Dynamic Energy Research Articles

The temperature-size rule in the context of Dynamic Energy Budget theory

The temperature-size rule (TSR) refers to the observation that a wide variety of organisms reach smaller sizes under warmer temperatures. Although various hypotheses have been stated about the mechanisms underlying TSR, a general understanding is still lacking. In this context, it seems that Dynamic Energy Budget (DEB) theory could be useful, as it constitutes a mechanistic framework that predicts growth and development from individual energetics. However, a critical assumption of this framework is that temperature affects all physiological rates equally, which prevents it from capturing the TSR. It remains an open question if and how the DEB framework can be used to capture the TSR patterns.The model does, however, include a parameter, the food searching rate, that does not need to obey this constraint, as it is a physical rather than a physiological parameter. Here, we aimed to test whether introducing a differing temperature-dependence of food searching rate in DEB models makes it possible to capture the TSR. We parameterized the model using an extensive dataset of Daphnia life history response to temperature. The resulting model was able to capture the decrease of both size at maturity and asymptotic size observed in this dataset.Therefore, DEB models can capture the TSR without any major modification, but only requires to introduce a different function for the temperature dependence of the food searching rate. The mechanism underlying this effect essentially results from increased food limitation at high temperature, which, in combination with the particular allocation scheme of the DEB model, affects differentially growth and development.

Dynamic Mechanical Performance of Sulfate-Bearing Soils Stabilized by Magnesia-Ground Granulated Blast Furnace Slag

Sulfate soils often caused foundation settlement, uneven deformation, and ground cracking. The distribution of sulfate-bearing soil is extensive, and effective stabilization of sulfate-bearing soil could potentially exert a profound influence on environmental protection. Ground granulated blast furnace slag (GGBS)–magnesia (MgO) can be an effective solution to stabilize sulfate soils. Dynamic cyclic loading can be used to simulate moving vehicles applied on subgrade soils, but studies on the dynamic mechanical properties of sulfate-bearing soil under cyclic loading are limited. In this study, GGBS-MgO was used to treat Ca-sulfate soil and Mg-sulfate soil. The swelling of the specimens was analyzed by a three-dimensional swelling test, and the change in compressive strength of the specimens after immersion was analyzed by an unconfined test. The dynamic elastic properties and energy dissipation of GGBS-MgO-stabilized sulfate soils were evaluated using a fatigue test, and the mineralogy and microstructure of the stabilized soils were investigated by X-ray diffraction and scanning electron microscopy. The results showed that the maximum swelling percentage of stabilized Ca-sulfate soil was achieved when the GGBS:MgO ratio was 6:4, resulting in an expansion rate of 14.211%. In contrast, stabilized Mg-sulfate soil exhibited maximum swelling at GGBS:MgO = 9:1, with a swelling percentage of 5.127%. As the GGBS:MgO ratio decreased, the dynamic elastic modulus of stabilized Ca-sulfate soil diminished from 2.8 MPa to 2.69 MPa, and energy dissipation reduced from 0.02 MJ/m3 to 0.019 MJ/m3. Conversely, the dynamic elastic modulus of stabilized Mg-sulfate soil escalated from 2.16 MPa to 6.12 MPa, while energy dissipation decreased from 0.023 MJ/m3 to 0.004 MJ/m3. After soaking, the dynamic elastic modulus of Ca-sulfate soil peaked (4.01 MPa) and energy dissipation was at its lowest (0.012 MJ/m3) at GGBS:MgO = 9:1. However, stabilized Mg-sulfate soil exhibited superior performance at GGBS:MgO = 6:4, with a dynamic elastic modulus of 0.74 MPa and energy dissipation of 0.05 MJ/m3. CSH increased significantly in the Ca-sulfate soil treated with GGBS-MgO. The generation of ettringite increased with the decrease in the GGBS-MgO ratio after immersion. MSH and less CSH were formed in GGBS-MgO-stabilized Mg-sulfate soil compared to Ca-sulfate soils. In summary, the results of this study provide some references for the improvement and application of sulfate soil in the field of road subgrade.

New energy vehicle technology research - Charging technology

In recent years, Chinas economic development is becoming more and more rapid, leading to the increasingly serious pollution emission problem of traditional fuel vehicles. In order to solve the environmental problems, the transformation of automobiles has become an urgent problem. At the present stage, electric vehicles are also widely concerned by all sectors of society because of their low carbon emissions, energy saving and low use cost. With the rapid growth of electric vehicle sales, the demand for charging piles is rising, and the mainstream charging facilities for new energy vehicles are still in short supply. Therefore, we need to focus on efficient wireless charging to solve the major problems in the current electric vehicle industry. This paper analyzes the wireless dynamic energy transmission technology, extending the service life of the battery, how to reduce the cost, the optimization of coil parameters design, the offset of transmission coil and receiving coil, foreign body detection and overheating problems.

