Search Results (264)

Solar thermal technology is an important component of low-carbon energy systems, but its application potential is constrained by two key factors: the inherent limits of energy flux density and the temporal mismatch between supply and demand. This study examined efficiency losses in building heating systems in Northwest China caused by the mismatch between supply and demand in intermittent solar thermal storage systems. Three typical building heating models (Day–Night Intermittent Mode, Day–Night + Monthly Intermittent Mode, and Composite Intermittent Mode (Day–Night + Weekly + Monthly)) were constructed through SketchUp, integrating the Transient System Simulation Tool (TRNSYS) with improved calculation methods in an innovative way. The study first examined regional energy consumption patterns and the temporal characteristics of building occupancy and then proposed a collaborative optimization framework for thermal collection and storage, focused on improving the dynamic matching algorithm of the thermal collection area ratio and the tank volume ratio and establishing a tank capacity calculation model that considers the time-varying characteristics of heat demand and fluctuations in thermal collection efficiency during the intermittent heating cycle. The results show that compared with continuous operation, the intermittent strategy reduces the annual cumulative heat load by 13–33%, among which the Day–Night Intermittent Mode shows the daily peak load reaches 1.8 times the normal value during restart, while the daily fluctuation amplitude of the Day–Night + Monthly Intermittent Mode decreases by 42%. The corresponding solar energy guarantee rate reaches 86–88%, and the heat storage loss is reduced by 19–27%. The time-varying coupling design method established in this study provides an optimization path that takes into account both system efficiency and economy for intermittent heating scenarios. The proposed dynamic capacity configuration criterion has universal guiding value for the design of solar district heating systems. Full article

Peat soil is a significant global carbon storage pool, accounting for one-third of the global soil carbon pool. Its greenhouse gas emissions have a significant impact on climate change. Seasonal freeze–thaw cycles are common natural phenomena in high-latitude and high-altitude regions. They significantly affect the mineralization of soil organic carbon and greenhouse gas emissions by altering the physical structure, moisture conditions, and microbial communities of the soil. In this study, through the construction of an indoor simulation experiment of the typical freeze–thaw cycle models in spring and autumn in the Greater Xing‘an Range region of China and the Jinchuan peatland of Jilin Longwan National Nature Reserve, the physicochemical properties, greenhouse gas emission fluxes, microbial community structure characteristics, and key metabolic pathways of peat soils in permafrost and seasonally frozen ground areas were determined. The characteristics of greenhouse gas emissions and their influencing mechanisms for peat soil in northern regions under different freeze–thaw conditions were explored. The research found that the freeze–thaw cycle significantly changed the chemical properties of peat soil and significantly affected the emission rates of CO₂, CH₄, and N₂O. It also clarified the interaction relationship between soil’s physicochemical properties (such as dissolved organic carbon (DOC), dissolved organic nitrogen (DON), ammonium nitrogen (NH₄⁺), soil organic carbon (SOC), etc.) and the structure and metabolic function of microbial communities. It is of great significance for accurately assessing the role of peatlands in the global carbon cycle and formulating effective ecological protection and management strategies. Full article

Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m² compared to 361.0 W/m² from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m²), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO₂ showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m², with a daily average of approximately 340 W/m². These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure. Full article

Massachusetts Bay in the northeastern United States is highly vulnerable to ocean acidification (OA) due to reduced buffering capacity from significant freshwater inputs. We hypothesize that acidification varies across temporal and spatial scales, with short-term variability driven by seasonal biological respiration, precipitation–evaporation balance, and river discharge, and long-term changes linked to global warming and river flux shifts. These patterns arise from complex nonlinear interactions between physical and biogeochemical processes. To investigate OA variability, we applied the Northeast Biogeochemistry and Ecosystem Model (NeBEM), a fully coupled three-dimensional physical–biogeochemical system, to Massachusetts Bay and Boston Harbor. Numerical simulation was performed for 2016. Assimilating satellite-derived sea surface temperature and sea surface height improved NeBEM’s ability to reproduce observed seasonal and spatial variability in stratification, mixing, and circulation. The model accurately simulated seasonal changes in nutrients, chlorophyll-a, dissolved oxygen, and pH. The model results suggest that nearshore areas were consistently more susceptible to OA, especially during winter and spring. Mechanistic analysis revealed contrasting processes between shallow inner and deeper outer bay waters. In the inner bay, partial pressure of pCO₂ (pCO₂) and aragonite saturation (Ω_a) were influenced by sea temperature, dissolved inorganic carbon (DIC), and total alkalinity (TA). TA variability was driven by nitrification and denitrification, while DIC was shaped by advection and net community production (NCP). In the outer bay, pCO₂ was controlled by temperature and DIC, and Ω_a was primarily determined by DIC variability. TA changes were linked to NCP and nitrification–denitrification, with DIC also influenced by air–sea gas exchange. Full article

