Search Results (528)

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k₀ and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article

Predicting the blow-off (BO) is critical for characterising the operability limits of gas turbine engines. In this study, the applicability of a low-order extinction prediction modelling, which is based on a stochastic variant of the Imperfectly Stirred Reactor (ISR) approach, to predict the lean blow-off (LBO) curve and the extinction conditions in a hydrogen Rich-Quench-Lean (RQL)-like swirl combustor is investigated. The model predicts the blow-off scalar dissipation rate (SDR), which is then extrapolated using Reynolds-Averaged Navier–Stokes (RANS) cold-flow simulations and simple scaling laws, to determine the critical blow-off conditions. It has been found that the sISR modelling framework can predict the BO flow split ratio at different global equivalence ratios, showing a reasonable agreement with the experimental data. This further validates sISR as an efficient low-order modelling flame extinction tool, which can significantly contribute to the development of robust hydrogen RQL combustors by enabling the rapid exploration of combustor operability during the preliminary design phases. Full article

Helium (He) accumulation in tungsten—widely used as a plasma-facing material in fusion reactors—can lead to clustering, trap mutation, and eventual formation of helium bubbles, critically impacting material performance. To clarify the atomic-scale mechanisms governing this process, we conducted systematic molecular statics and molecular dynamics simulations across a wide range of vacancy cluster sizes (n = 1–27) and temperatures (500–2000 K). We identified the onset of trap mutation through abrupt increases in tungsten atomic displacement. At 0 K, the critical helium-to-vacancy (He/V) ratio required to trigger mutation was found to scale inversely with cluster size, converging to ~5.6 for large clusters. At elevated temperatures, thermal activation lowered the mutation threshold and introduced a distinct He/V stability window. Below this window, clusters tend to dissociate; above it, trap mutation occurs with near certainty. This critical He/V ratio exhibits a linear dependence on temperature and can be described by a size- and temperature-dependent empirical relation. Our results provide a quantitative framework for predicting trap mutation behavior in tungsten, offering key input for multiscale models and informing the design of radiation-resistant materials for fusion applications. Full article

This study developed a two-dimensional multiphysics-coupled model for co-electrolysis of CO₂ and H₂O in solid oxide electrolysis cells (SOECs) using COMSOL Multiphysics, systematically investigating the influence mechanisms of key operating parameters including temperature, voltage, feed ratio, and flow configuration on co-electrolysis performance. The results demonstrate that increasing temperature significantly enhances CO₂ electrolysis, with the current density increasing over 12-fold when temperature rises from 923 K to 1423 K. However, the H₂O electrolysis reaction slows beyond 1173 K due to kinetic limitations, leading to reduced H₂ selectivity. Higher voltages simultaneously accelerate all electrochemical reactions, with CO and H₂ production at 1.5 V increasing by 15-fold and 13-fold, respectively, compared to 0.8 V, while the water–gas shift reaction rate rises to 6.59 mol/m³·s. Feed ratio experiments show that increasing CO₂ concentration boosts CO yield by 5.7 times but suppresses H₂ generation. Notably, counter-current operation optimizes reactant concentration distribution, increasing H₂ and CO production by 2.49% and 2.3%, respectively, compared to co-current mode, providing critical guidance for reactor design. This multiscale simulation reveals the complex coupling mechanisms in SOEC co-electrolysis, offering theoretical foundations for developing efficient carbon-neutral technologies. Full article

Vanadium dioxide (VO₂) is a phase-change material of great importance due to its thermochromic properties, which make it a potential candidate for energy-saving applications. In this work, a comparative study between VO₂ thermochromic films prepared from powders synthesized by either a lab-scale hydrothermal autoclave or a large-scale hydrothermal reactor is presented. In both cases, the as-obtained material, after the hydrothermal step, was subsequently annealed at 700 °C under a nitrogen atmosphere, in order to obtain the monoclinic VO₂(M) thermochromic phase. The VO₂ powder prepared in the large-scale hydrothermal reactor exhibited a critical transition temperature of 54 °C with a hysteresis width of 9 °C, while for the one prepared in the lab-scale autoclave, the respective values were 62 °C and 5 °C. Despite these differences, the prepared films showed similar thermochromic performance with the lab-scale material displaying a 17% IR (InfraRed), switching at 2000 nm upon heating, and a transmittance solar modulation of 11%, compared to 17% and 9%, respectively, for the large-scale material. Moreover, both films appeared to have similar luminous transmittance of 44% and 46%, respectively, at room temperature (25 °C). These results showcase the potential for scaling up the hydrothermal synthesis of VO₂, resulting in films with similar thermochromic performance to those from lab-scale fabrication. Full article