Design optimization of the cooling systems with PCM-to-air heat exchanger for the energy saving of the residential buildings

Driven by a rapid surge in cooling demand in buildings, the energy consumption dedicated to cooling has experienced remarkable growth. To address this challenge, the adoption of latent heat thermal energy storage utilizing phase change materials (PCM) has gained significant momentum in recent years. This paper presents the design and evaluation of an integrated latent heat thermal energy storage (ILHTES) system tailored for residential buildings. This system integrates a PCM-to-air heat exchanger (PAHX) with an air conditioning unit. Modelica language is utilized to develop a numerical model for the ILHTES system. The heat transfer model of the PAHX is developed and validated using existing literature data. To simulate the dynamic behavior and energy consumption of the residential building, the open-source library AixLib is adopted. The developed ILHTES system model is used for the optimization of key design variables, including the PCM slab thickness and air flow rate, based on the results of long-term simulations covering the entire cooling season. Evaluation of the energy saving potential of the optimized ILHTES systems is carried out in comparison to conventional air conditioning systems, considering various climatic conditions in five European cities. The results highlight the profound impact of PCM types on the Energy Saving Ratio (ESR) throughout the entire cooling season. Among the four commercially available PCMs examined—RT27, RT25, RT20, and RT18—RT25 consistently outperforms the others. Across all five cities investigated, using RT25 leads to a minimum ESR of 16% in Catania and a maximum ESR of 44.7% in Stockholm for the entire cooling season.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time. This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach withoutfiltering. Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality. The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients. This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patientthroughput.

Dynamic mechanical properties and energy release characteristics of Mg-Na-Al alloy under impact load

To investigate the ignition behavior of Mg-Na-Al alloy under impact load and its mechanical properties under different strain rates, the universal testing machine and Separated Hopkinson Pressure Bar (SHPB) experimental system were used to conduct static and dynamic mechanical performance tests of Mg-Na-Al alloy, and the mechanical properties of Mg-Na-Al alloy under different strain rates were discussed. The light gas gun experimental setup was used to launch Mg-Na-Al alloy projectiles and impact rigid target plates to study the energy release behavior of Mg-Na-Al alloy under impact loads. Finally, based on the 12.7 mm ballistic gun experimental platform, the penetration of 12.7 mm armor-piercing projectiles equipped with Mg-Na-Al alloy wind caps into the homogeneous armor steel plates with a thickness of 15 mm, and the ignition test of the oil box behind the steel plates were conducted. The results indicate that the yield strength of Mg-Na-Al alloy under static compression is 265 MPa, while under high strain rates (from 2490 s−1 to 5640 s−1), the yield strength of Mg-Na-Al alloy is 406–423 MPa, which is 53∼60% higher than that under quasi-static compression. When reaching a certain impact velocity, the Mg-Na-Al alloy projectile breaks and induces ignition behavior, releasing energy. The 12.7 mm armor-piercing projectile equipped with an Mg-Na-Al alloy wind cap penetrated the homogeneous armor steel plate with a thickness of 15 mm and ignited. The released energy and the kinetic energy of the fragments ignited the oil box behind the steel plate.

A New Intelligent Home Automation with Integration of Solar and Grid

This project presents a groundbreaking paradigm shift in residential energy management and automation, offering an innovative solution that seamlessly integrates two diverse energy sources: solar and conventional grid (EB) power. With solar energy taking precedence as the primary power supply, meticulously controlled by the versatile ESP32 microcontroller, the system ensures a consistent and reliable energy supply by intelligently switching to grid power when solar resources are scarce, seamlessly reverting back to solar power as soon as it becomes available again. In addition to its dynamic energy management capabilities, the project incorporates an LCD screen to provide homeowners with real-time insights into their energy consumption patterns, offering a comprehensive overview of usage data from both energy sources. This invaluable information empowers users to make informed decisions about their energy usage habits, fostering a more conscious and efficient approach to power consumption. Furthermore, the integration of the Blynk 2.0 IoT platform elevates the project to new heights by enabling remote monitoring and control of the system. Through this intuitive interface, users can effortlessly manage their energy usage from anywhere using their smart phones or computers, ensuring maximum flexibility and convenience. Expanding beyond traditional energy management, the project delves into the realm of home automation with the incorporation of an additional ESP32 microcontroller. This feature-rich functionality empowers users to remotely control lighting fixtures, enhance safety measures with gas detection capabilities utilizing MQ2 sensors, and efficiently monitor water levels using ultrasonic sensors, all seamlessly integrated into the IoT eco system. This holistic solution not only prioritizes renewable energy sources and automates power source transitions but also leverages the transformative potential of IoT technology to create a sustainable and intelligent approach to energy management. Powered by ESP32 technology, this project serves as a trailblazer for the future of smart, efficient, and eco-friendly homes, setting a new standard for residential energy management and automation.

