Ambient Temperature Variations Research Articles

Analysis of reducing thermal resistance between the PCM and ambient air for improving the power generation characteristics of PCM-based thermoelectric power generators

A power generator comprising a thermoelectric device (TED) and a phase change material (PCM) allows energy harvesting from ambient temperature variations, which exist ubiquitously; thus, such a device has received considerable attention as an energy harvester that can operate at any location. We developed a model for estimating the characteristics of a power generator in ambient air, whose temperature is forcibly changed between two temperature values, such as when an air conditioner is turned on and off. We calculated the influence of latent heat and thermal conductivity of the PCM on the characteristics of power generators with various thermal resistances between the TED/PCM interface and ambient air. Latent heat and thermal conductivity of the PCM affect the amount of heat energy (Q) transfer between the ambient air and PCM and the energy conversion efficiency (ηE), respectively, where the amount of electric energy is given by Q × ηE. The increase in Q caused by an increase in the latent heat of the PCM was almost independent of the thermal resistance between the TED/PCM interface and air. However, the increase in ηE caused by an increase in the thermal conductivity of the PCM decreased as the thermal resistance between the TED/PCM interface and air increased. These results indicate that the techniques to improve the power generation characteristics by increasing the thermal conductivity of PCM, which have been frequently investigated in recent years, are effective only when the thermal resistance between the TED/PCM interface and ambient air is small.

Verified of leakage loss, birefringence, nonlinear parameters and total number of modes in silica/silica doped and plastic fibers for fiber system efficiency improvement

Abstract This paper has clarified verified of leakage loss, birefringence, nonlinear parameters and total number of modes in silica/silica doped and plastic fibers for fiber system efficiency improvement. The signal power loss is demonstrated with silica/silica doped/plastic fibers at various ambient temperatures. The estimated number of fiber modes is clarified in the core against silica/silica doped/plastic fiber core radius. The fiber birefringence is studied against silica/silica doped/plastic fiber core radius. The silica/silica doped/plastic fibers effective refractive index is clarified versus ambient temperature variations. The nonlinear parameter is demonstrated based silica doped fiber against the dopant concentration ratio with different temperature variations. The effective area cross section is studied based silica doped fiber against the dopant concentration ratio with different temperature variations. The predicted fiber loss is demonstrated against the fiber operating wavelength for silica glass fibers/silica doped fibers/plastic fibers. As well as the predicted fiber loss is clarified and studied versus fiber core radius for silica glass/silica doped/plastic fibers.

Resource Scheduling Optimization of Fresh Food Delivery Porters Considering Ambient Temperature Variations

In the context of an evolving socio-economic landscape and rising living standards, the online market for fresh products, encompassing fruits, vegetables, meats, dairy, and eggs, has seen substantial growth, necessitating sophisticated logistics for e-commerce home delivery. This study tackles the distinct challenges of fresh product delivery, which demand rigorous adherence to climate conditions and product mix during transport, significantly influencing the operational strategies and scheduling of delivery platforms. To address these challenges, a comprehensive mathematical model was developed to optimize fresh food home delivery scheduling, focusing on reducing spoilage rates and accommodating the dynamic impact of environmental temperature changes. The model posits assumptions of a consistent and ample supply of fresh goods, standard initial quality loss, and efficient porter assignment for multi-category order combinations. It introduces three objective functions, targeting the minimization of fresh food loss, maximization of customer satisfaction, and reduction of distributor costs. The efficacy of the model and its genetic-algorithm-based solution method was assessed through numerical analysis and case studies, illustrating that the model enhances delivery efficiency and service quality across varying temperature conditions. This substantiates the critical role of environmental temperature management in optimizing fresh food delivery, offering a robust framework for advancing logistical operations in the perishable goods sector and ensuring quality and efficiency in fresh food e-commerce delivery.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

TEMPERATURE ERROR OF THE MEANS OF MEASURING THE GEOMETRIC CHARACTERISTICS OF BORES OF FIREARMS: MATHEMATICAL MODEL AND QUANTITATIVE EVALUATION

