High Load Conditions Research Articles

REVEALING ANOMALIES BY NETWORK PACKET FLOODING ON BUILT FTP AND OPENSSH SERVERS IN CONTROLLED LAB ENVIRONMENT

This scientific paper investigates the effects of network packet flooding on FTP (port 21) and SSH (port 22) protocols, aiming to reveal and document anomalies in server behavior under high-load conditions. By simulating packet flooding in a controlled lab environment, an analysis on vulnerabilities and anomalies unique to each protocol is conducted in order to the improve defensive capabilities. The results provide guidance on best practices to secure FTP and OpenSSH services against malicious traffic, such as Distributed Denial of Service (DDoS) attacks, supporting wider network security strategies.

Effects of the Interaction Between Time-on-Task and Task Load on Response Lapses

To investigate the interaction effects of prolonged working periods and different task loads on response lapses, focusing on the mechanisms of delayed responses and error lapses. Professionals such as pilots, truck drivers, and nurses often face extended work hours and fluctuating task loads. While these factors individually affect performance, their interaction and its impact on response lapses remain unclear. Twenty participants completed the Uchida–Kraepelin (U–K) Psychological Test and a dual-task version with functional near-infrared spectroscopy. Independent variables were time-on-task and task load. Dependent variables included measures of fatigue, arousal, workload, task performance (delayed and error rates), and brain functional connectivity. Both time-on-task and task load significantly affected cerebral connectivity, response lapses, workload (frustration level), fatigue, and arousal. Arousal levels significantly decreased and reaction times increased after 60 min of work. Cognitive resource regulation became challenging after 90 min under high task load levels. A decline in the connection between the prefrontal and occipital cortex during high-load tasks was observed. The findings provide insight into the mechanisms of response lapses under different task load levels and can inform strategies to mitigate these lapses during extended work periods. This study’s findings can be applied to improve work schedules and fatigue management in industries like aviation, transportation, and healthcare, helping reduce response lapses and errors during extended work periods under high task load conditions.

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Energy-exergy analyses of hydrogen enriched⸺ diesel and blended biofuel (Azolla pinnata macroalgae) on the dual fuel diesel engine

Hydrogen fuel has gained global attention as a potential alternative to conventional fuels as it reduces greenhouse gas emissions and addresses energy sustainability challenges. In addition, biofuel (Azolla pinnata) is highly productive having limited negative impacts with an exemplary productivity rate. The energy-exergy analysis in CI engine's is an effective method for determining its efficiency and effectiveness. This paper highlights the effects of diesel, H2 enriched diesel, and H2 enriched B40 (60% diesel + 40% Algal Biodiesel) on the energetic and exergetic efficiency of a dual-fuel diesel engine with a rated speed and power of 1500 rpm and 3.5 kW respectively. The analysis was done at varying engine loads: low loads (25%), medium loads (50%), and high loads (75%), respectively. The energy and exergy for 40% hydrogen-enriched B40 increased by 8.77% and 23.85%, respectively, compared to neat diesel operation at higher loads. In addition, the irreversibility of 40% hydrogen-enriched B40 decreased by 13.82% at higher load conditions as compared to base diesel operation. The sustainability index of 40% hydrogen-enriched B40 was 1.38 compared to 1.28 of diesel, showing an improvement of 7.82%. The theoretical energy-exergy analyses showed an error less than 5% compared to BTE validating the results of the energy-exergy analyses performed theoretically.