A complex mixed-methods data-driven energy-centric evaluation of net-positive households

Following the Paris agreement, different policy incentives aiming at the reduction of carbon emissions have been introduced worldwide. Dwellings that benefit from increased renewables penetration, aiming at achieving net-zero and even net-positive energy balance, are being designed and deployed in different countries. This article presents a design mixed-methods approach, based on collected quantitative and qualitative data, to answer the “what”, “why” and “how” of energy prosumption in net-positive dwellings. We demonstrate the strong influence of domestic routines and dynamic energy import and export pricing on explaining energy-centric deviation from net-positive design ambitions. Findings from net-positive neighbourhood households, equipped with geothermal heating, solar generation and electric vehicles, in Norway further provide actionable insights on demand-side reduction and flexibility in energy consumption and how to achieve true energy net-positive balance. Specifically, our analysis demonstrates a significant gap between actual energy bills and user expectations, and potential energy cost reduction up to 10% on a per-activity basis through demand side flexibility in relation to dynamic tariffs as well as a maximum observed bill reduction of up to 50% compared to the baseline scenario for households not adapting their activities inline with dynamic tariffs.

An improvement method for pancreas CT segmentation using superpixel-based active contour

Objective. Pancreas is one of the most challenging organs for Computed Tomograph (CT) image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model (ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model (SbACM) is proposed to improve the segmentation accuracy. Approach. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. Main results. (1) the superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2) as a post-processor, SbACM can increase the Dice similarity coefficients (DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3) Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models. Significance. The proposed SbACM can improve segmentation accuracy with the lowest cost, especially in cases of squeezed fuzzy edges with similar neighborhood , and complex edges.

Macroscale Superlubricity with High Pressure Enabled by Partially Oxidized Violet Phosphorus for Engineering Steel

AbstractAs a novel cross‐structured 2D material, violet phosphorus (VP) promises to further develop the dynamic performance, energy efficiency, and service lifetime of mechanical components owing to its high strength and toughness, large carrier mobility, wide bandgap, and good friction‐reducing properties. In this work, the partially oxidized violet phosphorus nanosheets (oVP) are synthesized and employed as the lubricating additives in the poly alpha olefin (PAO) oil environment with less oleic acid (OA) improver, which can trigger the macroscale superlubricity on the diamond‐like carbon (DLC) film deposited steel surface at high loading pressures (>800 MPa) and sliding speeds (>0.1 m s−1). The friction coefficient (COF) of the steel‐DLC tribopair lubricated by the oVP‐PAO oil can reduce down to 0.0064 with little wear. At the high‐pressure sliding interface, the force‐thermal coupling action during the running‐in process promotes the cleavage and recombination of oVP nanosheets and OA molecules to form a reliable chemical adsorption tribofilm mainly composed of phosphorus oxides and amorphous carbon, which prevents the original asperities from direct contact and provides an ultralow shear strength. These findings suggest a new method to achieve macroscale oil‐based high‐pressure superlubricity for engineering steel through the lubrication and catalyzation effects of oVP.

Pursuing European Green Deal milestones in times of war in Ukraine – a context of energy transition in Poland

Within the sustainable development (SD) paradigm, the European Green Deal (EGD) sets dynamic energy transition goals for the European Union (EU). However, diverse dynamics among member states, exacerbated by events like the COVID-19 pandemic and the war in Ukraine, directly influence this progress. This paper aims to present the energy transition course in EU countries up against the implementation of the objectives of the EGD. This paper conducts a thematic (scoping) literature review and comparative analysis of energy transition progress in all EU countries, concentrating on Poland, which faces unique challenges, including a sluggish pace of energy transition and the effects of the Russian invasion on Ukraine in this regard. Recommendations for Poland include an intensified focus on investments in renewable energy sources (RES) and low-carbon installations. The results demonstrate that collective success in SD necessitates each EU member fulfilling national climate goals, emphasising the need for a unified commitment to navigate complexities and build a sustainable future.