This study investigates the thermal response and surface modification of low-carbon manganese-alloyed 20GL steel during electrolytic plasma hardening. The objective was to evaluate the feasibility of surface hardening 20GL steel—traditionally considered difficult to quench—by combining high-rate surface heating with rapid cooling in an electrolyte medium. To achieve this, a transient two-dimensional heat conduction model was developed to simulate temperature evolution in the steel sample under three voltage regimes. The model accounted for dynamic thermal properties and non-linear boundary conditions, focusing on temperature gradients across the thickness. Experimental temperature measurements were obtained using a K-type thermocouple embedded at a depth of 2 mm, with corrections for sensor inertia based on exponential response behavior. A comparison between simulation and experiment was conducted, focusing on peak temperatures, heating and cooling rates, and the effective thermal penetration depth. Microhardness profiling and metallographic examination confirmed surface strengthening and structural refinement, which intensified with increasing voltage. Importantly, the study identified a critical cooling rate threshold of approximately 50 °C/s required to initiate martensitic transformation in 20GL steel. These findings provide a foundation for future optimization of quenching strategies for low-carbon steels by offering insight into the interplay between thermal fluxes, surface kinetics, and process parameters. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Land surface models (LSMs) play a crucial role in climate prediction and carbon cycle assessment. To ensure their reliability, it is crucial to evaluate their performance in simulating key processes, such as evapotranspiration (ET) and gross primary productivity (GPP), across various temporal scales and vegetation types. This study systematically evaluates the performance of the newly modernized Noah-MP LSM version 5.0 in simulating water and carbon fluxes, specifically ET and GPP, across temporal scales ranging from half-hourly (capturing diurnal cycles) to annual using observational data from 105 sites within the globally FLUXNET2015 dataset. The results reveal that Noah-MP effectively captured the overall variability of both ET and GPP, particularly at short temporal scales. The model successfully simulated the diurnal and seasonal cycles of both fluxes, though cumulative errors increased at the annual scale. Diurnally, the largest simulation biases typically occurred around noon; while, seasonally, biases were smallest in winter. Performance varied significantly across vegetation types. For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. Conversely, GPP simulations were most accurate for wetlands and closed shrublands, showing the largest deviation for evergreen broadleaf forests. Furthermore, an in-depth analysis stratified by the climate background revealed that ET simulations failed to capture inter-annual variability in the temperate and continental zones, while GPP was severely overestimated in arid and temperate climates. This study identifies the strengths and weaknesses of Noah-MP in simulating water and carbon fluxes, providing valuable insights for future model improvements. Full article

Potamogeton crispus (P. crispus), with strong nitrogen uptake capacity, plays an important ecological role during winter and early spring when most aquatic plants are inactive. Its presence can also influence microbial denitrification in sediments by regulating oxygen levels and organic carbon availability. In this study, an indoor hydroponic simulation system was used to systematically evaluate the effects of P. crispus under different nitrogen-loading conditions on nitrogen removal from water, changes in sediment carbon and nitrogen fractions, microbial community structure, and greenhouse gas fluxes. The results showed that P. crispus effectively removed TN, NH₄⁺-N, NO₃⁻-N, and NO₂⁻-N, maintaining strong denitrification capacity even under high-nitrogen loading. Under all nitrogen conditions, TN removal exceeded 80%, while NH₄⁺-N and NO₃⁻-N removal efficiencies surpassed 90%, with effective suppression of NO₂⁻-N accumulation. Rhizosphere-mediated regulation by P. crispus enhanced the transformation and stabilization of DOC and NO₃⁻-N in sediments, while also mitigating nitrogen-induced disturbances to carbon–nitrogen balance. The plant also exhibited strong CO₂ uptake capacity, low CH₄ emissions with a slight increase under higher nitrogen loading, and N₂O fluxes that were significantly affected by nitrogen levels—showing negative values under low nitrogen and sharp increases under high-nitrogen conditions. Correlation analyses indicated that CO₂ and N₂O emissions were mainly regulated by microbial taxa involved in carbon and nitrogen transformation, while CH₄ emissions were primarily driven by methanogenic archaea and showed weaker correlations with environmental factors. These findings highlight the importance of water restoration during low-temperature seasons and provide a theoretical basis for integrated wetland management strategies aimed at coordinated pollution reduction and carbon mitigation. Full article