Ground motion characteristics induced by high-magnitude events with different source mechanisms of longwall mining

Rockbursts have become one of the most serious hazards in underground mines worldwide. While the characteristics of ground motions before rockbursts (or high-magnitude events (HMEs)) have not been studied. In this study, the peak ground velocity (PGV) and cumulative absolute displacement (CAD) before HMEs were examined based on seismic monitoring in a coal mine. It’s found that the source mechanisms vary significantly across different zones of the longwall, attributed to the low correlation between rock fracture types, stress levels, and seismic intensity. This correlation is mainly related to the distribution of primary fractures within the rock mass. The non-overlap of CAD peak with HME and the induction of delayed rockbursts are mainly caused by the unavoidable creep and additional energy input into the coal mass under disturbances before instability. The energy conversion process of the entire system is determined to be controlled by the stiffness ratio between the roof-floor system and the loaded coal, in accordance with the concept of dynamic and static load superposition. A high static load, strong dynamic disturbances, and a low rigidity ratio between surrounding rock and coal are identified as the most hazardous factors.

Measurement of dynamic deformation during shock loading using a fiber-optic loop-sensor

Abstract Characterizing materials under shock loading has been of interest in fields such as protective material development, biomechanics to study the injury mechanics and high-speed aerodynamic structures. However, shock loading of material is a very short duration phenomenon and it is extremely challenging to develop sensors for dynamic measurements under such loading conditions. Optical fiber sensors that present the possibilities to allow high resolution measurement of force or displacement in such high strain rate loading conditions. This work studies the possibility of developing a single mode optical fiber sensor based on the principle of power losses from the curved section for dynamic measurements under shock loading conditions. The strain measurement results obtained from the optical sensors are validated with the controlled digital image correlation measurements are found to be in close correlation. The sensor was able to provide time sensitivity of better than 1 ms. The results show that the fiber sensor has the characteristics of high sensitivity and high precision, which can be used to measure fine vibration in real-time.

Exploring the hardness-independent wear behavior of typical wear-resistant materials under dynamic and static conditions

In this study, three types of wear-resistant materials including high-chromium cast iron (HCCI), medium-chromium cast steel (MCCS), and low-chromium cast steel (LCCS), with similar macroscopic hardness but distinct microstructures and impact toughness have been prepared. The wear behavior of these three materials under different loads were investigated by impact wear experiments under dynamic load and three-body wear experiments under static load. The results revealed that the almost fully martensitised MCCS had the least mass loss under low dynamic impact load wear conditions. The brittleness associated with large carbides intensified with increasing impact load, culminating in a significant reduction in the impact wear resistance of HCCI. The residual austenite rapidly transformed into martensite, thereby effectively improving the wear resistance of the LCCS. Comparatively, the experimental steels were all characterized by fatigue wear of the abrasive on the material surface under static wear conditions at low loads and showed similar wear resistance. Conversely, the large carbides demonstrated superior wear resistance under conditions of high static load, and HCCI had the best wear resistance.

Working memory load increases movement-related alpha and beta desynchronization

Working memory (WM) load has been well-documented to impair selective attention and inhibitory control. However, its effects on motor function remain insufficiently explored. To extend the existing literature, we investigated the impact of WM load on force control and movement-related brain activity. Sixteen healthy young participants performed a visual static force matching task using a pinch grip under varying WM loads. The task included low and high WM load conditions (memorizing one digit or six digits), and the precision level required to control force was adjusted by manipulating visual gain (low vs. high visual gains), with higher visual gain necessitating more precise force control. Peri-movement alpha and beta event-related desynchronization (ERD), along with force accuracy and steadiness, were measured using electroencephalography recorded over the central areas during the force control task. Results indicated that while force accuracy and steadiness significantly improved with higher visual gain, there was no significant effect of WM load on these measures. Alpha and beta ERD were greater under high than low visual gain, and also greater under high than low WM load. These findings suggest that in young adults, increased WM load leads to compensatory increases in sensorimotor cortical activity to mitigate potential declines in static force control performance that may result from the depletion of neural resources caused by WM load. Our findings extend current understanding of the interaction between WM and sensorimotor processes by offering new insights into how movement-related brain activity is influenced by heightened WM load.