With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN. Full article

Municipal solid waste (MSW) and refuse-derived fuel (RDF) incineration generate over 20 million tons of residues annually in the EU. These include bottom ash (IBA), fly ash (FA), and air pollution control residues (APCr), which pose significant environmental challenges due to their leaching potential and hazardous properties. While these residues contain valuable metals and reactive mineral phases suitable for carbonation or alkaline activation, chemical, techno-economic, and policy barriers have hindered the implementation of sustainable, full-scale management solutions. Accelerated carbonation technology (ACT) offers a promising approach to simultaneously sequester CO₂ and enhance residue stability. This review provides a comprehensive assessment of waste incineration residue carbonation, covering 227 documents ranging from laboratory studies to field applications. The analysis examines reactor designs and process layouts, with a detailed classification based on material characteristics, operating conditions, investigated parameters, and the resulting pollutant stabilization, CO₂ uptake, or product performance. In conclusion, carbonation-based approaches must be seamlessly integrated into broader waste management strategies, including metal recovery and material repurposing. Carbonation should be recognized not only as a CO₂ sequestration process, but also as a binding and stabilization strategy. The most critical barrier remains chemical: the persistent leaching of sulfates, chromium(VI), and antimony(V). We highlight what we refer to as the antimony problem, as this element can become mobilized by up to three orders of magnitude in leachate concentrations. The most pressing research gap hindering industrial deployment is the need to design stabilization approaches specifically tailored to critical anionic species, particularly Sb(V), Cr(VI), and SO₄²⁻. Full article

The widely utilized TiO₂ nanoparticles (NPs) tend to accumulate in wastewater and affect microbial growth. This work investigated the impacts of prolonged TiO₂ NP addition to filamentous aerobic granular sludge (AGS) using two identical sequencing batch reactors (SBRs, R1 and R2). R1 (the control) had no TiO₂ NP addition. In this reactor, filamentous bacteria from large AGS grew rapidly and extended outward, the sludge volume index (SVI₃₀) quickly increased from 41.2 to 236.8 mL/g, mixed liquid suspended solids (MLSS) decreased from 4.72 to 0.9 g/L, and AGS disintegrated on day 40. Meanwhile, the removal rates of COD and NH₄⁺-N both exhibited significant declines. In contrast, 5–30 mg/L TiO₂ NPs was added to R2 from day 21 to 100, and the extended filamentous bacteria were effectively controlled on day 90 under a 30 mg/L NP dosage, leading to significant reductions in COD and NH₄⁺-N capabilities, particularly the latter. Therefore, NP addition was stopped on day 101, and AGS became dominant in R2, with an SVI₃₀ and MLSS of 48.5 mL/g and 5.67 g/L on day 130. COD and NH₄⁺-N capabilities both increased to 100%. Microbial analysis suggested that the dominant filamentous bacteria—Proteobacteria, Bacteroidetes, and Acidobacteria—were effectively controlled by adding 30 mg/L TiO₂ NPs. XRF analysis indicated that 11.7% TiO₂ NP accumulation made the filamentous bacteria a framework for AGS recovery and operation without NPs. Functional analysis revealed that TiO₂ NPs had stronger inhibitory effects on nitrogen metabolism compared to carbon metabolism, and both metabolic pathways recovered when NP addition was discontinued in a timely manner. These findings offer critical operational guidance for maintaining the stable performance of filamentous AGS systems treating TiO₂ NP wastewater in the future. Full article

Cell-free protein synthesis (CFPS) has transformed protein production capabilities by eliminating cellular constraints, enabling the rapid expression of difficult-to-produce proteins in an open, customizable environment. As CFPS applications expand from fundamental research to industrial production, therapeutic manufacturing, and point-of-care diagnostics, the diverse array of reactor formats has become increasingly important yet challenging to navigate. This review examines the evolution and characteristics of thirteen major CFPS reactor formats, from traditional batch systems to advanced platforms. The historical development of CFPS reactors from the 1960s to present day is presented. Additionally, for each format, operational principles, advantages, limitations, and notable applications are evaluated. The review concludes with a comparative assessment of reactor performance across critical parameters, including productivity, scalability, technical complexity, environmental stability, and application suitability. To our knowledge this structured analysis is the first to focus predominantly on the various reactor formats of cell-free systems and to provide a guide to assist researchers in choosing the reactor type that best fits their specific applications. Full article