Exploring the dynamics of nonlinear nanofluidic flow-induced vibrations, this work focuses on single-walled branched carbon nanotubes (SWCNTs) operating in a thermal–magnetic environment. Carbon nanotubes (CNTs), renowned for their exceptional strength, conductivity, and flexibility, are modeled using Euler–Bernoulli beam theory alongside Eringen’s nonlocal elasticity to capture nanoscale effects for varying downstream angles. The intricate interactions between nanofluids and SWCNTs are analyzed using the Differential Transform Method (DTM) and validated through ANSYS simulations, where modal analysis reveals the vibrational characteristics of various geometries. To enhance predictive accuracy and system stability, machine learning algorithms, including XGBoost, CATBoost, Random Forest, and Artificial Neural Networks, are employed, offering a robust comparison for optimizing vibrational and thermo-magnetic performance. Key parameters such as nanotube geometry, magnetic flux density, and fluid flow dynamics are identified as critical to minimizing vibrational noise and improving structural stability. These insights advance applications in energy harvesting, biomedical devices like artificial muscles and nanosensors, and nanoscale fluid control systems. Overall, the study demonstrates the significant advantages of integrating machine learning with physics-based simulations for next-generation nanotechnology solutions. Full article

Low-carbon lath martensite is highly susceptible to hydrogen embrittlement due to the presence of a high density of lath/block boundaries. In this study, I employ a continuum decohesion model to investigate the effects of varying hydrogen concentrations and tensile loads on the cohesive strength of low- and high-angle block boundaries. The thermodynamic properties of the cohesive zone are described using excess variables, which establish a link between atomistic energy calculations and the continuum model for gradual decohesion at a grain boundary. The aim of this study is to develop an in-depth understanding of how hydrogen affects the cohesive strength of block boundaries in a lath martensitic structure by integrating continuum and atomistic computational modeling and to apply the resulting insights to investigate the effects of varying hydrogen concentrations and tensile loads on interface decohesion. I incorporate hydrogen mobility and segregation at low- and high-angle twist boundaries in body-centered cubic (bcc) Fe to quantify the hydrogen-induced effects on progressive decohesion under tensile stress. A constant hydrogen flux through the free surfaces of a bicrystal containing a block boundary is imposed to simulate realistic boundary conditions. The results of the simulations show that, in the presence of hydrogen flux, separation across the block boundaries occurs at a tensile load significantly lower than the critical stress required for rupture in a hydrogen-free lath martensitic structure. Full article

Methane hydrate is a crystalline compound in which methane is trapped within a water lattice under high-pressure, low-temperature conditions. Its presence in oceanic and permafrost sediments makes it a promising alternative energy source, but also a potential contributor to climate change. Accurate in situ detection remains a major challenge due to hydrate’s dispersed occurrence and the limitations of conventional geophysical methods. This study investigates the feasibility of using the associated alpha particle (AAP) technique for the direct detection of methane hydrate. A series of laboratory measurements was conducted on sand-based samples with varying levels of methane hydrate simulant. Using a 14 MeV neutron generator and a LaBr₃ gamma detector, the 4.44 MeV carbon peak was monitored and calibrated against volumetric hydrate saturation. The minimum detection limit (MDL) was experimentally determined to be $(67\pm 25)\%$. Although the result is subject to high uncertainty, it provides a preliminary benchmark for evaluating the method’s sensitivity and highlights the potential of AAP-based gamma spectroscopy for in situ detection, especially when supported by higher neutron flux in future applications. Full article

Under the global climate governance framework, the Paris Agreement and the China–U.S. Glasgow Joint Declaration established a non-negotiable target of limiting 21st-century temperature rise to 1.5 °C. To date, over 130 nations have pledged carbon neutrality by mid-century, with agricultural activities contributing 25% of global greenhouse gas (GHG) emissions. The spatiotemporal dynamics of these emissions critically determine the operational efficacy of carbon peaking and neutrality strategies. While China’s Nationally Determined Contributions (NDCs) commit to achieving carbon peaking by 2030, a policy gap persists regarding differentiated implementation pathways at the county level. Addressing this challenge, this study selects the Three Gorges Reservoir (TGRA)—a region characterized by monocultural cropping systems and intensive fertilizer dependency—as a representative case. Guided by IPCC emission accounting protocols, we systematically evaluate spatiotemporal distribution patterns of agricultural CH₄ and N₂O emissions across 18 county-level units from 2006 to 2020. The investigation advances through two sequential phases: Mechanistic drivers analysis: employing the STIRPAT model, we quantify bidirectional effects (positive/negative) of critical determinants—including agricultural mechanization intensity and grain productivity—on CH₄/N₂O emission fluxes. Pathway scenario prediction: We construct three developmental scenarios (low-carbon transition, business-as-usual, and high-resource dependency) integrated with regional planning parameters. This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. Methodologically, we establish a multi-scenario simulation architecture incorporating socioeconomic growth thresholds and agroecological constraints. The derived decision-support system provides empirically grounded solutions for aligning subnational climate actions with global mitigation targets. Full article

