Sustainable Aviation Fuels (SAFs) are recognized as the primary pathway for decarbonizing the aviation sector, yet their higher reactivity relative to conventional kerosene poses challenges for aeroengine compatibility. To enable high-ratio substitution, effective SAF reactivity modulation is essential. This study proposes a strategic approach by introducing renewable low-reactivity components into a SAF base fuel (HEFA-SPK). To this end, the ignition delay times (IDTs) of HEFA-SPK, blended with iso-octane, decalin, and propylbenzene at 50 % by volume, were measured in a rapid compression machine (RCM) at pressures 10–20 bar and temperatures 650–980 K. Results show that propylbenzene exhibits the strongest ignition suppression, followed by iso-octane and decalin. However, a more significant finding lies in their distinct temperature-dependent behaviors. Decalin manifests the strongest negative temperature coefficient (NTC) magnitude and the highest onset temperature. Kinetic analysis reveals that decalin acts as a low-temperature reaction driver by establishing a well-developed reaction network and a robust radical pool during first-stage ignition, whereas iso-octane and propylbenzene undergo limited conversion, contributing minimally to radical generation. This work establishes a predictive framework for modulating SAF reactivity and further supports the 100 % SAF utilization in aeroengines.

With the growing demand for renewable energy, alternative fuels have garnered significant attention. Methanol and biodiesel, as two important alternative fuels, have seen their blends emerge as a research focus due to superior combustion properties. In this study, methyl butanoate was used as a surrogate for biodiesel. The laminar burning velocities of various methanol-methyl butanoate blend ratios are measured using a heat flux method under ambient pressure (1 atm), with an unburned mixture temperature of 348 K, and an equivalence ratio ranging from 0.7 to 1.5. The experimental results indicate that the peak laminar burning velocity shifts to higher equivalence ratios with increasing methanol content, confirming the transition of fuel reactivity from an ester-dominated to an alcohol-dominated characteristic. Comparative analysis with multiple chemical kinetic model simulations reveal that while all models capture the overall trend of burning velocity, significant deviations persist under lean conditions, particularly for pure methyl butanoate (M0), with average absolute relative errors of 3.47% for X. Dong model, 5.54% for S. Dooley model, and 3.63% for S. Gail model. Based on X. Dong model, a simplified skeletal mechanism is developed using Manifold Projection Trajectory method, reducing the original mechanism to 87 species and 276 reactions, achieving a maximum error of 4.79% within the 5% tolerance. On this basis, the pre-exponential factors of 12 elementary reactions in the skeletal mechanism were systematically optimized through sensitivity analysis and reaction pathway tracking. The optimization reinforces key chain-branching reaction pathways to correct the systematic underestimation under low methanol fraction conditions, while coordinately regulating small-molecule reaction pathways to suppress the slight overestimation at high blending ratios. The refined model reduces the overall average absolute relative error from 3.47% to 1.98%, with a maximum error of 6.39% across all tested conditions, which is below the generally accepted 10% threshold. Both the skeletal and refined models show good agreement with experimental data over a wide range of fuel compositions, providing a reliable kinetic foundation for numerical simulations of combustion characteristics of oxygenated fuel blends. Future work may further integrate machine learning with multi-objective genetic algorithms to achieve efficient parameter optimization and mechanism validation.

Tip discharge in oil-immersed transformers poses a significant threat to insulation integrity. Conventional detection methods, such as gas and electrical analysis, are limited by slow response times or susceptibility to interference. Additionally, the lack of systematic comparisons between aged and fresh oil using multi-modal signal correlations hinders the development of accurate diagnostic strategies. To address this, a multi-modal sensing platform employing optical, UHF, and HFCT sensors, complemented by visual observation, was developed to investigate the evolution characteristics and mechanisms of tip discharge and to compare the detection effectiveness of these methods. Experimental results reveal that aged oil undergoes a novel four-stage evolution, where discharge signals first rise to a local peak, then experience suppression, followed by a dramatic surge, and finally decline slightly before breakdown. This process is governed by an “Impurity-Assisted Cumulative Breakdown Mechanism,” driven by impurity bridge growth and space charge effects, with signal transitions from ‘decoupling’ to synchronization. The optical sensor demonstrated superior sensitivity in early discharge stages compared to electrical methods. In contrast, fresh oil exhibited a “High-Field-Driven Stochastic Breakdown Mechanism,” with isolated pulses from micro-bubble discharges maintaining a metastable state until a critical threshold triggers instantaneous failure. This study enhances the understanding of how oil condition alters discharge mechanisms and underscores the value of multi-modal sensing for insulation condition assessment.

