Humidity Fields Research Articles

Global Observing System Experiments within the Météo-France 4D-Var Data Assimilation System

Abstract Observing system experiments were undertaken within the 4D-Var data assimilation of the Météo-France global numerical weather prediction (NWP) model. A 6-month period was chosen (October 2019–March 2020) where 40 million observations per day were assimilated. The importance of in situ observations provided by aircraft, radiosondes, and surface weather stations, despite their small fractional amount (7%), has been confirmed particularly in the Northern Hemisphere. Moreover, the largest impact over Europe in terms of root-mean-square error (RMSE) scores comes from surface observations. Satellite data play a dominant role over tropical regions and the Southern Hemisphere. Microwave radiances have a more pronounced impact on the long range and on the humidity field than infrared radiances, despite being less numerous (10% versus 80%). Bending angles impact significantly the quality of the upper-troposphere–lower-stratosphere temperature of the tropics and Southern Hemisphere. Atmospheric motion vectors (AMVs) are beneficial in wind forecasts at low and high levels in the tropics and the Southern Hemisphere, but also in the humidity field. Such impacts are only significant during the first 48 h of the forecasts. Scatterometer winds have an impact restricted to low levels that is kept at longer ranges. A comparison with forecast sensitivity–observation impact studies over a 3-month period using the same measure of short-range (24-h) forecast errors reveals that the ranking between the major observing systems is kept between these two ways of measuring observation impact in NWP. From our conclusions, recommendations are provided on possible evolutions of the global observing system for NWP.

A Novel Capacitive Low-Humidity Sensor Based on a Double-Sided Structure

The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> -Al2O3 sensor has low sensitivity, and its capacitance value only slightly changes in low-humidity environment. To improve the characteristics of such sensors in the field of low humidity, a sensor with a double-sided structure was designed for measuring ppm levels. In this article, the principle of increasing the total effective surface area of the sensing film is investigated based on a conventional sensor. The sensor consists of a parallel-plate capacitive structure with a porous <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> -Al2O3-nanostructured sensing film fabricated on an aluminum substrate using an anodic oxidation method. First, the background and objectives of the work are presented. Next, the mathematical model of the sensor with a double-sided structure and its equivalent circuit is briefly described. A simulation in COMSOL is also presented. Then, the process of manufacturing the moisture-sensitive film and sensor is briefly explained. Finally, the performance of the double-sided <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\alpha }$ </tex-math></inline-formula> -al2O3 low-humidity sensor is tested and compared. The results show that the output response of the sensor increases by 2–3 times, and the sensitivity increases by nearly two times to 1.68%/ppm in the range of 1–1000 ppm. The sensor’s temperature dependence, response time, and measurement range are improved.

A Prototype of Supply Chain Traceability using Solana as blockchain and IoT

The main objective of this article is to present a prototype that integrates blockchain and Internet-of-Things (IoT) devices to digitize information across a generic Supply Chain (SC). Blockchain bring many benefits to the operation of improving provenance and reliability, such as, digitizing the interaction among SC actors to allow implementation of smart contracts to support a secure and authenticated chain. In technical terms, blockchain is a decentralized database where information recording is secure and blockchain-based traceability in the SC can address shortcomings that exist in centralized solutions. Provenance could be achieved by implementing SC business logic within smart contracts using blockchains. This article presents an architecture of software that combines blockchain with IoT devices that allows traceability of a generic product from source to destination, through multi-echelon suppliers, logistics, manufacturers to the end-customer. The presented software architecture uses the Solana blockchain for the implementation of SC processes and business logic. This blockchain was selected from a review, reported in this article, of several widely used blockchain networks. It was selected mainly due to its speed and cost of transactions. Within the blockchain we store SC related data and events communicated over the internet and mobile application channels, in a Solana blockchain using Solana's native blockchain libraries. the IoT devices used are Sigfox cloud gateway, and Sensit that uses LPWAN (Low power wide area network) wireless telecommunication for data transfer. In our IoT device, the blockchain stores temperature, humidity, light, location, tilting, door opening, vibration, and magnetic field. The goal is to use existing technologies to develop a software architecture for a medium term objective of an implementable generic blockchain for SCs.

Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field

Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field

Early shrinkage experiment of concrete and the development law of its temperature and humidity field in natural environment

Concrete shrinkage is one of the main causes of structural deterioration, and clear understanding of the development law of temperature and humidity field is a key to accurately predict concrete shrinkage. To study the effect of temperature and humidity (T&H) difference of concrete caused by curing conditions on its shrinkage, this study designs a concrete shrinkage experiment with three different maintenance conditions of moisture-thermal exchange, moisture retention, and moisture-thermal retention. Subsequently, a data correlation algorithm combining maximum information coefficient (MIC) and random walk (RW) is proposed. The correlations between the environment T&H and the concrete T&H, concrete temperature and its humidity, as well as the concrete T&H and its shrinkage are quantitatively investigated. The experimental results indicate that the curing conditions of moisture retention and moisture-heat retention respectively reduce the concrete shrinkage strain by 30.7% and 11.3% compared with the moisture-heat exchange condition at the age of 125 days. The coupling effect between the change rate of temperature and the change rate of humidity needs to be considered in the analysis of the moisture and heat field of concrete, while the influence of the second derivative of temperature with respect to space on the humidity change rate (that is, the Soret effect) can be ignored. The conclusions obtained in this study can provide a basis for the selection of coupling terms in the moisture and heat transfer analysis of concrete and the establishment of shrinkage prediction models.

Analysis of Rewetting Characteristics and Process Parameters in Tobacco Strip Redrying Stage

To study the rewetting characteristics of tobacco strips during the redrying stage, a conjugate heat and mass transfer model of tobacco strips was established based on their physical properties. The fundamental relationship between the multiphysical fields and humidity fields of tobacco strips and the key process parameters was considered in this model, and the feasibility of the model was verified via experiments. Based on this model, the transfer and variation laws of the moisture content, humidification rate, and temperature of tobacco strips under different relative humidity and temperature conditions were studied, and the rewetting process parameters of tobacco strips were determined using COMSOL Multiphysics. The results show that this model can be used to study the rewetting characteristics of tobacco strips during the redrying stage. The rewetting of tobacco strips can be divided into two stages, namely, the accelerated stage and approximate constant rate increase stage, where the former stage has a greater impact on the rewetting effect. The higher the relative humidity and temperature, the greater the change in the moisture content, humidification rate, and effective moisture diffusivity, and the faster the increase rate. The best rewetting effect was achieved when the relative humidity was 85% and the temperature was 55 °C. Under these conditions, the moisture content of the tobacco strips was 11% to 13%, the humidification rate was 0.03 %/s to 0.29 %/s, the highest effective moisture diffusivity was 7.8 × 10−10 m2/s, and the maximum activation energy was 37.86 kJ/mol. This study provides a theoretical basis and data support for analyzing the characteristics of the rewetting process and for optimizing the process parameters.

Climate Behaviour and Plant Heat Activity of a Citrus Tunnel Greenhouse: A Computational Fluid Dynamic Study

Response to the expanding demand for high-quality citrus saplings plants requires optimisation and a deep understanding of production climate behaviour. In this context, greenhouse production is the most used technique because it allows farmers to effectively monitor plant growth through production condition control, especially climatic parameters. The current work presents an analysis of climate behaviour and plant heat activity of a citrus sapling tunnel greenhouse in the middle region of Morocco. In this regard, a computational fluid dynamic (CFD) model was developed and validated with respect to temperature and relative humidity measured values. The specificity of this model is the inclusion of a new non-grey radiative and heat transfers physical sub-models to couple the convective and radiative exchanges at the plastic roof cover and crop level. The findings showed that using a green shade net increased the greenhouse shadow, and the layering of plastic and shade net significantly reduced solar radiation inside the greenhouse by 50%. Also, the greenhouse airflow speed was deficient; it cannot exceed 0.3 ms−1, hence the dominance of the chimney effect in heat transfer. Despite the previous results, analyses of greenhouse temperature and relative humidity fields clearly showed the greenhouse climate behaviour heterogeneity, where spatial greenhouse air temperature and relative humidity difference values reached a maximum of 29.7 °C and 23%, respectively. For citrus plants, heat activity results showed that a weak fraction (1.44%) of the short wavelength radiation is converted to latent heat, which explains the low plant transpiration under these conditions. While the convective currents are the primary source of temperature and relative humidity heterogeneity inside the greenhouse, the presence of crop rows tends to homogenise the climate inside the greenhouse. We also concluded the necessity of proper condensation modelling near ground surfaces and inside the crop, and the water vapour effect on climate determination.