Exploring the benefits of hydrogen-water injection technology in internal combustion engines: A rigorous experimental study

Previous hydrogen researchers have demonstrated the great potential of hydrogen as a zero-carbon fuel and its wider operational ability to directly replace the existing fossil-fuelled internal combustion engines (ICE). Hydrogen direct-injection technology can operate at stoichiometric combustion conditions, fully eliminating the intake backfire phenomena, which has the benefit of low airflow requirements and is a suitable solution for naturally aspirated ICE platforms. However, significant challenges must be addressed when operating an H2 ICE at or near stoichiometric. These include rapid pressure rise rate, high in-cylinder gas pressure and temperature and the consequential higher engine-out NOx emissions. This study provides a comprehensive experimental analysis of the effect of water injection on a light-duty H2 engine’s performance, efficiency, burn durations and NOx emission. The engine was modified to operate with hydrogen via a centrally-mounted direct H2 injector and an intake port-mounted water injection system. The study included water vapour measurements, NOx concentrations and other exhaust gases. The practical implications of these findings are significant. The results demonstrated that the NOx emissions were reduced by 79% when the water was injected at a rate of 5 kg/h at 10 bar IMEP and 2000 rpm. This reduction in emissions, coupled with the observed decrease in cylinder pressure and the rate of pressure rise, could substantially improve engine performance and efficiency. At higher load regions, the water injection extended the maximum engine load to 24 bar IMEP and increased the engine torque output by 10%. The maximum Indicated Thermal Efficiency (ITE) increased from 41% at 16.5 bar IMEP to 42.5% at 18 bar IMEP at a lower lambda, with a corresponding reduction in the intake-air-boost pressure requirement. Of particular note, the use of water injection decreased the engine-out NOx emissions under high-load conditions by more than 55%, even at richer AFR compared to pure hydrogen operation.

Stability‐Enhanced Variable Stiffness Metamaterial with Controllable Force‐Transferring Path

AbstractSelf‐contact variable stiffness (SVS) metamaterial offers specific patterns of elastic strain energy storage by changing its force‐transferring path. The change of the elastic strain energy storage patterns further influences the stiffness variation of the SVS metamaterials. However, challenges still arise because typically fabricated SVS metamaterials inevitably contain structural irregularities, eccentricities, and defects. These microstructural imperfections impede the stable generation of designed strain energy storage patterns in the metamaterial, implying that the SVS metamaterials cannot achieve expected stiffness variations. To address this burning question, an SVS metamaterial/meta‐structure design paradigm with eccentric unit designs is proposed, which can realize both stable stiffness variations and high load‐bearing. The theoretical, simulation, and experimental results demonstrate that the SVS chain meta‐structure achieves ≈110 times stiffness variation between the low and high loading conditions. Its load‐bearing can reach more than 1500 N when the meta‐structure falls into the high stiffness stage. Furthermore, some typical functions are demonstrated using the stability‐enhanced SVS metamaterial, including customized mechanical protections for human joints and information transformation regulated by remote mechanical input signals. The results can provide a useful solution for generating a more feasible stability‐enhanced SVS metamaterial/meta‐structure and promote their potential applications in aerospace, medical devices, and robotics.

Developing Rapid-Charging Li-S Batteries via "Target Catalysis" of Cations and Anions Modified Electrocatalyst.

The shuttle effect and sluggish sulfur reduction reaction have resulted in significantly low efficiency and poor high current cycling stability in lithium-sulfur batteries, impeding their practical applications. To address these challenges, the introduction of Ni cations into MoS2 grown on reduced graphene oxide (MoS2/rGO) induces the formation of impurity energy levels between the conduction and valence bands of MoS2. Additionally, the introduction of anionic Se expands the interlayer spacing, enhances intrinsic conductivity, and improves ion diffusion rates. Simultaneously introducing anionic and cationic species into the MoS2/rGO causes the center of the d-band to shift upward, reducing the occupancy of electrons in antibonding orbitals. This modification leads to a rearrangement of the electronic structure of Mo, accelerating the redox reactions of lithium polysulfides. It particularly enhances the binding energy and lowers the conversion energy barrier of Li2S4. Consequently, the Li||S coin cell with the Ni-MoSSe/rGO cathode demonstrates an initial capacity of 446 mAh g-1 at 20 C, with a remarkable capacity retention of ≈96.7% after 200 cycles. Moreover, even under high sulfur loading conditions (6.45 mg cm-2) and a low electrolyte/sulfur ratio (5.4 µL mg-2), it maintains a high areal capacity of 6.42 mAh cm-2.