Ammonia, a zero-carbon fuel producing no CO2 during combustion, is recognized by the IMO as key for future zero-emission shipping. Ammonia engines are thus pivotal for maritime decarbonization. However, ammonia's inherent characteristics-high ignition energy, slow flame speed, and narrow flammability limits-present challenges for marine engine application. While research on ammonia engine combustion and emissions exists, most focus on small-bore high-speed engines, with limited data on large-bore engines under high AER and load. This study investigates a 270 mm bore marine medium-speed engine. Using port-injected ammonia ignited by direct-injected diesel, the effects of AER and lambda on combustion and emissions were systematically explored at high load (IMEP = 18.6 bar). Results show stable combustion up to 86 % AER. Increasing AER raised peak cylinder pressure, temperature, and pressure rise rate, while thermal efficiency exhibited a non-linear trend, overall exceeding diesel mode efficiency. Higher AER increased total NH3 emission but decreased its escape rate (defined as the ratio of NH3 emission value to the supplied ammonia mass flow rate), NOx emissions first decreased, then increased, and N2O decreased continuously. Over the lambda range tested, its impact on peak pressure and pressure rise rate was limited, but higher lambda significantly lowered peak temperature and thermal efficiency at low lambda. Increasing lambda raised NH3 emissions and escape rate, NOx emissions first increased then decreased, while N2O rose continuously. Crucially, higher lambda effectively reduced CO2 and equivalent CO2 emissions.

Accurate temperature measurement is essential for understanding the combustion behavior of single micron-sized metal particles. Conventional two-color pyrometry typically relies on high-speed cameras to provide temporal resolution, but their high noise and limited dynamic range restrict both accuracy and measurable temperature range. A two-color pyrometry method based on a high-resolution consumer-grade color camera is presented. A vibrating mirror in the optical path encodes temporal information into the particle emission streak, enabling temporal resolution comparable to that of high-speed imaging. Meanwhile, the high spatial resolution and large sensor area allow both the initial particle diameter and the complete combustion trajectory to be recorded in a single image. Owing to the lower noise and wider dynamic range of the camera sensor, the measurable temperature range is significantly extended and the measurement accuracy is improved. In addition, the system cost is at least one order of magnitude lower than that of conventional high-speed camera setups, substantially lowering the barrier to single-particle combustion diagnostics. Novelty and significance statement A low-cost optical diagnostic is developed to measure the combustion temperature evolution and initial size of single micron-sized metal particles. Novelty arises from a purpose-built optical configuration that encodes temporal information into spatial features within a single image, enabling time-resolved pyrometry with a consumer-grade camera. The approach further leverages intrinsic consumer-camera sensor advantages to extend the usable temperature range and improve temperature accuracy relative to conventional high-speed imaging pyrometry. With instrumentation cost reduced significantly without sacrificing fidelity, the method broadens access to high-quality single-particle pyrometry and combustion-related applications.

Ammonia-hydrogen (NH 3 -H 2 ) co-firing is an important technological path for achieving clean combustion, and passive pre-combustion chamber (PPC) has received widespread attention as a low-cost and easy to implement optimization method. However, research on PPC mainly focuses on the discussion of fuel supply strategies, with relatively little research on chemical reactions and structural boundaries, which undoubtedly limits the structural updates of PPC and the development of clean combustion technologies. Based on experiments, this study constructed a simulation model of NH 3 -H 2 engine and then proposed a multivariate structural study by changing the inclination angle (α), swirl angle (β), and nozzle numbers, combined with the nitrogen (N 2 ) labeling method. In the study, thermal pollutants are distinguished by labeling nitrogen that comes from air as N* 2 , and the results showed that Thermal N*O accounted for 50% of the total generated NO, mainly generated in the temperature range of 2400 K-2800 K. The study further clarified the N 2 O generation process, which consists of three stages: high temperature, medium-to-low temperature, and low temperature, and is dominated by Thermal N* 2 O (increased by 83.39%), Fuel N 2 O (increased by 50.75%), and Thermal N* 2 O (increased by 40.37%), respectively. Finally, it is confirmed that the structure with 4-nozzle α of 20° and β of 5° possesses the optimal thermodynamic properties, which can increase the indicated thermal efficiency (ITE) to 40.92%. This study provides new optimization ideas for the efficient utilization and pollutant control of NH 3 -H 2 engines and provides mathematical support and empirical reference for achieving clean combustion.