The work is devoted to the construction of mathematical models and quantitative assessment of the components of the temperature error of a laser triangulation sensor for measuring the geometric characteristics of bores of firearms.
 The existence of problematic issues in the field of evaluating the metrological characteristics of laser triangulation means for technical diagnostics of firearms barrel channels is highlighted. An analysis of publications
 on the evaluation of the error of a laser triangulation sensor caused by the influence of ambient temperature is carried out, and known approaches to their mathematical modeling are considered. The need to develop mathematical models of the components of the temperature error of a laser triangulation sensor and to quantify this error is substantiated.
 The parameters of the optical scheme of the laser triangulation sensor, whose deviation from their nominal (calculated) values will affect the conversion function, are determined. It is shown that the presence of a temperature error during the processing of measurement information will be perceived as an apparent change in the measured value with a constant input signal.
 On the basis of algebraic transformations of expressions reflecting the relationship between the output value of a laser triangulation sensor and deviations of the optical circuit parameters from their nominal values, mathematical models of the temperature error components are obtained and presented in the form of error component limits at the input of the measuring instrument. The expressions for determining the confidence limits of the total temperature error, taking into account the correlation between its components, are substantiated.
 A quantitative assessment of the temperature error for the characteristic combinations of weapon parameters (for four ranges of barrel calibers) in the range of ambient temperature variations inherent in the region where measurements are made is carried out. The results of the quantitative evaluation are presented in the form of graphs of the dependence of the limits of relative and absolute temperature errors on temperature and on the increase in the bore radius in the measurement range. The data obtained were analyzed, the peculiarities of the dependencies were determined, and conclusions were drawn about the compliance of the temperature error characteristics of the measuring instrument with the requirements for its accuracy.

Thermodynamic modelling, testing and sensitive analysis of a directly pressurized hydrogen refuelling process with a compressor

This paper presents the development of a thermodynamic model for the hydrogen refuelling station (HRS) to simulate the process of refuelling, which involves the transfer of hydrogen gas from a high-pressure storage tank to the onboard tank of a fuel cell electric vehicle (FCEV). This model encompasses the fundamental elements of an HRS, which consists of a storage tank, compressor, piping system, heat exchanger, and an on-board vehicle tank. The model is implemented and validated using experimental data from SAE J2601. Various simulations are conducted to assess the impact of the Joule-Thomson effect and compression on the temperature of hydrogen flow, specifically focusing on an average pressure rate of 18 MPa/min. Furthermore, a comprehensive analysis is conducted to examine the impact of pressure variations in the storage tank (10–90 MPa) and the initial pressure within the vehicle tank (5–35 MPa), as well as variations in ambient temperature (0–40 °C). The study revealed that the energy consumption in the cooling system surpasses the average power consumption in the more advantageous scenario of 60 MPa by a range of 36% to over 220% when the pressure in the storage system drops below 30 MPa. Furthermore, it was noted that the impact of ambient temperature is comparatively less significant when compared to the initial pressure of the vehicle's tank. The impact of an ambient temperature change of 10 °C on the final temperature of a hydrogen vehicle is found to be approximately 2 °C. Similarly, a variation in the initial vehicle pressure of 10 MPa results in a modification of the final hydrogen vehicle temperature by approximately 8.5 °C.

A novel metamaterial with instantaneously sign-switchable coefficient of thermal expansion and Poisson's ratio