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.

AI-Enhanced Demand Response Strategies in Smart Grids: Toward Sustainable Energy Future

To accomplish a sustainable energy future, this article researches the consolidation of computerized reasoning (AI) into request reaction components inside smart grids. While considering the joining of sustainable power sources and expanding energy utilization, current energy frameworks depend altogether on load the executives and continuous energy request reaction. Conventional methodologies battle with the intricacies of dynamic energy situations. Results from the examination feature the worth of AI and ML for enhancing energy use. Versatile learning for energy proficiency, exact interest determining, continuous observing, and sustainable power source mix are completely made conceivable by these innovations. An intensive vision for the Smart Lattice is introduced, underscoring financial matters, proficiency, wellbeing, ecological obligation, security, and reliability. We check out at the benefits and downsides of incorporating energy stockpiling gadgets and the job of circulated framework insight. The determination gives a careful future vision to AI-enhanced request reaction in smart grids by featuring research issues connected with self-learning frameworks, complete robotization, self-recuperating grids, fitting and-play advances, network safety, and labor force improvement.

Dynamic Energy Budget approach for modeling growth and reproduction of Neotropical stink bugs

Global warming and other climate change drivers have a significant impact on the life cycle of insects which in turn affect their population dynamics and geographic distribution. Among these insects are stink bugs (Hemiptera: Pentatomidae), which are hemimetabolous agricultural pests that cause major damage on crops in many countries. Their life cycle consists of three stages: egg, nymph, and adult. The nymph grows and molts through a small number of instars until reaching emergence, at which point growth ceases. The Dynamic Energy Budget (DEB) theory provides the framework for modeling the life cycle of stink bugs. Nevertheless, determining the most accurate model for nymphal growth and energy allocation during the adult stage remains a challenge, as the best model is not always evident. For two species of the Pentatomidae family, we parameterized and evaluated four DEB models that differ in nymphal growth and the energy allocation rules in adults: isomorphic or V1-morphic nymphal growth combined with the energy allocation scheme in adults that follows the κ-rule or the scheme that the κ-rule is not operational in adults. Overall, all models fit the data, but those with isomorphic nymphal growth reproduce the growth trajectories and instar duration better. However, with the available data we cannot conclude which rule is most likely the energy allocation in adults. Using these models, we further studied the effects of temperature and food availability on individual dynamics and its life cycle. Simulations show that temperature has a higher impact on the duration of life stages and survival, while food availability affects egg production. When simulating a variable environment in terms of temperature for both a summer and a fall generation, the results reveal that the summer generation has higher feeding and egg production rates in comparison to the fall generation. DEB models are valuable tools for simulating individual under various environmental conditions and when combined with appropriate modeling approaches, they can predict population traits and even the potential distribution of a species.

Path planning of stratospheric airship in dynamic wind field based on deep reinforcement learning

The path planning of stratospheric airships holds significant potential for high altitude platform station applications. However, current research is confronted with certain limitations. On the one hand, the existing approaches are predominantly manual due to complex scenarios and numerous factors to consider, which are inefficient and lacks the ability to anticipate the dynamic wind field. On the other hand, the path planning approaches employed by other aircrafts rarely account for the dynamic wind field and energy cycle characteristics, rendering them unsuitable for airships. To address these challenges, this paper proposes a novel path planning approach based on deep reinforcement learning. Firstly, according to the characteristics of airship, the state space and the reward function are well-designed. Secondly, heterogeneous data, including dynamic wind field data and airship state features, are fused as state input for the model. Further, the dueling double deep recurrent Q network is used for path planning model. In contrast to the model employing a double deep recurrent Q network and heuristic algorithm, our proposed approach maintains higher energy and higher success rate, empowering airships to adeptly navigate emergencies.

Physical and mechanical properties of deflection-hardening hybrid fibre-reinforced recycled aggregate concrete

Recycled aggregate concrete (RAC) has been gaining a large interest in recent years due to the depletion of natural aggregates and environmental protection requirements. To improve the ductility of RAC for structural applications and to avoid corrosion risks involved with utilising steel fibres, this study proposes a deflection-hardening hybrid fibre-reinforced recycled aggregate concrete (FRRAC) by combining newly developed macro-basalt fibre (BF, 0.67 % by volume) and recycled macro-polypropylene fibre (PF, 0.67 % by volume). Its fresh, static and dynamic mechanical properties were investigated and compared with the mix containing macro-BF only (1.34 % by volume). A Ø 100 mm split Hopkinson pressure bar (SHPB) system was used to determine the dynamic mechanical properties in compression and tension. The hybrid FRRAC retained up to 90 % of its average first peak load at 3.5 mm crack width and showed better cracking bridging performance under dynamic splitting tension than the specimens with the same volume of macro-BF. Empirical formulae for dynamic increase factors and energy absorption capacities against varying strain rates have also been proposed for the newly developed hybrid FRRAC.