Abstract Transformer partial discharge (PD) diagnosis may simultaneously face narrowband interference under undersampling conditions, limited fault samples, class imbalance, and multi-source signal mixing. To address these issues, this paper proposes a multi-modal pulse-sequence-based diagnostic framework using synchronized Optical, ultra-high-frequency (UHF), and high-frequency current transformer (HFCT) measurements, and experiments are conducted on a laboratory platform with five typical PD defect models of oil-immersed transformers. For front-end signal processing, a spectral dilation and linear trend replacement (SDLTR) method is proposed to suppress narrowband interference in HFCT signals while preserving the original pulse timing and amplitude characteristics. On this basis, conventional single-sensor pulse sequence analysis (PSA) is extended to an adaptive tri-modal PSA fusion scheme for single-source PD classification. By constructing the temporal union of synchronized Optical, UHF, and HFCT pulse streams and using expert-weighted decision fusion, the proposed method exploits cross-modal complementarity and enlarges the effective sample set. Under class-imbalanced conditions, the four-pulse-based PSA6 fusion scheme achieves an accuracy of 95.47% and a Macro- F 1 of 95.17%. For dual-source PD mixtures, an adaptive cascaded decoupling framework (ACDF) is further proposed by combining class-level precision-weighted fusion, an adaptive confidence boundary, and two-stage dominant-source stripping based on PSA6 and PSA4. The proposed framework produces zero false decisions in single-source verification and correctly identifies both PD sources in all ten dual-source combinations. These results demonstrate that the proposed framework provides an effective and practical solution for transformer PD diagnosis under complex operating conditions.

The development of hybrid electric vehicle technology integrated with ammonia-fueled internal combustion engine (ICE) is expected to effectively promote the mitigation of greenhouse gas (GHG) emissions in the transportation sector. The intelligent charge compression ignition (ICCI) mode based on direct-injection ammonia-diesel dual-fuel engine has been proven to achieve high thermal efficiency and low carbon emissions at medium and high loads. However, its thermal efficiency and GHG emissions are significantly constrained at low-load conditions. To fully exploit the energy-saving advantages of hybrid powertrains and the emission reduction characteristics of ICCI engines, this paper designs and integrates a hybrid powertrain for the ammonia-diesel ICCI (AD-ICCI) engine. Furthermore, dynamic programming (DP) and adaptive equivalent consumption minimization strategy (AECMS) energy management strategies are formulated, and powertrain parameter sizing is conducted. On the basis of the sized powertrain, charge-sustaining simulations are performed. The results demonstrate that, compared with the conventional diesel combustion (CDC) engine hybrid powertrain, the AD-ICCI engine hybrid powertrain achieves substantial GHG emission reductions under comparable power performance, with a 36.5% tank-to-wheels GHG reduction under the DP strategy and a 45.6% NOx reduction under the AECMS strategy. Furthermore, the robustness analysis of the energy management strategies verifies that such emission reduction capabilities are maintained under both charge-depleting and battery-replenishing operations.

Ammonia is considered as a crucial alternative fuel for maritime decarbonization due to its carbon-free combustion. However, the application of ammonia faces challenges such as difficult ignition, slow flame propagation, and a narrow flammable range in compression ignition engines, leading to a prominent issue of unburned NH3 emissions, especially under low-load conditions. This study focuses on a large-bore ammonia-diesel medium-speed engine for marine applications. Through multi-cylinder engine (MCE) and single cylinder engine (SCE) experiments, the formation mechanism of unburned NH3 emissions and combustion optimization strategies under low-load, high-speed conditions are systematically investigated. MCE test results show that under 25% load and 750 r/min conditions, unburned NH3 emissions can reach 42.01 g/kWh. Based on this, refined control studies on intake air temperature (Tair_in), excess air coefficient (lambda), and diesel pilot-main injection parameters, including pilot injection ratio (PQR), pilot injection timing (SOI_Dp), injection pressure (PCR), were conducted on a SCE. Results show that increasing Tair_in to 343 K and reducing lambda to 1.64 improve in-cylinder thermal conditions, reducing NH3 emissions by similar to 30%. Furthermore, an optimized injection strategy (PQR of 30%, SOI_Dp at -40 degrees CA ATDC, PCR of 1200 bar) achieved a synergistic reduction of similar to 80% in NH3 emissions. This study demonstrates the effectiveness of the synergistic "thermal-atmosphere-injection" strategy in suppressing unburned NH3 emissions under low-load conditions. It is important to note that NOx and N2O are also significant emission metrics, but unburned NH3 is the predominant issue under low-load conditions, which represents the primary bottleneck for current engineering applications. Hence, this study prioritizes its investigation. By clarifying the dominant controlling factors of unburned NH3, this work lays a theoretical and experimental foundation for future multi-objective synergistic emission reduction (including NOx and N2O), and provides an engineering pathway for clean combustion control in large-bore ammonia-diesel medium-speed marine engines.

