Carlos F.M. Coimbra

A gas bubble in an unbounded liquid medium undergoing heat and mass transfer deforms as the bubble surface expands or contracts. Thermocapillary instabilities on the surface of bubbles can lead to convective flow in the near-field of the bubble. If the evaporation-induced thermocapillary instability is large enough, the interfacial tension gradients can sustain the instability and lead to the emergence of localized zones of cooler temperatures, herein referred to as thermal islands. This work uses detailed finite element numerical simulations to accurately resolve the transport and fluid dynamics around the gas–liquid interface. The interaction between shape oscillations, heat and mass transfer, and the fluid flow around the bubble is analyzed. We establish a correlation between the tangential velocities and the temperature gradient along the interface, thus emphasizing the role of the thermocapillary effect. We show how heat transfer in one direction may lead to stability with suppression of the thermal islands, yet the system becomes unstable when the direction is reversed. A discussion on the effect of appropriate dimensionless parameters on the stability of the perturbations is also presented. This investigation provides new insight into the dynamic behavior of nonequilibrium bubbles undergoing heat and mass transfer. It has practical implications for precisely controlling the transfer at gas–liquid interfaces.

Abstract This work addresses challenges and opportunities in the evaluation of solar power plant impacts, with a particular focus on thermal effects of solar plants on the environment and vice-versa. Large-scale solar power plants are often sited in arid or desert habitats, which tend to include fauna and flora that are highly sensitive to changes in temperature and humidity. Our understanding of both shortwave (solar) and longwave (terrestrial) radiation processes in solar power plants is complete enough to render the modeling of radiation fluxes with high confidence for most applications. In contrast to radiation, the convective environment in large-scale solar power plants is much more difficult to characterize. Wind direction, wind speed, turbulence intensity, dust concentration, ground condition, panel configuration density, orientation and distribution throughout the solar field, all affect the local environment, the balance between radiation and convection, and in turn, the performance and thermal impact of solar power plants. Because the temperatures of the two sides of photovoltaic (PV) panels depend on detailed convection–radiation balances, the uncertainty associated with convection affects the heat and mass transfer balances as well. Those balances are critically important in estimating the thermal impact of large-scale solar farms on local habitats. Here we discuss outstanding issues related with these transfer processes for utility-scale solar generation and highlight potential pathways to gain useful knowledge about the convective environment directly from solar farms under operating conditions.

The response of spherical particles to oscillatory fluid flow forcing at finite Reynolds numbers exhibits significant deviations from classical analytical predictions due to nonlinear convective contributions. This study employs finite element simulations to explore the long-term (stationary) behavior of such particles across a wide range of conditions, including various external and particle Reynolds numbers, Strouhal numbers, and fluid-to-particle density ratios. Key contributions of this work include determining the range of validity of Tchen's equation of motion for infinitesimal and finite Reynolds numbers and correlating particle response for a wide range of density ratios and flow conditions at high frequency oscillations. This work introduces a modified form of the history drag term in a newly proposed Lagrangian equation of motion. The new equation incorporates a parameter-dependent fractional-order derivative tailored to accommodate nonlinearities due to convective effects. These novel correlations not only extend the operational range of existing model equations but also provide accurate estimates of particle response under a range of external flow conditions, as validated by comparison with numerical solutions of the Navier–Stokes flow around the particles.

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m 2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE’s physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions. • Remote sensing images are combined with CNNs to produce high-fidelity solar forecasts. • A hybridized physical model of cloud layers increases the forecasting skills. • A Long Short-Term Memory CNN model is applied to further improve solar forecasts. • Remote sensing plus CNNs offer novel ways to forecast solar irradiance. • Hybrid short-term forecasts proposed here outperform existing methods

