depending on dew point temperature changes in the future, preventing statistical inference of extreme precipitation from near‐surface humidity.

Dependence of Inertial Confinement Fusion capsule performance on fuel reaction rate

In Inertial Confinement Fusion the ignition of the Deuterium–Tritium fuel and the self-sustained thermonuclear burn-wave propagation depend on several factors among them the reaction rate, product of the number densities of the reactants and the thermal reactivity of the fusion reaction. Different mechanisms could affect the reactivity and modify the final thermonuclear gain leading to a failure of ignition. Here, it is analysed the effect of a fuel reaction rate degraded by a given factor $$\upchi ~< ~1$$ on the kinetic energy needed to reach fuel ignition and thermonuclear burn-out. Ignition and burnout thresholds are firstly defined in the metrics of reactivity factor $$\upchi $$ and homothetic scaling curves. Then a parametric variation of $$\upchi $$ shows that a reduction of 10% (20%) on the reaction rate approximately implies a 15% (50% respectively) increase in the kinetic energies thresholds.