Hydrogen-assisted pre-chamber turbulent jet ignition (TJI) technology has demonstrated great potential for enhancing zero-carbon ammonia combustion. However, the concentration distribution of H2 affects the jet flame strength and the subsequent flame propagation process. Therefore, this study aims to comprehensively evaluate the impact of H2 multiple injection strategies on mixture formation, ignition, combustion and emission characteristics in an active pre-chamber ammonia-hydrogen engine with a high compression ratio of 21:1 under a load condition of IMEP = 15 bar by employing advanced computational fluid dynamics (CFD) coupled with detailed chemical kinetics. It is found that, compared to the single injection, multiple injection strategy enables to enhance fuel stratification inside the pre-chamber, which significantly accelerates the development process of the flame kernel, thereby promoting the turbulent flame propagation in the main chamber. Noteworthy, the indicated thermal efficiency (ITE) is dramatically improved from 45 % to nearly 50 % employing multiple injection. Moreover, multiple injections proved to be more effective in reducing pollutant emissions than single injection, with substantial mitigations in NH3 and N2O. In addition, the H2 mass in the pre-chamber decreases as the injection timing of first pulse (SOI1) is advanced, but the SOI1 has less influence on the combustion phasing and ITE under the current conditions. At fixed injection timing, increasing the proportion of the first injection pulse tends to increase the H2 amount in the main chamber, thus favoring the flame propagation process, and also could reduce unburned NH3 and N2O emissions, with the largest effect on unburned NH3. Given the less total mass injected, the effects of the injection ratio on the ignition and combustion characteristics are slight. The H2 concentration inside the pre-chamber increases significantly as delaying the injection timing of second pulse (SOI2). However, the formation and development processes of flame kernel in the pre-chamber are greatly diminished with the too-late SOI2 timing, which results in the retarded combustion phasing and prolonged combustion duration, ultimately deteriorating the combustion performance and emissions. Besides, the SOI2 shows a significant effect on the generation of unburned NH3 and N2O but a smaller effect on NO. As such, multiple injection strategy is a promising technology for achieving high efficiency and low emissions in ammonia-fueled TJI engines.

This work adopted intelligent charge compression ignition (ICCI) on a methanol-diesel dual fuel engine and explored the effects of injection timing of methanol and stratification strategy on combustion and emissions under high methanol energy ratio (MER) at low loads. Single injection of methanol and diesel was first adopted at indicated mean effective pressure (IMEP) of 3.5 bar and 6 bar, and comparisons were made on different stratification strategies. Results showed that by adjusting proper injection timings of methanol at 3.5 bar and 6 bar, indicated thermal efficiency (ITE) can be improved to above 43 %. Meanwhile, the coefficient of variation of IMEP (COV) can be reduced to about 1.5 % under 6 bar. Delaying injection timing of methanol can lead to a decrease in CO emissions, while NOx and HC emissions do not change much. Within these operating conditions, methane, ethylene, and acetylene emissions are relatively low, while methanol, formaldehyde, and formic acid emissions decline orderly. Almost all particle diameters are within 200 nm, and most particles are in nuclei mode. Compared to the aforementioned single injection strategy, single injection of diesel + double injection of methanol has a comparative ITE and a lower COV. Emissions with the two strategies also have their advantages and disadvantages. Thus single injection of diesel + double injection of methanol has its potential at low loads. However, adjusting the single injection of methanol to intake stroke, or applying double injection of both diesel and methanol, are at a disadvantage compared to the original strategy.