Most materials expand when heated and contract when cooled. In contrast, negative thermal expansion materials exhibit the opposite behavior. In this paper, we present a metamaterial that expands both when heated and cooled. The unit cell configuration is crafted by embedding two isosceles triangular structures composed of high thermal expansion materials within a re-entrant hexagonal honeycomb framework. Initially, the bases of the two isosceles triangles are in contact. The metamaterial exhibits two deformation states under different external loads. Analytical formulas for the equivalent parameters were derived based on classical beam theory within the regime of small deformation. Finite element simulations were conducted to validate the accuracy of these analytical formulas. The influence of geometric parameters on the metamaterial is discussed in detail. The results demonstrate that the metamaterial can exhibit excellent multi-functional properties within a small range of deformation, including instantaneous sign-variable coefficients of thermal expansion and Poisson's ratios, along with significantly distinct tensile and compressive stiffnesses. This metamaterial consistently exhibits expansive characteristics regardless of variations in ambient temperature. Additionally, the material always expands laterally regardless of the sign of uniaxial loads. Based on these properties, the metamaterial can be applied to fasteners in the aerospace industry that require permanent locking, ensuring stability in various temperature and mechanical load conditions.

A decoupling calculation method of separating temperature component from the nano-scale metal film thickness measurement output by the eddy current sensor

The eddy current method has been widely applied in the field of nano-scale metal film thickness detection. The output signal of an eddy current sensor is generally tiny in practice, and it is easily affected by the ambient temperature variation, which results in a decrease in the measurement accuracy. How to separate the temperature effect on film thickness measurement for achieving a high precision is a major problem. Therefore, a coupling model of an eddy current sensor with an electromagnetic field and a temperature field is established in this study, and the influence of the film thickness and temperature on coil impedance is calculated. It is found that the inductance and resistance of the coil vary monotonically as thickness and temperature with a measure of linearity in a certain range, as well as the real part and imaginary part of the output voltage by using an AC bridge. Then a film thickness-temperature decoupling calculation method is proposed, and a group of linear calibration intervals are further divided considering the linearity and measurement accuracy. According to the calculation results, it is confirmed that the method can decouple the two, and accomplish a higher accuracy. In addition, the method is verified by a series of experiments, and the variation trend of real and imaginary parts of output voltage with thickness and temperature is consistent with simulation results. At the same time, it is feasible to realize a synchronous detection of metal film thickness and ambient temperature in a certain range.

Reduced OFF-state current and suppressed ambipolarity in a dopingless vertical TFET with dual-drain for high-frequency circuit applications

In this article, a dopingless TFET with dual-drain and reversed T-shaped channel (DLVIT-TFET) is investigated to offer improvement in ON-state current (IOn) along with the reduction in OFF-state leakage current (IOff) and in ambipolarity. The investigated device has a dual channel region which helps in improving the on-state parameters like IOn and subthreshold swing (SS) due to the extended tunneling area. The inverted T-shaped channel minimizes the charge carriers to tunnel during the off-state, thereby reducing the magnitude of IOff. The results obtained from 2D TCAD simulation show that the investigated device provides ∼1 order of improvement in IOn and ∼3 orders of improvement in IOff. Furthermore, DLVIT-TFET offers an ∼3 orders of improvement in ambipolar current (IAmb) due to a reduction in peak electric field at channel-drain interface caused by splitting of drain potential at ambipolar state. Moreover, various analog/RF parameters such as transconductance (gm), gate parasitic capacitance (Cgd, and Cgs), cut-off frequency (fTmax), gain-bandwidth product (GBP), transconductance-frequency product (TFP) and transit time (τ) are found to be improved in DLVIT-TFET compared to the conventional DL-TFET. In addition, the transient analysis of DLVIT-TFET based inverter is analyzed, which shows improvement in the parameters like propagation delay and voltage swing. Finally, reliability of the investigated device is discussed by considering the effect of interface trap charges (ITCs) and variation in ambient temperature and it is observed that DLVIT-TFET is more immune to such defects and environmental factors than the conventional one.

Temperature compensation for piezoresistive pressure sensor based on deep learning on graphs

With the escalating demand for precision in piezoresistive pressure sensors to cater to a wide spectrum of applications, a significant challenge emerges due to the material properties of these sensors, which induce a substantial temperature coefficient. This limitation restricts the operational temperature range and is further exacerbated by ambient temperature variations, resulting in pronounced nonlinearity in sensor responses. To address these challenges, A new method using Graph Neural Networks (GNNs) is introduced to address nonlinearity and temperature drift in piezoresistive pressure sensors. GNNs improve the sensors’ accuracy and reliability across different temperatures, expanding their applicability. Test results show a notable accuracy enhancement, with a maximum full-scale error below 0.05% and even lower in specific ranges (under 0.04%). This precision makes the method ideal for industrial pressure sensor applications and production, offering significant benefits.