Efficient Synthesis of Flower-Ball Structured CuFeS2 as Advanced Anode Material for Lithium-Ion Batteries Across Wide Temperature Ranges.

Transition metal sulfides stand as potential anode candidates for lithium-ion batteries offering high capacity, redox reversibility, and safety. However, cycling-induced volume variations and slow kinetics hinder their application. Here, CuFeS2 with a flower-ball nanosheet structure is synthesized via a hydrothermal method, enhancing electrolyte infiltration, Li+ transport, and cycle life. CuFeS2 exhibits a large initial discharge specific capacity of 532.4 mAh g-1 at 500 mA g-1 with 95.7% initial Coulombic efficiency, retaining an impressive 90.5% (481.6 mAh g-1) of its initial capacity after 300 cycles. Remarkably, at 2000 mA g-1 for 700 cycles, it maintains a high specific capacity of 487.1 mAh g-1 with an 89.4% capacity retention rate. Moreover, it maintains excellent reversibility at both high temperature (60 °C) and low temperature (-25 °C) and demonstrates excellent electrochemical performance even under high loading conditions. Consequently, CuFeS2 holds immense promise as a lithium-ion battery anode material, offering fast charging, safety, high capacity, and long life.

Performance Evaluation of Routing Algorithm in Satellite Self-Organizing Network on OMNeT++ Platform

Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with Dijkstra and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads.

Pre-choice midbrain fluctuations affect self-control in food choice: A functional magnetic resonance imaging (fMRI) study.

Recent studies have shown that spontaneous pre-stimulus fluctuations in brain activity affect higher-order cognitive processes, including risky decision-making, cognitive flexibility, and aesthetic judgments. However, there is currently no direct evidence to suggest that pre-choice activity influences value-based decisions that require self-control. We examined the impact of fluctuations in pre-choice activity in key regions of the reward system on self-control in food choice. In the functional magnetic resonance imaging (fMRI) scanner, 49 participants made 120 food choices that required self-control in high and low working memory load conditions. The task was designed to ensure that participants were cognitively engaged and not thinking about upcoming choices. We defined self-control success as choosing a food item that was healthier over one that was tastier. The brain regions of interest (ROIs) were the ventral tegmental area (VTA), putamen, nucleus accumbens (NAc), and caudate nucleus. For each participant and condition, we calculated the mean activity in the 3-s interval preceding the presentation of food stimuli in successful and failed self-control trials. These activities were then used as predictors of self-control success in a fixed-effects logistic regression model. The results indicate that increased pre-choice VTA activity was linked to a higher probability of self-control success in a subsequent food-choice task within the low-load condition, but not in the high-load condition. We posit that pre-choice fluctuations in VTA activity change the reference point for immediate (taste) reward evaluation, which may explain our finding. This suggests that the neural context of decisions may be a key factor influencing human behavior.

Effects of charge motion on knock and thermal efficiency under high load condition of a heavy-duty natural gas engine