Ammonia, with low volumetric energy density, exhibits high thermal efficiency like diesel. Based on piston dynamics, the concept of heat to work conversion intensity (HWCI) was proposed, which is defined as the ratio of instantaneous indicated work to instantaneous heat release (δWδQ) under the condition of neglecting transient losses, and is used to explain the high thermal efficiency phenomenon of ammonia engines and identify a key temporal heat release window for efficient work. The injection direction was systematically investigated based on the HWCI. Results indicate that while horizontal plane adjustments have limited effect due to cooling and exhaust losses, the longitudinal plane adjustments effectively expand the equivalence combustion range and concentrate heat release during high HWCI periods, enhancing engine performance. The high ambient temperature resulting from concentrated heat release significantly reduced the emissions of N2O and unburned NH3. Based on HWCI analysis and thermodynamic cycle considerations, the heat release in rapid and lower rate combustion phases were both optimized, the ammonia engine can achieve an indicated thermal efficiency (ITE) 5.2% higher than a comparable diesel engine, while maintaining the similar peak firing pressure (20 MPa). These findings provide a practical pathway for implementing high efficiency ammonia combustion in marine propulsion systems under realistic operational constraints.

Recyclable metal fuels such as iron are promising carbon-free energy carriers for heat and power. In such systems, particle ignition characteristics strongly affect combustion efficiency and combustor stability, making them critical for burner and reactor design. However, predictive ignition modelling remains limited by the lack of time-resolved data for single-particle solid-phase oxidation and phase transitions. In this work, digital in-line holography combined with ultra-high-speed single-color pyrometry is used to resolve characteristic solid-phase oxidation times of spherical micron-sized iron particles burning in well-defined hot oxidizing environments. Three temperature plateaus are identified, corresponding to FeO melting, the γ-Fe to δ-Fe transition, and Fe melting, from which pre-melting oxidation times and melting durations are extracted. An ignition model based on solid-phase iron oxidation kinetics following a parabolic rate law, coupled with external-oxygen-transport-limited description, is used to simulate these characteristic times. The model accurately captures the FeO-scale pre-melting oxidation time, which is nearly independent of oxygen concentration, while the FeO, γ-Fe to δ-Fe, and Fe melting stages show strong oxygen-concentration dependence consistent with external-oxygen-transport-limited reaction rates. These measurements and simulations provide the first diameter-resolved dataset for FeO and Fe melting processes and show that this modelling framework can quantitatively predict characteristic times for single iron particles in metal-fuel applications.

Toluene is a representative aromatic component in gasoline surrogates, and its combustion characteristics have arisen significant research interest. However, the laminar burning velocity of toluene, a paramount fundamental combustion property, shows significant data scatterings among available literature studies, where there also lacks the data under the very lean conditions. In the present study, the laminar burning velocities of toluene flames, together with toluene reference fuel (TRF), and primary reference fuel (PRF) flames, were measured at 1 atm and 298 K using the heat flux method. The experiments included ultra-lean conditions with equivalence ratio as low as 0.5, which was achieved through oxygen enrichment. Extrapolation using a validated linear model was adopted for the lean fuel+air laminar burning velocities that can’t be measured directly. Together with the present data, all the available 1 atm toluene+air data were collected in literature, and more reliable datasets for model validations were suggested based on comprehensive data consistency analyses. This process was also applied to benzene+air flames, as benzene is an important intermediate species during toluene combustion and an individual fuel. Two widely used detailed models for gasoline surrogates, from LLNL and CRECK, were assessed using the suggested datasets, where noticeable deviations were found for both models. A reduced toluene subset was also proposed in the present study, combined with the former model from the authors, yielding a compact model with lower deviations across all conditions. Beside the experiments and simulations, global flame characteristics and reaction sensitivities were analysed, revealing toluene flames to be more stable and robust, contrasting with isooctane, n-heptane, and PRF flames, which doesn’t have noises in the ultra-lean measurements that associated with pulsating flame instabilities. The importance of cyclic-carbon reactions on laminar burning velocities under the various equivalence ratio conditions was also highlighted, different from the isooctane, n-heptane, and PRF flames that are governing mostly by the H2/C1 reactions especially under the ultra-lean conditions.