A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis.

Because the conventional Temperature Drift Error (TDE) estimation model for Capacitive MEMS Gyros (CMGs) has inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification to improve their bias stability, its novel model based on thermal stress deformation analysis is presented. Firstly, the TDE of the CMG is traced precisely by analyzing its structural deformation under thermal stress, and more key decisive TCQs are explored, including ambient temperature variation ∆T and its square ∆T2, as well its square root ∆T1/2; then, a novel TDE estimation model is established. Secondly, a Radial Basis Function Neural Network (RBFNN) is applied to identify its parameter accurately, which eliminates local optimums of the conventional model based on a Back-Propagation Neural Network (BPNN) to improve bias stability. By analyzing heat conduction between CMGs and the thermal chamber with heat flux analysis, proper temperature control intervals and reasonable temperature control periods are obtained to form a TDE precise test method to avoid time-consuming and expensive experiments. The novel model is implemented with an adequate TCQ and RBFNN, and the Mean Square Deviation (MSD) is introduced to evaluate its performance. Finally, the conventional model and novel model are compared with bias stability. Compared with the conventional model, the novel one improves CMG's bias stability by 15% evenly. It estimates TDE more precisely to decouple Si-based materials' temperature dependence effectively, and CMG's environmental adaptability is enhanced to widen its application under complex conditions.

Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies

This article addresses challenges in the design of underground high-voltage transmission lines, focusing on thermal management and cable ampacity determination. It introduces an innovative proposal that adjusts the dimensions of the backfill to enhance ampacity, contrasting with the conventional approach of increasing the core cable’s cross-sectional area. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization and restart strategies, implemented in MATLAB for parameter autoadaptation. The article emphasizes more efficient solutions than traditional PSO, showcasing improved convergence and precise results (success probability of 66.1%). While traditional PSO is 81% faster, the proposed PSO stands out for its accuracy. The inclusion of thermal backfill results in an 18.45% increase in cable ampacity, considering variations in soil thermal resistivity, backfill properties, and ambient temperature. Additionally, a sensitivity analysis was conducted, revealing conservative values that support the proposal’s robustness. This approach emerges as a crucial tool for underground installation, contributing to continuous ampacity improvement and highlighting its impact on decision making in energy systems.

Trend Decomposition for Temperature Compensation in a Radar-Based Structural Health Monitoring System of Wind Turbine Blades

The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods.

Structural damage identification under ambient temperature variations based on CNN and normalized modal flexibility-autoregressive coefficients hybrid index

This study proposes a novel method to identify structural damage considering ambient temperature variations. In this method, the normalized modal flexibility based index (nMFBI) and autoregressive (AR) coefficients are combined to form the nMFBI-AR hybrid index, and convolutional neural networks (CNN) are exploited to locate and quantify the damage. Moreover, the effects of ambient temperature variations and measurement noise are not considered in the training dataset, which is preferable in practical engineering. To verify the effectiveness of the proposed method, firstly, a numerical structure of a simply supported beam is investigated, and the performance of the nMFBI-AR index is evaluated by making a comparison with the nMFBI and AR indexes. Then, the proposed method is further verified by a test model of a three-story frame and a practical engineering example of a continuous rigid frame bridge. The results demonstrate that although the influence of ambient temperature variations and measurement noise are only considered in the test datasets, this approach has the best performance in locating and quantifying structural damage, and the errors are less than 16%, which is promising. In addition, this paper provides a guideline and a new idea for the study of damage index based on time-frequency hybrid information.