Nowadays, the carbon-neutral trend and the crude oil crisis have brought natural gas into increasing focus. However, heavy-duty engines fueled by natural gas have been plagued by thermal efficiency and knocking limitation problems. Well-structured charge motions can enhance thermal efficiency and concurrently mitigate knock, however, there is a lack of comprehensive work involving charge motions. In this paper, numerical simulation is employed to investigate the impact of charge motion on knock and thermal efficiency under high load conditions for a heavy-duty spark-ignition natural gas engine. The findings indicate that in the configuration with intense swirl, the diminished turbulent kinetic energy (TKE) at the combustion chamber’s base lessens the convective heat exchange between the fresh charge and the heated exhaust gases, resulting in a noticeable high-temperature zone and triggering hot spots. In configurations with inclined swirl and strong tumble motion, the extensive tumble motion near the spark plug region close to top dead center leads to an asymmetric flame front shape, determining the location of hotspots. The research also revealed that excessive tumbling motion and TKE can increase the likelihood of engine knock, while appropriate cylinder charge motion can improve the knock resistance of the engine to some extent, the maximum reduction in knocking intensity can reach 92.5%. The optimized combustion systems can significantly improve the initial combustion rate, shorten the ignition delay period, and advance the combustion center. With the combined effects of ignition timing and charge motion, the total indicated specific power per unit of fuel mass is increased in the optimized systems. Compared to the original combustion system, the indicated thermal efficiency within the knocking boundary can be improved by 1.11%.

Study on the sliding tribological behavior of ZIF-8 under lubricating grease conditions

ZIF-8 is widely applied in lubrication, adsorption, and catalysis owing to its unique physicochemical properties. Previous experimental studies have demonstrated its feasibility as a lubricant additive. In the present work, the lubricating performance of ZIF-8 as an additive to lithium-based grease is quantitatively and dynamically analyzed at the atomic scale using molecular dynamics simulations. Friction wear experiments are also conducted to elucidate the lubrication mechanism of ZIF-8. The simulation and experimental results indicate that the incorporation of ZIF-8 effectively enhances the antifriction and antiwear characteristics of lithium grease. The most significant improvement in the lubrication performance of the grease is obtained at a mass fraction of 2.0 wt. % ZIF-8, which reduces the friction factor μ of the grease by about 17.0% and the wear by ∼40.0%. Furthermore, the molecular dynamics simulations reveal that ZIF-8 primarily functions as a ball bearing under low-load conditions. However, under high-load conditions, ZIF-8 undergoes significant deformation and primarily acts as a filler. This explains the experimentally observed significant reduction in friction coefficient after the addition of ZIF-8. The results of this study provide a theoretical foundation for the development of new environmentally friendly grease additives.

Experimental and simulation study on lean combustion characteristics of passive pre-combustion chamber ignition boosted GDI engine

In order to verify the potential application of passive pre-combustion chamber technology in commercial GDI engines, the efficiency characteristics, combustion characteristics (CA50, Knock Index and COV) and emission characteristics of a pre-combustion chamber ignition engine in a boosted environment were studied by three-stage injection strategy to form a lean burn environment (λ = 1.3). A 3D simulation model of pre-combustion chamber ignition engine is established. Combined with the engine bench test results, the process of intake, mixture formation, combustion and emission generation under different working conditions is simulated by simulation method. The development process of TKE (turbulent kinetic energy), air-fuel ratio, temperature and combustion emissions in the main/pre-combustor is studied. It is found that under high load conditions in boosted pre-combustion chamber engine, the average knock index can be controlled between 2 and 3.4 bar, and the COV can be controlled between 3% and 3.5%. The indicated thermal efficiency reaches over 40% within the load range of IMEP from 10 to 14 bar. When the load increases, NOx and soot emissions show a downward trend, the HC and CO emissions slight increase. The pre-combustion chamber engine exhibits a tumble intake pattern around the cylinder axis perpendicular to the cylinder axis. The TKE reaches its maximum value 20 CAD before the top dead center and then rapidly decreases. The use of a three-injection strategy can form a uniform stratified mixture at the ignition moment. The fuel air equivalence ratio in the ignition area of the pre-combustion chamber is 0.8–0.9, slightly higher than the average fuel air equivalence ratio of 0.75.

Electrophysiological and hemodynamic mechanisms underlying load modulations in visuospatial working memory: A functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) study.

