Abstract. A reduced-order model (ROM) is developed to capture conjugate aero-thermal physics of effusion cooling in an entire micro-turbine vane/blade. The model considers a single-wall effusion scheme with internal boundary layer flow between the shell and an inner core. Coolant is supplied inside the leading edge and spread to both suction and pressure sides. The compound effect of multiple effusion rows is used to calculate spanwise-averaged cooling effectiveness. Metal temperature is modeled both in streamwise and shell thickness directions. The development of the model and a number of numerical/experimental validation cases are presented in detail in Part I. Part II of the work is geared toward the application of this method to skin cooling of a turbine airfoil by single-wall effusion. The reduced-order model, with all its subroutines functioning together, is validated against a higher fidelity 3D Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) solution. It is shown that the model can predict the main features of the combined internal and effusion cooling in gas-turbine blades at a computational cost which is 105 times lower than RANS (∼1 month with 700M elements and 24 modern Xeon Cores) on a whole turbine vane/blade. Due to this great advantage in speed, a design optimization is then accomplished toward minimizing coolant flow rate while keeping thermal gradients and temperature of the solid within acceptable levels. Implementing this scheme on a typical micro-gas-turbine vane, optimal distributions of the effusion-hole pitch and diameter are found within a given set of constraints. This preliminary design tool potentially enables wider and more efficient usage of effusion cooling in turbine vanes/blades.

While light is the driving force of photosynthesis, excessive light can be harmful. Photoinhibition is one of the key processes that limit photosynthetic productivity. A well-defined mechanism that protects from photoinhibition has been described. Chlorella ohadii is a green micro-alga, isolated from biological desert soil crusts, that thrives under extreme high light (HL). Here, we show that this alga evolved unique protection mechanisms distinct from those of the green alga C. reinhardtii or plants. When grown under extreme HL, a drastic reduction in the size of light harvesting antennae occurs, resulting in the presence of core PSII, devoid of outer and inner antennas. This is accompanied by a massive accumulation of protective carotenoids and proteins that scavenge harmful radicals. At the same time, several elements central to photoinhibition protection in C. reinhardtii, such as psbS, LHCSR, PSII protein phosphorylation and state-transitions are entirely absent or were barely detected. In addition, a carotenoid biosynthesis related protein (CBR) accumulates in the thylakoid membranes of HL cells and may function in sensing HL and protecting the cell from PI. Taken together, a unique photoinhibition protection mechanism evolved in C. ohadii, enabling the species to thrive under extreme-light intensities where other photosynthetic organisms fail to survive.

Advanced machine learning (ML) algorithms have outperformed traditional approaches in various forecasting applications, especially electricity price forecasting (EPF). However, the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training. This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices, an increase in renewable penetration, a change in operational policies, etc. While the dip in model accuracy for unseen data is a cause for concern, what is more, challenging is not knowing when the ML model would respond in such a manner. Such uncertainty makes the power market participants, like bidding agents and retailers, vulnerable to substantial financial loss caused by the prediction errors of EPF models. Therefore, it becomes essential to identify whether or not the model prediction at a given instance is trustworthy. In this light, this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques. The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input. These scores are formulated in two stages: in the first stage, the coarse version of the score is formed using correlations of local and global explanations, and in the second stage, the score is fine-tuned further by the Shapley additive explanations values of different features. Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders. A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm. Results show that the algorithm has more than 85% accuracy in identifying good predictions when the data distribution is similar to the training dataset. In the case of distribution shift, the algorithm shows the same accuracy level in identifying bad predictions.