A thorough understanding of fuel behaviour is essential for designing and operating thermochemical systems. Thermogravimetric analysis (TGA) is among the most widely used fuel characterization methods, offering parameters like reactivity and ignition temperature, and enabling comprehensive fuel behaviour assessment through combined indices. This study critically examines the applicability of TGA-based indices for predicting coal performance in industrial processes such as gasification and combustion, where devolatilization, ignition, and burnout stages are key. TGA-derived data are compared with results from established methods, including drop tube furnace (DTF), pulse ignition (PI), and entrained flow reactor (EFR) tests. Findings indicate that the Volatile Matter Release Index (D₂) effectively predicts DTF behaviour (R² = 0.938, max residuals: 4.1 pp), proving useful for fast devolatilization analysis. The Flammability Index (C₁) and Ignition Index (C₃) correlate well with PI results (R² = 0.927 and 0.931, max residuals: 53.3a °C), making them reliable ignition indicators. While TGA tools showed limited accuracy in burnout prediction, the proposed Modified Burnout Characteristic Index (B_1′) achieved reasonable performance (R² = 0.734, max residuals: 0.062%∙°C⁻¹). Overall, selected TGA-based indices offer strong predictive potential for key thermochemical conversion stages. Full article

This study explores innovative strategies for decarbonizing sludge thermal treatments used in electrical power generation, with a focus on integrating sensor technologies and artificial intelligence. Sludge, a carbon-intensive byproduct of wastewater treatment, presents both environmental challenges and opportunities for energy recovery. The paper provides a comprehensive analysis of thermal processes such as pyrolysis, gasification, co-combustion, and emerging methods, including hydrothermal carbonization and supercritical water gasification. It evaluates their carbon mitigation potential, energy efficiency, and economic feasibility, emphasizing the importance of catalyst selection, carbon dioxide capture techniques, and reactor optimization. The role of real-time monitoring via sensors and predictive modeling through artificial intelligence (AI) is highlighted as critical for enhancing process control and sustainability. Case studies and recent advances are discussed to outline future pathways for integrating thermal treatment with circular economy principles. This work contributes to sustainable waste-to-energy practices, supporting global decarbonization efforts and advancing the energy transition. Full article

While traditional risk importance measures in probabilistic risk assessment are effective for ranking safety-significant components, they often overlook critical aspects such as the timing of accident progression and consequences. Dynamic probabilistic risk assessment offers a framework to quantify such risk information, but standardized approaches for estimating risk importance measures remain underdeveloped. This study addresses this gap by: (1) reviewing traditional risk importance measures and their regulatory applications, highlighting their limitations, and introducing newly proposed risk-triplet-based risk importance measures, consisting of timing-based worth, frequency-based worth, and consequence-based worth; (2) conducting a case study of Level 2 dynamic probabilistic risk assessment using the Japan Atomic Energy Agency’s RAPID tool coupled with the severe accident code of MELCOR 2.2 to simulate a station blackout scenario in a boiling water reactor, generating probabilistically sampled sequences with quantified timing, frequency, and consequence of source term release; (3) demonstrating that the new risk importance measures provide differentiated insights into risk significance, enabling multidimensional prioritization of systems and mitigation strategies; for example, the timing-based worth quantifies the delay effect of mitigation systems, and the consequence-based worth evaluates consequence-mitigating potential. This study underscores the potential of dynamic probabilistic risk assessment and risk-triplet-based risk importance measures to support risk-informed and performance-based regulatory decision-making, particularly in contexts where the timing and severity of accident consequences are critical. Full article

Mixed ionic–electronic conducting (MIEC) oxygen-permeable membranes have emerged as a frontier in oxygen separation technology due to their high efficiency, low energy consumption, and broad application potential. In recent years, computational fluid dynamics (CFD) has become a pivotal tool in advancing MIEC membrane technology, offering precise insights into the intricate mechanisms of oxygen permeation, heat transfer, and mass transfer through numerical simulations of coupled multiphysics phenomena. In this review, we comprehensively explore the application of CFD in MIEC membrane research, heat and mass transfer analysis, reactor design optimization, and the enhancement of membrane module performance. Additionally, we delve into how CFD, through multiscale modeling and parameter optimization, improves separation efficiency and facilitates practical engineering applications. We also highlight the challenges in current CFD research, such as high computational costs, parameter uncertainties, and model complexities, while discussing the potential of emerging technologies, such as machine learning, to enhance CFD modeling capabilities. This study underscores CFD’s critical role in bridging the fundamental research and industrial applications of MIEC membranes, providing theoretical guidance and practical insights for innovation in clean energy and sustainable technologies. Full article