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Mixing function vs time relationship for liquid propellant explosion hazards prediction, noting thermocouple grid for maximum information
To support the transition to next-generation refrigerants, accurate modelling of heat transfer and pressure drop is essential for designing efficient heat exchangers. Current models, largely based on traditional refrigerants and unexpanded micro-fin tubes, may not reliably predict performance for new refrigerants and expanded micro-fin geometries. This study evaluates four condensation models using experimental data for six A2L refrigerants: R-32, R-454B, R-454C, R-455A, R-1234yf, and R-1234ze(E). For heat transfer models, the Han and Lee (2005) model initially yields the best accuracy (mean absolute Deviation, MAD = 22.1%). To further improve predictions, a correction factor reduces the Cavallini et al. (2009) model’s MAD from 68.2% to 15.4%, while optimization of the Kedzierski and Goncalves (1997) model achieves a MAD of 13.1%. For pressure drop, the Cavallini et al. (1997) model proves most accurate (MAD = 6.4%), with the simpler Haraguchi et al. (1993) model also effective (MAD = 9.4%). Keywords: Flow condensation models, heat transfer coefficient, frictional pressure drop, next-generation refrigerants, aluminum micro-fin tubes
Developing next-generation battery chemistries that move beyond traditional Li-ion systems is critical to enabling transformative advances in electrified transportation and grid-level energy storage. Here, we provide the first evidence for common descriptors for improved reversibility of metal plating/stripping in nonaqueous electrolytes for multivalent ion batteries. Focusing first on the specific role of chloride (Cl – ) in promoting electrochemical reversibility in multivalent systems, rotating disk (RDE) and ring-disk electrode (RRDE) investigations were performed utilizing a variety of divalent cations (Mg 2+ , Zn 2+ , and Cu 2+ ) and the bis-(trifluoromethane sulfonyl) imide (TFSI – ) anion. By introducing varying concentrations of Cl – , a cooperative effect is observed between TFSI – and Cl – that yields the more reversible behavior of mixed electrolytes relative to electrolytes containing only TFSI – . This effect is shown to be general for Mg, Zn, and Cu electrodeposition, and mechanistic understanding of the role of Cl – in improving reversibility of TFSI-based electrolytes is obtained through the combination of R(R)DE experimental results and density functional theory (DFT) evaluation of the redox activity and thermodynamic stability of various TFSI- and Cl-based solution complexes of metal ions. The cooperative anion effect is further generalized to other mixed-anion systems, where systematic variations in anion association strength predicted from DFT (i.e., Cl – > OTf – ≈ TFSI – > BF 4 – > PF 6 – ) yield corresponding trends in redox potentials and improvements of ≥200 mV in the reversibility of metal deposition/dissolution. These results identify anion association strength as a common descriptor for the reversibility of divalent metal anodes and suggest a set of general design principles for developing new electrolytes with improved activity and stability.
Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.
Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance Computing (HPC) systems. However, Parallel File System (PFS) overhead and failure uncertainty cause significant application overhead. This paper develops an adaptive multi-level C/R model that incorporates a failure prediction and analysis model, which orchestrates failure prediction, checkpointing, checkpoint frequency, and proactive live migration along with the additional benefit of Burst Buffers (BB). It effectively reduces the overheads due to failures, checkpointing, and recovery. Simulation results for the Summit supercomputer yield a reduction of ~20%-86% in application overhead due to BBs, orchestrated failure prediction, and migration. We also observe a ~29% decrease in checkpoint writes to BBs, which can increase the longevity of the BB storage devices.
The dissociative recombination of vibrationally excited O2(+) ions is studied in light of the possible importance of this reaction in upper atmospheric chemistry. A plasma spectroscopy experiment was performed in a microwave cavity filled by an argon-oxygen mixture, with O(1S) production monitored by measurements of the 5577-A afterglow, the O2(+) density and the electron concentration. Plasma and optical data reveal the predominant afterglow ions to be Ar2(+) and O2(+), with an effective O(1S) dissociative recombination coefficient of 2.1 x 10 to the -8th cu cm/sec, corresponding to a quantum yield of 10%. Experiments with an argon-krypton-oxygen mixture reveal that vibrationally excited O2(+) ions are the chief source of the O(1S) atoms, with a specific recombination coefficient for the dissociation of O2(+)(2 pi g) into O(1S) and O(1D) of 4.2 x 10 to the -9th cu cm/sec. A comparison of the laboratory results with Atmospheric Explorer data on the 5577-A airglow implies that O2(+) ions in the sunlit ionosphere are vibrationally excited to the same degree as in the laboratory, with the vibrational relaxation of these ions much slower than dissociative recombination. Results also predict a dawn-twilight asymmetry in the effective O(1S) yield due to the normal variation of electron content.