Phase change materials (PCMs) based on hydrated salts are promising candidates for energy storage and release applications owing to their relatively high latent heats per volume, their high thermal conductivities, and their nonflammability. The use of these PCMs, however, is limited owing to incongruent melting, significant supercooling during crystallization, and irreversible phase transformations. PolyHIPEs, polymers templated within high internal phase emulsions (HIPEs), were proven to be effective at encapsulating aqueous solutions and organic liquids. Here, calcium chloride hexahydrate (CC_HH) was successfully encapsulated within elastomeric, acrylate-based, emulsion-templated polymers by using free-radical polymerization within molten-salt-in-oil HIPEs. The effects of adding a nucleating agent to the internal phase to reduce supercooling and the effects of enhancing HIPE stability through modification of the external phase were investigated. The crosslinking and stabilization modifications made to enhance the original polyHIPE formulation successfully enhanced the encapsulation efficiency. The most promising system, 75% CC_HH dispersed as relatively uniform internal phase droplets of 100–300 μm in diameter, exhibited melting and crystallization heats of ⁓120 J/g, a relatively low degree of supercooling, and negligible transformation to calcium chloride tetrahydrate. This work, demonstrating that molten salt hydrates can be successfully encapsulated within elastomeric, emulsion-templated monoliths, can be used as a blueprint for the encapsulation of other salt hydrates.

Emerging lead halide perovskite (LHP) photovoltaics are undergoing intense research and development due to their outstanding efficiency and potential for low manufacturing costs that render them competitive with existing photovoltaic (PV) technologies. While today’s efforts are focused on stability and scalability of LHPs, the toxicity of lead (Pb) remains a major challenge to their large-scale commercialization. Here, we present a screening-level, EPA-compliant model of fate and transport of Pb leachate in groundwater, soil, and air, following hypothetical catastrophic breakage of LHP PV modules in conceptual utility-scale sites. We estimated exposure point concentrations of Pb in each medium and found that most of the Pb is sequestered in soil. Exposure point concentrations of Pb from the perovskite film fell well below EPA maximum permissible limits in groundwater and air even upon catastrophic release from PV modules at large scales. Background Pb levels in soil can influence soil regulatory compliance, but the highest observed concentrations of perovskite-derived Pb would not exceed EPA limits under our assumptions. Nonetheless, regulatory limits are not definitive thresholds of safety, and the potential for increased bioavailability of perovskite-derived Pb may warrant additional toxicity assessment to further characterize public health risks.

A new multivalent metal–air battery employing Titanium (Ti) as active material in EMIm(HF)_2.3F room temperature ionic liquid electrolyte is introduced. Ti metal is highly attractive as it is a light metal that can possibly transfer up to 4 electrons. The electrochemical behavior of Ti is known for its passivity in various media. Here, we evaluated the electrochemistry of Ti in EMIm(HF)_2.3F with potentiodynamic polarization and galvanostatic discharge experiments. Ti–air battery could successfully be operated under relatively high current densities (up to 0.75 mA cm⁻²) with an average cell voltage of 1–1.2 V, yielding up to a discharge capacity of 66 mAh cm⁻². When its full potential is harvested, such a metal–battery holds a unique potential to be the only metal with 4 electrons transfer during its discharge.

Traditional event-driven emergency load shedding determines the quantitative strategy by simulation of a specific set of expected faults, which requires high model accuracy and operation mode matching. However, due to the model complexity of renewable power generators and fluctuating power generation, traditional event-driven load shedding strategy faces the risk of mismatching in high renewable energy penetrated power systems. To address these challenges, this paper proposes an emergency load shedding method based on data-driven strategies and deep reinforcement learning (RL). Firstly, the reason for the possible mismatch of the event-driven load shedding strategy in the renewable power system is analyzed, and a typical mismatch scenario is constructed. Then, the emergency load shedding strategy is transformed into a Markov Decision Process (MDP), and the decision process’s action space, state space, and reward function are designed. On this basis, an emergency control strategy platform based on the Gym framework is established for application of deep reinforcement learning in the power system emergency control strategy. In order to enhance the adaptability and efficiency of the RL intelligence agent to multi-fault scenarios, the Proximal Policy Optimization (PPO) is adopted to optimize the constructed MDP. Finally, the proposed reinforcement learning-based emergency load shedding strategy is trained and verified through a modified IEEE 39-bus system. The results show that the proposed strategy can effectively make correct strategies to restore system frequency in the event-driven load shedding mismatch scenario, and have good adaptability for different faults and operation scenarios.