Liquid ammonia direct injection is a promising technical route for decarbonizing heavy-duty engines with higher energy density. However, it faces significant challenges regarding ignition stability and thermal efficiency, especially at high ammonia energy ratios (AERs). To address these limitations, this study proposes a global reactivity enhancement (GRE) strategy on an ammonia/diesel dual-fuel direct injection engine. Distinct from conventional diesel pilot ignition modes, GRE strategy utilizes diesel pre-injection during the intake stroke to construct a homogeneous "active-thermal atmosphere". Through the low-temperature oxidation of the preinjected diesel, critical radicals such as OH and HO2 are inferred to be formed, significantly lowering the global ignition activation energy barrier. First, in order to establish an ideal active-thermal atmosphere for facilitating ammonia ignition, the optimal diesel pre-injection timing and ratio are identified. Furthermore, the effect of intake pressure on the ignition process and emissions is elucidated. Experimental results demonstrate that the GRE strategy broadens the stable operating conditions, enabling stable combustion at 12 bar IMEP with a maximum AER of 88%. Notably, a peak indicated thermal efficiency (ITE) of 46.6% was achieved at an AER of 86%. Crucially, the study reveals that the ammonia combustion efficiency is dominated by the equivalence ratio and the active-thermal atmosphere window. In addition, a synergistic control of unburned NH3 and NOx emissions is realized which can significantly reduce the reliance on the downstream aftertreatment system, thereby minimizing the cost and complexity. These findings validate the GRE strategy as a robust technical framework for the advancement of next-generation, zero-carbon ammonia engines.

This paper proposes an intelligent detection method for bolt loosening in Gas Insulated Switchgear (GIS) basin insulators based on a hybrid DCN-Transformer architecture. The method utilizes a multi-channel ultrasonic system to acquire guided wave signals under varying clamping conditions. To address the limitations of traditional Convolutional Neural Networks (CNNs) in handling local waveform distortions and global temporal dependencies, the proposed model integrates Deformable Convolutional Networks (DCN) with a Transformer encoder. DCN adaptively extracts local features by dynamically adjusting the sampling offsets, while the Transformer captures long-range dependencies across time, frequency, and sensor channels. Experimental results on a 252 kV GIS basin insulator platform demonstrate that the proposed method achieves high accuracy in classifying bolt loosening states and exhibits superior robustness against environmental noise and operating condition variations compared to conventional methods.

The biodiesel-methanol blends, as a renewable fuel, can reduce fossil energy dependence, yet its application requires precise kinetic mechanisms to optimize combustion and reduce emissions. However, biodiesel's high boiling point and low saturated vapor pressure hinder the acquisition of adiabatic flame speed data needed for mechanism validation. To address this, this study chose Methyl Decanoate (MD) and Methyl Butyrate (MB) as biodiesel surrogates, applying a nitrogen-assisted gasification method to stabilize MD vaporization. Laminar burning velocities (SL) for various blends were measured via the heat flux method at 1 atm, 323 K, and equivalence ratios (Phi) from 0.7 to 1.5, filling a key experimental data gap. Results show that increasing methanol content raises the SLmax and shifts it toward Phi = 1.2. While existing kinetic models accurately predict the SLmax location, they over- or under-predict its magnitude at high equivalence ratios. Notably, MD blends exhibited lower SL values than MB blends, better reflecting biodiesel's combustion characteristics. Sensitivity and reaction path analyses reveal that for the MD-methanol blends, overestimation of radical reactions (e.g., CH3 + H (+M) = CH4 (+M)) leads to an overprediction of the SL. Similarly, in blends with high MB content, overestimation of reactions involving C2H3 and C2H2 (e.g., C2H3 + H = C2H2 + H2, C2H2 + H (+M) = C2H3 (+M)) results in a significant underprediction of the SL. Furthermore, excessive involvement of oxygenated intermediates (e.g., HOCHO) also contributes to underprediction. Based on the kinetic analysis results, this study proposes an optimized model for MD-methanol with superior performance, further validating the rigor of the kinetic analysis. In summary, this work provides crucial SL data for fatty acid methyl ester-methanol fuels and clarifies the sources of discrepancies in kinetic models, and proposes an optimized mechanism for blended fuels, laying a theoretical foundation for efficient and clean combustion.