Abstract Clear sky emittance models provide critical information for the determination of downwelling longwave irradiance at the Earth's surface. This study updates existing calculations which relate clear sky longwave emissivity with the main (and most variable) greenhouse gas in the atmosphere, water vapor. Impacts of station elevation and data quality control are quantified. Empirical results are used to validate highly resolved spectral models, and the resultant simplified calculates provide accurate estimations of clear sky emissivity without the need for extensive computation. Results show that correlation coefficients are mostly robust to nuanced data processing choices when regressed from sufficiently large data sets (≥10 4 samples) with the exceptions of altitude adjustment and measurement bias corrections. The empirical results from this study are compared to results from other leading empirical, physics‐based, and hybridized phenomenological models. Correlations for effective clear sky emissivity, transmissivity and optical depth are provided, based on parameterized line‐by‐line (LBL) model results, for the broadband 0–2,500 cm −1 and for seven wavenumber wideband of interest. Results for the (b3) wideband 580–750 cm −1 are particularly relevant because of its disaggregated and combined carbon dioxide‐water vapor contributions. The broadband effective optical depth ( δ ) of water vapor is found to be , where p w is the dimensionless partial pressure of water vapor at the surface. Equivalently, the broadband effective optical depth of carbon dioxide in the presence of water vapor is found to be . Processed training data sets are provided as supplementary content for comparative studies.

The aluminum minichannel solar collector is a novel technology for solar water heating. Minichannel-based solar collectors have higher thermal efficiency than conventional flat plate collectors and do not suffer from potential loss of vacuum as evacuated-tube collectors. This technology can play a significant role in reducing natural gas consumption that translates into lower greenhouse gas emissions to the atmosphere. However, the performance of solar collectors depends on the geographical location of the installation due to solar resource availability and weather pattern. The potential reduction in natural gas consumption using aluminum minichannel solar collectors is assessed using solar irradiance, ambient temperature, and wind data obtained from ground weather station and satellite-derived data. A data-driven numerical analysis is performed using a validated solar water heater (SWH) model, population, and natural gas consumption data for the entire state of California to assess the best locations to install these systems. The SWH model is validated based on data collected from an actual SWH system installed at a single-family house in Northridge, California. A K-means clustering method is then applied to select the best regions for installation of this technology. Based on performance, population density, and natural gas consumption, the regions of Southern California and the Central Valley are chosen as having the highest potential for reduction of natural gas consumption. The analysis was performed from weather data obtained based on two full years (2020 and 2022), where the effect of COVID-19 (year 2020) is observed as having higher water tank temperatures and higher solar fractions, which could be associated with lower hot water consumption.

The stationary behavior of freely moving spherical particles under harmonic flow forcing from a Newtonian fuid ranging from low to high particle Reynolds (Re p ) numbers and from low to high Strouhal ( Sl ) numbers is studied numerically. This study extends the classical studies on harmonic Stokes flows by exploring particle dynamics in regimes where the convective contributions can no longer be neglected. High-order finite-element numerical simulations determine the order of the derivative that satisfies the long term (stationary) solution for the particle velocities. We propose a new history drag expression that correlates well (R 2 > 0.995) with the numerical results for (radius-based) particle Reynolds numbers up to 10, and for dimensionless frequency ( S = SlRe p ) values up to 10.

Effective decarbonization strategies employ both hard and soft measures to address climate change. Soft approaches can deliver carbon savings comparable to hard approaches, which are typically both infrastructure- and investment-intensive and are often postponed due to financial risks. As demonstrated in this study, the time variable can be used as a lever to reduce energy demand through an academic calendar shift that aligns the end of fall term with Thanksgiving break, thus reducing the need for redundant holiday travel among a significant population. If implemented at all undergraduate campuses of the University of California (UC) system, this strategy would produce a significant reduction (nearly 50,000 tCO2e) in the annual carbon footprint of the UC, an impact approximately equal to decarbonizing all UC-owned vehicles. This outcome is robust to many of its key assumptions and can be realized at any higher education institution that sees a significant portion of its population travel for Thanksgiving. The proposed academic calendar shift is a prime example of a soft decarbonization measure; it can be implemented within existing systems, provides numerous co-benefits, does not require new technologies, and augments ongoing hard decarbonization efforts that will lead to compounding benefits into the future.