A Comparative Study on the Performances of Spectral Nudging and Scale-Selective Data Assimilation Techniques for Hurricane Track and Intensity Simulations

It is a common practice to use a buffer zone to damp out spurious wave growth due to computational error along the lateral boundary of limited-area weather and climate models. Although it is an effective technique to maintain model stability, an unintended side effect of using such buffer zones is the distortion of the data passing through the buffer zone. Various techniques are introduced to enhance the communication between the limited-area model’s inner domain and the outer domain, which provides lateral boundary values for the inner domain. Among them, scale-selective data assimilation (SSDA) and the spectral nudging (SPNU) techniques share similar philosophy, i.e., directly injecting the large-scale components of the atmospheric circulation from the outer model domain into the interior grids of the inner model domain by-passing the lateral boundary and the buffer zone, but the two methods are taking different implementation approaches. SSDA utilizes a 3-dimensional variational data assimilation procedure to accomplish the data injection objective, whereas SPNU uses a nudging process. In the present study, the two approaches are evaluated comparatively for simulating hurricane track and intensity in a pair of cases: Jeanne (2004) and Irma (2017) using the Weather Research and Forecasting (WRF) model. The results indicate that both techniques are effective in improving tropical cyclone intensity and track simulations by reducing the errors of the large-scale circulation in the inner model domain. The SSDA runs produced better simulations of temperature and humidity fields which are not directly nudged. The SSDA runs also produced more accurate storm intensities in both cases and more realistic structure in Hurricane Jeanne’s case than those produced by the SPNU runs. It should be noted, however, that extending these case study results to more general situations requires additional studies covering a large number of additional cases.

Quantifying the effects of advection on single crop coefficients over a humid paddy field for sustainable irrigation

Understanding the variation and magnitude of crop coefficient (Kc) is important to accurate determine crop evapotranspiration (ETc) and optimal irrigation scheduling. Sensible heat advection is expected to increase Kc by providing additional energy, while its effect on Kc over paddy field is rarely featured. A three-year experiment was conducted over a paddy field to develop local Kc using the eddy covariance technique (EC) in the Poyang River basin, southern China. The local Kc curve, the characteristics of advection and its contribution to Kc were investigated. The three-year average local Kc values during the initial, mid-season and late-season stages were 1.12, 1.29 and 1.13 for the early rice and 1.11, 1.39 and 1.02 for the late rice, respectively. The advection was more likely to occur during the late rice season than the early rice season. The three-year average advective days were 8.0 d for the early rice season and 29.7 d for the late rice season, respectively. The contribution of advection to daily Kc ranged from 0.6% to 13.2% for the early rice season and 1.1% to 37.7% for the late rice season, respectively. The upwind city was found to be a possible energy source for advection. The occurrence of advection was closely related to air temperature gradients (ΔT) between the city and paddy field and wind speed (u2). Our study indicated advection also existed in the humid paddy field and had considerable effects on Kc. These results are helpful for decision-makers to quantify crop water consumption and improve irrigation water efficiency.

Drying characteristics of concrete under surface low air pressure

The humidity change of C30, C50, and C80 concretes under surface low air pressure and atmospheric pressure were tested. Based on the mechanism of moisture transfer under low air pressure, a theoretical model of concrete humidity field considering the effect of pressure was established. The model was used to analyze the changes of concrete internal humidity field when the surface pressure changes. The results show that the relative humidity values of each concrete under low air pressure are lower than that of atmospheric pressure. And the effect is greater near the drying surface. The improved calculation model was used to calculate the humidity change of concrete under low air pressure. The calculated values were in good agreement with the experimental values. It can be used to study the internal humidity field of concrete under different pressure conditions.