Efficient utilization of lignocellulosic biomass as a sustainable alternative resource for biofuels and chemicals is of great importance in view of global warming and depleting fossil fuel reserves. High-pressure homogenization of hydrophobic liquids with cellulose solution or aqueous dispersion of cellulose hydrogel particles yields emulsion of double-shelled micro-particles (dsMP) composed of a hydrophobic core with two concentric shells: an inner shell of aqueous cellulose hydrogel covered by an external coating of continuous amorphous cellulose. Enzymes introduced into the aqueous emulsion medium can be incorporated with the dsMPs: lipases penetrate the shells to the inner core-shell interface, so as to effectively catalyze trans-esterification reactions. Addition of yeasts (S. cerevisiae) to the emulsification process results in integrated hybrids in which the micro-particles are attached to the yeasts' surface. Cellulytic enzymes can be attached to the cellulose surface coatings and the integrated structures exhibited effective simultaneous cellulose saccharification and fermentation to ethanol. Lipase introduced to the aqueous medium are incorporated within the microparticles at the inner core-hydrogel interface, and function effectively in trans-esterification of long-chain molecules dissolved in the core. These results are promising steps towards "one-pot" processes for cellulose valorization by a cascade of biochemical reactions.

In the backdrop of global climate change and expanding urbanization, climate-responsive urban planning and design must be more regularly adopted to better adapt cities to the negative effects of urban heat. One important aspect of securing more effective and efficient heat adaptation actions is the adoption of urban microclimate simulation tools for design purposes, to inform planning professionals on the capacity of different measures to mitigate urban heat and heat stress. However, the accuracy of these tools in representing real-life conditions is questionable and may differ from one urban morphology to another or between different climates and weather conditions.ENVI-met is currently one of the most popular simulation tools of urban microclimates, used by researchers and designers alike. Yet, in recent years some concerns have been raised regarding the software's capability to accurately simulate the effects of solar irradiance on increased heat stress, especially in hot weather conditions. These concerns have eventually led to the implementation of a new radiation scheme in version 5 of the software. This study set out to evaluate whether the new scheme increased the software's accuracy in evaluating incoming shortwave and longwave radiation fluxes, expressed as Mean Radiant Temperature (MRT), by comparing monitored and simulated summertime values at 60 locations.Our results show that while the new version of ENVI-met improved MRT simulation accuracy, the software systematically underestimated the magnitude of MRT in both shaded and unshaded summer conditions. Our MRT evaluation resulted in an RMSE of 6.08 °C in the shade and 13.32 °C at locations fully exposed to solar radiation, which may result in inaccurate and less severe appreciation of outdoor heat stress, especially when outdoor shade is missing. Based on our analysis, we suggest that the origin of error may stem from inaccurate representation of longwave radiation fluxes emitted by ground surfaces.

Decreasing costs of distributed generation and storage, alongside increasing network charges, provide consumers with a growing incentive to defect from the main grid. On a large scale, this may lead to price inflation, hindrance of the energy transition, and even a “death spiral” – a domino effect of disconnections. Here, we develop a game-theoretic framework that demonstrates how conflicting interests among consumers — an aspect that previous studies overlooked — may lead to complex dynamics of grid defection. Our results reveal that although individual consumers benefit from staying connected at the distribution level, the defection of small energy communities from the grid may lead to the defection of larger communities. We also demonstrate that centralized design approaches may lead to inefficient outcomes, e.g., redundant grid expansions, because of the inherent inability to predict potential defections. However, we indicate how, by properly incorporating defection considerations into the grid’s design, social welfare can be improved.