A conceptual model of polyoxymethylene dimethyl ether 3 (PODE3) spray combustion under compression ignition engine-like conditions

As one of the most popular synthetic fuels (E-fuels), Polyoxymethylene dimethyl ethers(PODEX) have garnered significant attention among researchers in the compression ignition engine field. This work aims to provide an exhaustive analysis of the combustion characteristics of PODE3, which typically constitutes the primary component of PODEX. This research seeks to deepen our understanding of the flame structure of PODE3, particularly in relation to ambient temperature variation, and unravel the underlying mechanisms driving the observed trends. To achieve this aim, a range of optical techniques was employed within a high-temperature high-pressure combustion vessel to quantify various general combustion parameters and assess OH* chemiluminescence, formaldehyde, and soot distribution. Two additional reference fuels, pure n-dodecane and a blend of 50 % vol n-dodecane and 50 % vol PODE3 were also tested. Additionally, some analysis from chemical kinetics and 1D spray model calculations were used to substantiate the experimental observations. The findings reveal that the flame structure of PODE3 differs significantly from traditional diffusion flames associated with diesel-like sprays. The flame lift-off length (LOL) of PODE3 aligns closely with that of n-dodecane, and maintains the two-lobe structure under high-temperature conditions. In contrast, under low ambient temperatures, the LOL of PODE3 significantly extends, accompanied by a concentration of OH* radicals at the spray center. The notable sensitivity of PODE3 flames to temperature variation is related to the chemical kinetics of formaldehyde and CO. As a result of the analysis, conceptual models of PODE3 flames under varying temperatures were proposed in the end.

Temperature drift suppression for micro‐electro‐mechanical system gyroscope based on vibrational‐displacement control with harmonic amplitude ratio

AbstractMicro‐electro‐mechanical system gyroscope detection output is susceptible to drift due to ambient temperature variations. Furthermore, the drive‐mode vibration amplitude is affected by the signal pickup circuit and structural parameters, which significantly influence the temperature drift of the bias and scale factor (SF). This study proposes a vibration amplitude control method based on harmonic amplitude sideband‐ratio (SBR), namely SBR‐AGC, which characterizes the vibration amplitude using the harmonic amplitude ratio of the vibrational electrical signal. The constancy of vibration amplitude is maintained via closed‐loop control to suppress bias and SF temperature drift. The experimental results on cobweb‐like disk resonator gyroscope reveal that the temperature coefficient of bias in the SBR‐AGC mode lies within −20 to 60°C and decreases by 36%, and the temperature coefficient of the SF lies within −10 to 50°C and decreases by 49.7%. Therefore, the environmental adaptability of the gyroscope is effectively improved.

High performance efficiency of optical networking infrastructure characteristics with maximum speed of commercial fiber optic line cables employment

Abstract Fiber optic communications have made great strides, and it is now the foundation of our telecommunications and data networking infrastructures. Due to its beneficial characteristics, including low attenuation, a large bandwidth, and immunity to electromagnetic interference, optical fiber is frequently acknowledged as a superior transmission medium. Optical fibers have been widely used to create high speed lines that can transmit either a single wavelength channel or many wavelength channels using wavelength division multiplexing due to their special features (WDM). The maximum speed of commercial fiber-optic cables was only 2.5 Gb/s and can be extended to 1 Tb/s for short and medium distance. The study clarifies the possible transmission distance for different transmission media silica/plastic optical fibers with various optical fiber window transmissions versus environmental ambient temperature variations. The study demonstrated the bad effects of the temperature effects on the optical fiber transmission media (silica/plastic fibers) for various transmission windows.

Ambient temperature variations and AIDS-related mortality: A time-stratified case-crossover study in 103 counties, China