A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
We present an integrated experimental and theoretical study of the dynamics and rheology of self-crosslinked, slightly charged, temperature responsive soft poly(N-isopropylacrylamide) (pNIPAM) microgels over a wide range of concentration and temperature spanning the sharp change in particle size and intermolecular interactions across the lower critical solution temperature (LCST). Dramatic, non-monotonic changes in viscoelasticity are observed as a function of temperature, with distinct concentration dependence in the dense fluid, glassy, and soft-jammed regimes. Motivated by our experimental observations, we formulate a minimalistic model for the size dependence of a single microgel particle and the change of the interparticle interaction from purely repulsive to attractive upon heating. Using microscopic equilibrium and time-dependent statistical mechanical theories, theoretical predictions are quantitatively compared with experimental measurements of the shear modulus. Good agreement is found for the nonmonotonic temperature behavior that originates as a consequence of the competition between reduced microgel packing fraction and increasing interparticle attractions. Furthermore, testable predictions are made for nonlinear rheological properties such as the yield stress and strain. To our knowledge, this is the first attempt to quantitatively understand in a unified manner the viscoelasticity of dense, temperature-responsive microgel suspensions spanning a wide range of temperatures and concentrations.
We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.
Pure chromium (Cr) targets were exposed to high-flux deuterium (D) plasmas in the Pisces-RF linear plasma device, and measurements of D retention, release, and erosion were subsequently performed. Post-exposure D retention was quantified using temperature-programmed desorption on Cr targets irradiated by 50 eV ions over a broad range of exposure temperatures (423–873 K) and ion fluences (3 × 10 24 – 3 × 10 26 m −2 ). The retained D inventory was observed to decrease rapidly with increased exposure temperature, from approximately ∼7 × 10 20 m −2 at ∼ 420 K, to then saturate near ∼ 10 20 m −2 for exposure temperatures above ∼550 K. Separately, at fixed exposure temperature (∼450 K), D retention was found to have only a weak dependence on increasing ion fluence. Lastly, the erosion of Cr in D plasma was investigated for ion impact energies in the range 40 ≤ E i ≤ 250 eV. Erosion was inferred using optical emission spectroscopy (OES) from the ratio of emission lines (Cr I (425.4 nm)/D I (656.1 nm)) measured close to the target. Conversion of the OES yield data to net erosion yield was made with singular ion energy target mass-loss measurements. These net erosion yield data were then further corrected to obtain gross erosion yield by accounting for a re-deposition factor, computed using a simple model. The gross erosion yield is found to be 2–4 times lower than predicted by SDTrimSP, consistent with that typically observed for light-ion sputtering under high-flux plasma conditions.
The discovery of exotic muonic atoms, including antimatter hydrogen muonic atoms and kaon muonic atoms, constitutes a milestone in our ability to make and study new forms of matter. Relativistic heavy-ion collisions provide the only likely condition for production and detection of these exotic atoms. Taking Coulomb correlations into account from the time of the fireball freeze-out, which refers to the cessation of hadronic interactions in the high-temperature, high-density region formed after the collision, until the formation of a stable atom has dramatic consequences on the expected yields of these atoms. When the coalescence model with the assumption of quantum wave function localization is applied to the formation of muonic atoms, we find that the atom yields are about two orders of magnitude higher than previously predicted.
Pressure-induced phase transformations (PTs) in Si, the most important electronic material, have been broadly studied, whereas strain-induced PTs have never been studied in situ. Here, we reveal in situ various important plastic strain-induced PT phenomena. A correlation between the direct and inverse Hall-Petch effect of particle size on yield strength and pressure for strain-induced PT is predicted theoretically and confirmed experimentally for Si-I→Si-II PT. For 100 nm particles, the strain-induced PT Si-I→Si-II initiates at 0.3 GPa under both compression and shear while it starts at 16.2 GPa under hydrostatic conditions. The Si-I→Si-III PT starts at 0.6 GPa but does not occur under hydrostatic pressure. Pressure in small Si-II and Si-III regions of micron and 100 nm particles is ~5–7 GPa higher than in Si-I. For 100 nm Si, a sequence of Si-I → I + II → I + II + III PT is observed, and the coexistence of four phases, Si-I, II, III, and XI, is found under torsion. Retaining Si-II and single-phase Si-III at ambient pressure and obtaining reverse Si-II→Si-I PT demonstrates the possibilities of manipulating different synthetic paths. The obtained results corroborate the elaborated dislocation pileup-based mechanism and have numerous applications for developing economic defect-induced synthesis of nanostructured materials, surface treatment (polishing, turning, etc.), and friction.
The elements in the Universe are synthesized primarily in stars and supernovae, where nuclear fusion favours the production of even-Z elements. In contrast, odd-Z elements are less abundant and their yields are highly dependent on detailed stellar physics, making theoretical predictions of their cosmic abundance uncertain. In particular, the origin of odd-Z elements such as phosphorus (P), chlorine (Cl) and potassium (K), which are important for planet formation and life, is poorly understood. While the abundances of these elements in Milky Way stars are close to solar values, supernova explosion models systematically underestimate their production by up to an order of magnitude, indicating that key mechanisms for odd-Z nucleosynthesis are currently missing from theoretical models. Here we report the observation of P, Cl and K in the Cassiopeia A supernova remnant using high-resolution X-ray spectroscopy with X-Ray Imaging and Spectroscopy Mission data, with the detection of K at above the 6σ level being the most significant finding. Supernova explosion models of normal massive stars cannot explain the element abundance pattern, especially the high abundances of Cl and K, while models that include stellar rotation, binary interactions or shell mergers agree closely with the observations. Our observations suggest that such stellar activity plays an important role in supplying these elements to the Universe.