In an integrated gasification combined cycle (IGCC), a gasification process produces a gas stream from a solid fuel, such as coal or biomass. This gas (syngas or synthesis gas) resulting from the gasification process contains carbon monoxide, molecular hydrogen, and carbon dioxide (other gaseous components may also be present depending on the gasified solid fuel and the gasifying agent). Separating hydrogen from this syngas stream has advantages. One of the methods to separate hydrogen from syngas is selective permeation through a palladium-based metal membrane. This separation process is complicated as it depends nonlinearly on various variables. Thus, it is desirable to develop a simplified reduced-order model (ROM) that can rapidly estimate the separation performance under various operational conditions, as a preliminary stage of computer-aided engineering (CAE) in chemical processes and sustainable industrial operations. To fill this gap, we present here a proposed reduced-order model (ROM) procedure for a one-dimensional steady plug-flow reactor (PFR) and use it to investigate the performance of a membrane reactor (MR), for hydrogen separation from syngas that may be produced in an integrated gasification combined cycle (IGCC). In the proposed model, syngas (a feed stream) enters the membrane reactor from one side into a retentate zone, while nitrogen (a sweep stream) enters the membrane reactor from the opposite side into a neighbor permeate zone. The two zones are separated by permeable palladium membrane surfaces that are selectively permeable to hydrogen. After analyzing the hydrogen permeation profile in a base case (300 °C uniform temperature, 40 atm absolute retentate pressure, and 20 atm absolute permeate pressure), the temperature of the module, the retentate-side pressure, and the permeate-side pressure are varied individually and their influence on the permeation performance is investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit are demanded. The module length is allowed to change in order to satisfy these targets. Other dependent permeation-performance variables that are investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor of the hydrogen permeation. The contributions of our study are linked to the fields of membrane applications, hydrogen production, gasification, analytical modeling, and numerical analysis. In addition to the proposed reduced-order model for hydrogen separation, we present various linear and nonlinear regression models derived from the obtained results. This work gives general insights into hydrogen permeation via palladium membranes in a hydrogen membrane reactor (MR). For example, the temperature is the most effective factor to improve the permeation performance. Increasing the absolute retentate pressure from the base value of 40 atm to 120 atm results in a proportional gain in the permeated hydrogen mass flux, with about 0.05 kg/m².h gained per 1 atm increase in the retentate pressure, while decreasing the absolute permeate pressure from the base value of 20 bar to 0.2 bar causes the hydrogen mass flux to increase exponentially from 1.15 kg/m².h. to 5.11 kg/m².h. This study is linked with the United Nations Sustainable Development Goal (SDG) numbers 7, 9, 11, and 13. Full article

Against the backdrop of climate warming leading to an increase in extreme weather events, extreme precipitation events have become more frequent, and the impact of changes in precipitation on ecosystems cannot be ignored. There is a scarcity of field in situ observational data on greenhouse gas emissions during the growing season for alpine wetlands, especially for alpine river source wetlands, which limits our understanding of the ability of alpine wetland ecosystems to convert between carbon sources and carbon sinks and also hinders our comprehension of the primary effects of extreme precipitation events on wetland ecosystems. In this study, we investigated the main greenhouse gas emission fluxes in two consecutive growing seasons (May to September) under the conditions of natural control (CK), 75% increase in precipitation (IP), and 75% decrease in precipitation (DP) through in situ field simulations of extreme precipitation in an alpine source wetland in the Qinghai Lake Basin of the Qinghai–Tibet Plateau. The results indicate the following: (1) The extreme precipitation increase (IP) treatment did not significantly increase CO₂ fluxes; it promoted CH₄ flux emissions by 168.2% and N₂O flux emissions by 178.4% over the two growing seasons. The extreme precipitation decrease treatment had a non-significant impact on CO₂ fluxes; it inhibited CH₄ emission fluxes by 96.8% and promoted N₂O emission fluxes by 137.8%. (2) During the growing season, CO₂ fluxes were 2.2% lower in the IP treatment than in the DP treatment under the two precipitation patterns; the CH₄ flux under the IP treatment is 84.1% higher than that under the DP treatment, and N₂O fluxes were 43.8% lower in the IP treatment than in the DP treatment. CH₄ fluxes were the most sensitive to precipitation changes. (3) The extreme changes in precipitation were not the main influencing factor for CO₂ fluxes, while CH₄ fluxes were primarily affected by precipitation changes. (4) During the entire growing season, IP reduced the global warming potential (GWP) by 9.03%, and DP decreased GWP by 8.40%. These results suggest that the primary driver of CO₂ fluxes in alpine river source wetlands remains temperature factors; in scenarios where extreme climate events occur frequently, both extreme increases and decreases in precipitation have inhibitory effects on the global warming potential of alpine river source wetlands. Full article