BackgroundClimate change, characterized by the steady ascent of global temperatures and the escalating unpredictability of climate patterns, poses multifaceted challenges to public health worldwide. However, vulnerable groups, particularly the population affected by HIV/AIDS, have received little attention. ObjectivesWe aimed to examine the impacts of temperature variations on AIDS-related mortality. MethodsData on individuals with HIV/AIDS were collected from the HIV/AIDS Comprehensive Response Information Management System between 2013 and 2019. Temperature variation metrics were constructed by diurnal temperature range (DTR), temperature changes between neighboring days (TCN), and temperature variability (TV0-t). Time-stratified case-crossover design with conditional logistic regression models was used to investigate the associations between ambient temperature variations and AIDS-related mortality. ResultsEach 1 °C elevated in DTR was linked with a 5.28 % [95 % confidence intervals (CIs): 1.61, 9.08] increment in AIDS-related mortality at a lag of 0–6 days. Stronger associations between DTR and AIDS-related mortality were observed in the married than in single, with corresponding excess ORs (%) of 5.33 (95 % CIs: 0.29, 10.62) versus 4.79 (95 % CIs: −0.50, 10.36) for 1 °C increased in DTR at lag 0–6 days. Additionally, we noticed the impact of DTR was more pronounced in the warm season, leading to a 7.32 % (95 % CIs: 0.57, 14.51) elevation in the risks of AIDS-related mortality for 1 °C increase in DTR at lag 0–6 days, while the effect value decreased to 5.16 % (95 % CIs: 0.71, 9.81) in the cold season. ConclusionsOur findings indicated that DTR might be a significant risk factor for AIDS-related deaths among ambient temperature variation indicators, and underscored the importance of considering temperature variability in public health interventions aimed at mitigating this risk of AIDS-related mortality.

Does the expensive brain hypothesis apply to amphibians and reptiles?

Vertebrate brains show extensive variation in relative size. The expensive brain hypothesis argues that one important source of this variation is linked to a species’ ability to generate the energy required to sustain the brain, especially during periods of unavoidable food scarcity. Here we ask whether this hypothesis, tested so far in endothermic vertebrates, also applies to ectotherms, where ambient temperature is an additional major aspect of energy balance. Phylogenetic comparative analyses of reptiles and amphibians support the hypothesis. First, relative brain size increases with higher body temperature in those species active during the day that can gain free energy by basking. Second, relative brain size is smaller among nocturnal species, which generally face less favorable energy budgets, especially when maintaining high body temperature. However, we do not find an effect of seasonal variation in ambient temperature or food on brain size, unlike in endotherms. We conclude that the factors affecting energy balance in ectotherms and endotherms are overlapping but not identical. We therefore discuss the idea that when body temperatures are seasonally very low, cognitive benefits may be thwarted and selection on larger brain size may be rare. Indeed, mammalian hibernators may show similarities to ectotherms.

Effect of Mn on the ZnBiMnO varistor ceramics sintered at a low-temperature of 875 °C

The effect of Mn on ZnO–Bi2O3 (ZnBiO) based varistor ceramics sintered below 900 °C has not been fully understood due to the conventional ZnBiO-based varistor ceramics usually being sintered at temperatures higher than 900 °C. Therefore, (99.5-x) ZnO+0.5Bi2O3+xMnCO3 (x = 0, 1, 2, and 3; all in mole ratio) ceramics were prepared by solid-state sintering at 875 °C for 3 h to study the effect of Mn on the microstructure, electrical nonlinear properties and their stability within the range of 25–100 °C. The results show that doping MnCO3 results in the formation of Mn3O4 and Bi12MnO20 secondary phases within the sintered samples. Mn3O4 mainly exists at grain boundary as particle phase, restricting the growth of ZnO grains during sintering. The presence of Bi12MnO20 indicates MnCO3 may enhance the sintering of ZnBiMnO ceramics by increasing the amount of Bi-rich liquid phase. MnCO3 effectively improves the electrical nonlinear properties of ZnBiMnO varistor ceramic and their stability against the variations in ambient temperature. The 2 mol% MnCO3 doped ZnBiMnO varistor ceramic shows the best electrical nonlinearity, with a nonlinear coefficient, a breakdown voltage and a leakage current density of 53.61, 503.11 V/mm, and 2.85 μA/cm2 respectively. This study may improve our understanding of the effect of Mn on the low-temperature sintered ZnBiO based varistor ceramics, and provide a promising candidate material for manufacturing low-cost ZnO based varistor.