A detailed analytic model is presented here to investigate the physics of burn propagation in inertially confined plasmas. The onset of ignition and burn propagation occurs when alpha heating of the hot spot causes rapid ablation of shell mass into the hot spot. This allows large energy gains to be achieved since most of the fuel mass is located in the shell. Here, we first present a comprehensive review of previous analytic models that have been used to describe the physics of hot-spot evolution and ignition; we then show that a proper description of a propagating burn wave requires a comprehensive model of hot spot and shell evolution that includes proper mass conservation in the shell, fusion reactivity, and fuel depletion. The analytic theory is in good agreement with detailed radiation-hydrodynamic simulations that predict the onset of burn propagation as occurring when the yield enhancement caused by alpha heating is between 15- and 25-fold, $f_α$ ~ 1.4, where $f_α$ = alpha energy deposited/hot-spot energy at bang time, and the hot-spot burnup fraction is approximately 2%. We show that the definition of ignition is not sensitive to the alpha-particle stopping power nor asymmetries provided that the absorbed fraction of alpha particles $θ_α$ is correctly accounted for. Finally, we use the results of 2-D simulations to show that even when $θ_α$ is small and unknown (as is true in hot spots with mid modes that have significant leakage of alpha particles into the surrounding cold bubbles), one can still relate the experimentally measureable parameter $\chi^{53}_α$ to the yield amplification and the burning-plasma parameter $Q^{\text{hs}}_α$ = alpha energy deposited/total input work delivered to the hot spot
Multipole moments are the first-order responses of the energy to spatial derivatives of the electric field strength. The quality of density functional theory prediction of molecular multipole moments thus characterizes errors in modeling the electron density itself, as well as the performance in describing molecules interacting with external electric fields. However, only the lowest non-zero moment is translationally invariant, making the higher-order moments origin-dependent. Therefore, instead of using the 3 × 3 quadrupole moment matrix, we utilize the translationally invariant 3 × 3 matrix of second cumulants (or spatial variances) of the electron density as the quantity of interest (denoted by K). The principal components of K are the square of the spatial extent of the electron density along each axis. A benchmark dataset of the principal components of K for 100 small molecules at the coupled cluster singles and doubles with perturbative triples at the complete basis set limit is developed, resulting in 213 independent K components. The performance of 47 popular and recent density functionals is assessed against this Var213 dataset. Several functionals, especially double hybrids, and also SCAN and SCAN0 predict reliable second cumulants, although some modern, empirically parameterized functionals yield more disappointing performance. The H, Li, and Be atoms, in particular, are challenging for nearly all methods, indicating that future functional development could benefit from the inclusion of their density information in training or testing protocols.
The non-thermal particles escaping from collision-less shocks into the surrounding medium can trigger a non-resonant streaming instability that converts parts of their drift kinetic energy into large amplitude magnetic field perturbations, and promote the confinement and acceleration of high energy cosmic rays. We present simulations of the instability using an hybrid-Particle-in-Cell approach including Monte Carlo collisions, and demonstrate that the development of the non-resonant mode is associated with important ion pressure anisotropies in the background plasma. Depending on the initial conditions, the anisotropies may act on the instability by lowering its growth and trigger secondary micro-instabilities. Introducing collisions with neutrals yield a strong reduction of the magnetic field amplification as predicted by linear fluid theory. In contrast, Coulomb collisions in fully ionized plasmas are found to mitigate the self-generated pressure anisotropies and promote the growth of the magnetic field.
In this work, liquid xenon (LXe) is employed in a number of current and future detectors for rare event searches. We use the EXO-200 experimental data to measure the absolute scintillation and ionization yields generated by γ interactions from 228 Th (2615 keV), 226 Ra (1764 keV), and 60 Co (1332 keV and 1173 keV) calibration sources, over a range of electric fields. The W value that defines the recombination-independent energy scale is measured to be 11.5±0.5 (syst.) ±0.1 (stat.) eV. These data are also used to measure the recombination fluctuations in the number of electrons and photons produced by the calibration sources at the MeV scale, which deviate from extrapolations of lower-energy data. Additionally, a semiempirical model for the energy resolution of the detector is developed, which is used to constrain the recombination efficiency, i.e., the fraction of recombined electrons that result in the emission of a detectable photon. Detailed measurements of the absolute charge and light yields for MeV-scale electron recoils are important for predicting the performance of future neutrinoless double β -decay detectors.