Phloretin prolongs lifespan of Caenorhabditis elegans via inhibition of NDUFS1 and NDUFS6 at mitochondrial complex Ⅰ

Phloretin has been widely perceived as an antioxidant. However, the bioavailability of phloretin in vivo is generally far too low to elicit a direct antioxidant effect by scavenging reactive oxygen species (ROS). Here we showed that administration of phloretin of apple polyphenols extended lifespan of Caenorhabditis elegans and promoted fitness. Specially phloretin enhanced the survival rates of nematodes under oxidants in an inverted U-shaped dose-response manner. The lifespan-extending effects of phloretin were mediated by ROS via mitochondrial complex I inhibition. The increase of ROS stimulated p38 MAPK/PMK-1 as well as transcription factors of NRF2/SKN-1 and FOXO/DAF-16. Consistent with the involvement of NRF2/SKN-1 and FOXO/DAF-16 in lifespan-extending effects, activities of superoxide dismutase (SOD) and catalase (CAT) were enhanced by phloretin. The exogenous application of antioxidants butylated hydroxyanisole and N-acetylcysteine abolished the increase of ROS, the enhancement of SOD and CAT activities, and the lifespan extending effects of phloretin. Meanwhile, with the inhibition of mitochondrial complex I, ATP was instantly decreased. Both energy sensors of AMPK/AAK-2 and SIRT1/SIR-2.1 were involved in the lifespan extension by phloretin. Transcriptomic, real-time qPCR and molecular docking analyses demonstrated that the binding of phloretin at complex I located at NDUFS1/NUO-5, NDUFS2/GAS-1, and NDUFS6/NDUF-6. The molecular dynamic simulation and binding free energy calculations showed that phloretin had high binding affinities towards NDUFS1 (−7.21 kcal/mol) and NDUFS6 (−7.02 kcal/mol). Collectively, our findings suggested phloretin had effects of life expectancy enhancement and fitness promotion via redox regulations in vivo. NDUFS1/NUO-5 and NDUFS6/NDUF-6 might be new targets in the lifespan and wellness regulations.

The storage ring of the Compton source of the NCPM

In this paper, the magnetic structure of the storage ring of an X-ray source based on the effect of Compton backscattering and designed for use in the electron beam energy range from 35 MeV to 120 MeV is investigated. For this storage ring, the results of calculating the dynamics of the electron beam are presented and the effects of radiation damping, quantum excitation, laser damping and intrabeam scattering are considered. The dynamic aperture and energy acceptance of this ring are discussed. The results of calculating the magnitude of the spectral brightness of X-ray radiation and its change over time are presented.

Magnetosheath Ion Field‐Aligned Asymmetry and Implications for Ion Leakage to the Foreshock

AbstractThe ion foreshock is highly dynamic, disturbing the bow shock and the magnetosphere‐ionosphere system. To forecast foreshock‐driven space weather effects, it is necessary to model foreshock ions as a function of upstream shock parameters. Case studies in the accompanying paper show that magnetosheath ions sometimes exhibit strong field‐aligned asymmetry toward the upstream direction, which may be responsible for enhancing magnetosheath leakage and therefore foreshock ion density. To understand the conditions leading to such asymmetry and the potential for enhanced leakage, we perform case studies and a statistical study of magnetosheath and foreshock region data surrounding ∼500 Time History of Events and Macroscale Interactions during Substorms mission bow shock crossings. We quantify the asymmetry using the heat flux along the field‐aligned direction. We show that the strong field‐aligned heat flux persists across the entire magnetosheath from the magnetopause to the bow shock. Ion distribution functions reveal that the strong heat flux is caused by a secondary thermal population. We find that stronger asymmetry events exhibit heat flux preferentially toward the upstream direction near the bow shock and occur under larger IMF strength and larger solar wind dynamic pressure and/or energy flux. Additionally, we show that near the bow shock, magnetosheath leakage is a significant contributor to foreshock ions, and through enhancing the leakage the magnetosheath ion asymmetry can modulate the foreshock ion velocity and density. Our results imply that likely due to field line draping and compression against the magnetopause that leads to a directional mirror force, modeling the foreshock ions necessitates a more global accounting of downstream conditions.

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.