Moisture and contamination ingress lead to a prominent failure issue of the epoxy-casting current transformer (CT)-air-insulating barrier (IB) system in 40.5 kV indoor switchgears. To address this, mechanisms of insulation failure from microscale material aging to macroscale system breakdown were investigated. Epoxy resin (EP) and sheet moulding compound (SMC) samples of CTs and IBs were prepared, and tests of surface resistivity, dielectric properties, contamination level, microscopic morphology, and molecular structure were conducted. For both CTs and IBs, the results indicate that the surface resistivity decreased by over 99.9 %, dielectric properties significantly deteriorated, and microstructural defects were observed. Raman spectra revealed breakages of C-H, C-O and C=C, ester bond hydrolysis, and existence of amorphous carbon. Primary aging mechanisms included structural degradation induced by synergistic erosion of moisture and contamination, organic material carbonization caused by discharge, and accelerated insulation failure driven by aging byproducts. Additionally, impacts of increased surface conductivity and barrier displacement on electric field strength and current density were investigated through finite element simulation. The results show that the formation of weakly insulating layers on material surfaces due to aging was essential for partial discharge initiation and elevated current density. The most probable interphase flashover path was identified as initiating from one busbar, propagating along the CT surface, then through air to the IB surface, continuing along the IB surface, then through air to another CT surface, and finally reaching another busbar, which aligned well with practical experience. The risk of interphase flashover in the CT-air-IB system increased with the increment of barrier displacement and was high when the conductivity of weakly insulating layers ranged from 0.001 to 0.01 S/m.

Liquid ammonia/diesel dual fuel direct injection (DFDI) engine has attracted significant attentions for the potential of decarbonization in the transportation sector. To address the challenges of low indicated thermal efficiency (ITE) and emission deterioration at high ammonia energy ratios (AER), this study proposes and systematically investigates the novel Intelligent Charge Compression Ignition (ICCI) combustion mode in an ammonia/diesel DFDI engine. The injection strategies of diesel and ammonia were independently investigated to elucidate their respective influence mechanisms on combustion and emission characteristics. The results show that diesel pre-injection, by establishing local reactivity stratification, significantly improves the ignition of ammonia, achieving an ammonia combustion efficiency over 95 % at the optimal pre-injection ratio. Besides, the ammonia pre-injection strategy, through concentration stratification, enables effective control over the combustion phasing, increasing the ITE to 45.9 %. With the ammonia injection timing retarding, the combustion mode transitions from homogeneous premixed charge to heterogeneous spray interaction ignition. Furthermore, the ammonia post-injection strategy is confirmed as an effective strategy for NOx controlling, achieving a 49.1 % NOx reduction through thermal NOx suppression and in-cylinder DeNOx pathways. Ultimately, the proposed ICCI strategy achieves an ITE of 49.9 % at an AER of 80 %, which surpasses the quasi-RCCI mode (39.8 %) and conventional diesel mode (47.3 %). In addition, ICCI strategy suppresses NOx emissions to a low level of 12.2 g/ kWh and obtains an ideal combustion stability with coefficient of variation below 2.5 %. This study presents a highly promising technical pathway for achieving high-efficiency, clean, and stable combustion with ammonia/ diesel dual fuels at high AERs.

Measuring the ignition temperature of micron-sized iron particles can verify the ignition mechanism and aid in designing efficient iron powder combustion devices. This study captured the diameter, morphology and ignition status of iron particles with diameter range from 17 to 45 mu m entering a stable high-temperature environment by high-speed cameras. The ignition frequency of iron particles at different ambient temperatures and oxygen concentration were recorded. Defining the ignition temperature as the ambient temperature at which the ignition frequency of iron particles exceeds 0.9, it was found that the ignition temperature of iron particles heated from room temperature and closer to a spherical shape is approximately 1140 K, while the non-spherical iron particles is around 1120 K. The ignition temperature is independent of particle diameter and ambient oxygen concentration. The theoretical method for estimating ignition temperature (X.C. Mi, A. Fujinawa, J.M. Bergthorson, 2022) aligns well with the experimental results. Theoretical analysis indicates that the oxidation mechanism at low temperatures (below 800 K) does not affect the ignition temperature, preheating does not effectively reduce the ignition temperature, and iron particles with high specific surface areas, such as sponge iron powder, exhibit significantly lower ignition temperatures. Novelty and significance statement: This study, for the first time integrates experimental investigation with theoretical models to systematically examine the ignition temperature of individual micron-sized iron particles under diverse conditions. The experimental approach allows precise in-situ characterization of particle diameter, particle morphology in different ambient oxygen concentration, providing insights into their respective effects on ignition temperature. Through comprehensive theoretical discussion and experimental validation, the ignition mechanism of iron particles is verified, offering crucial parameters for the design and optimization of efficient iron particle combustion systems.