The n-back task has been widely used to study working memory. Previous studies investigating the electrophysiological (electroencephalogram [EEG]) and hemodynamic correlates (functional near-infrared spectroscopy [fNIRS]) of the n-back task have been generally based on verbal stimuli and only investigated EEG frequency bands. We simultaneously acquired the EEG and fNIRS in 35 participants (16 males; age = 26.4 ± 4.3 years; educational attainment = 18 ± 2 years) during a visuospatial n-back task. The task encompassed a control condition and a low (requiring to recall one previous stimulus) and a high (requiring to recall two previous stimuli) working memory load experimental conditions. Accuracy decreased and reaction times slowed in the high compared to both low load and control conditions. Regarding EEG, P3a showed higher amplitude in the experimental conditions compared to the control one, and P3b exhibited higher amplitude in the low compared to the high load condition. Regarding fNIRS, the high load condition showed higher deoxygenated hemoglobin compared to the control one. Moreover, the central frontopolar cortex showed higher activation compared with the left frontal cortex. Our study showed that working memory load during a visuospatial n-back task influenced behavioral and electrophysiological indices. Even if the load effect was only observed for deoxygenated hemoglobin on hemodynamic data, this was in line with previous studies and coherent with its electrophysiological correlates. Thus, our study confirms that EEG and fNIRS can be successfully used in multimodal acquisitions, but also highlights that future studies are needed to develop a novel version of the task. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Spatial structure design of interlayer for advanced lithium–sulfur batteries

The practical application of lithium-sulfur (Li–S) batteries has been hindered by the lithium polysulfide shuttle effect. An effective way to solve this problem is to utilize interlayer engineering to confine polysulfides and promote their catalytic conversion. From a spatial perspective, we designed a carbon nanofiber conductive layer (CNF, without Sn content, labeled as 0) and two Sn-doped carbon nanofiber catalytic layers (SCNF, with 10 wt% and 20 wt% Sn content, labeled as 1 and 2, respectively) with different contents of catalyst content, and verified an efficient interlayer structure by adjusting the order of preferential contact between the conductive layer and the catalytic layer with the sulfur cathode to form a hierarchical system for the inhibition and conversion of lithium polysulfide. Electrochemical measurements show that different spatial configurations have significant discrepancies on the electrochemical performance of Li–S batteries. Thus, the space configuration of 210 enables the Li–S battery to provide a specific capacity of up to 1088 mAh g−1 after 100 cycles at 0.2C. Even under the harsh conditions of high sulfur loading (5.6 mg cm−2) and lean electrolyte (E/S = 10 μL mg−1), the Li–S battery was able to cycle stably for 94 cycles at 0.2C with 87 % capacity retention. This study provides a novel spatial strategy for advancing the spatial design of high-performance Li–S batteries.

Impact of cold source temperature on transcritical CO2 power cycle: Design point optimization and off-design performance analysis

The transcritical CO2 (T-CO2) power cycle is regarded as one of the most promising alternatives to the steam power cycle in nuclear power plants. However, these plants often operate with transient cold source conditions, leading to deviations from the optimal design point and performance degradation. Therefore, this study examines a double recompression T-CO2 power cycle to optimize the design point pump inlet temperature and investigate part-load performance under cold source fluctuations. Results indicate that the lowest design point pump inlet temperature is optimal for low temperature cold sources and high load conditions to maximize thermal efficiency. When the system operates at 80% load with a cold source temperature of 5oC, the thermal efficiencies at the design point pump inlet temperature of 15oC and 25oC are 58% and 56.93%, respectively. In contrast, a moderate design point pump inlet temperature is better suited for accommodating wide fluctuations in operating conditions. A lower cold source temperature helps mitigate the reduction in thermal efficiency caused by load decreases. Furthermore, reducing the split ratio of low-temperature compressor can enhance thermal efficiency as the cold source temperature increases. These findings provide an effective approach to minimizing performance degradation in the T-CO2 power cycle under off-design conditions.