Comparison of Three Convolution Neural Network Schemes to Retrieve Temperature and Humidity Profiles from the FY4A GIIRS Observations

FY4A/GIIRS (Geostationary Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical sounder onboard a geostationary satellite. It can achieve observations of atmospheric temperature and humidity profiles with high vertical and temporal resolutions. Presently, convolutional neural network algorithms are relatively less used in the field of atmospheric profile retrieval, and different convolutional neural network approaches have different characteristics. The one-dimensional convolutional neural network scheme 1D-Net and two three-dimensional retrieval schemes U-Net 1 and U-Net 2 are used to achieve atmospheric temperature and humidity profiles under all skies based on GIIRS-observed brightness temperatures in this paper. After validation with test training data, the retrievals of different schemes derived from actual GIIRS observations and level 2 operational products were verified with ERA5 reanalysis data and radiosonde measurements in summer and winter respectively. The retrieved three-dimensional temperature and humidity fields from U-Net 1 and U-Net 2 are closer to the ERA5 reanalysis field in both distribution and value than the retrievals from the 1D-Net scheme and level 2 operational products. In particular, the inversion field of the U-Net 2 scheme is more continuous in space. Compared with radiosonde observations, the accuracy of the level 2 temperature product is the highest when the field of view is completely clear both in winter and summer month. The root mean square error (RMSE) of temperature retrieval of the two U-Net schemes is the second highest, and the RMSE and bias of the 1D-Net scheme are both large. Two U-Net schemes overestimate the temperature and humidity slightly in winter and underestimate it in summer in both clear and all sky cases. Under all sky conditions, the temperature retrieval RMSE and bias of the two U-Net schemes above 800 hPa are lower than those of the level 2 products, especially the U-Net 2 scheme with an RMSE of approximately 2.5 K. The U-Net 2 scheme bias is the smallest, with a value of approximately 0.5 K in winter. Since the level 2 product only provides the atmospheric temperature above the cloud top, it indicates that its temperature product accuracy is very low when the field of view is influenced by clouds. The humidity retrieval RMSEs of the two U-Net schemes is within 2 g/kg, better than that of the 1D-Net scheme. The retrieval accuracy of the U-Net 2 scheme is approximately 0.3 g/kg better than that of the U-Net 1 scheme below 600 hPa in winter. Level 2 does not provide humidity products. The summer humidity retrieval is worse than in winter. In general, among the three deep machine learning algorithms, 1D-Net has a large retrieval error, and the temperature and humidity from U-Net 2 have the highest accuracy. The retrieval speeds of the two U-Net schemes are nearly the same, and both are faster than that of scheme 1D-Net.

Application and algorithm research of TDLAS

Tunable diode laser absorption spectroscopy (TDLAS) is extensively utilized in monitoring of trace gases in the environment. With the relative entropy tomographic reconstruction, simultaneous multiplicative algebraic reconstruction technique algorithm and optimization of existing functions and models, TDLAS has been applied to reconstruction of temperature and humidity field, combustion diagnosis, mass flow monitoring and other domains, this paper will analyze the existing TDLAS application and algorithm research.

A case study of evaluating the GRAPES_Meso V5.0 forecasting performance utilizing observations from South China Sea Experiment 2020 of the “Petrel Project”

The evaluations of numerical weather prediction (NWP) models are pivotal for their development. However, there is a big challenge to carry out over oceans because of the rarity of in-situ observations. With a unique suite of marine observations from South China Sea Experiment 2020 of the “Petrel Project” that undertook a mission to observe the peripheral cloud system of Typhoon Sinlaku (2020), this study evaluates the performance of GRAPES_Meso v5.0 forecasts operated by the China Meteorological Administration. Compared with the observations by dropsondes from the unmanned aerial vehicles (UAV), nine forecast cycles all well forecast the atmospheric vertical profiles with a basically consistent bias structure over ocean, which reveals that the model has a roughly accurate and stable forecast performance. The temperature bias demonstrates a pattern of warm-cool-warm distribution from model bottom level to near 300 hPa, corresponding to a wet-dry-wet pattern in humidity fields with considerable relative errors. The positive (negative) temperature (humidity) biases at heights below 925 hPa are related to the underestimation of total hydrometeor content. The pressure field has been systematically overestimated with a maximum value of ∼20 hPa at 6 km height. The vertical velocities have been underestimated, yet, they are closer to the observation in its spatial distribution when the leading time is shorter. The forecasted sea level pressure and temperature at 2-m have consistent temporal variation with two buoy observations, with biases varying from −0.82 hPa to 1.38 hPa and − 1.74 °C to 2.1 °C, respectively. These results reveal that GRAPES_Meso V5.0 has stable forecast performance with relatively small biases except for humidity field. Although the conclusions may not be universal since they are only based on the evaluation for one single typhoon event, this study highlights the potential of UAVs observations in model evaluation, especially over oceans. More observational experiments conducted by UAVs at different weather events are expected to obtain more robust results for the performance of NWP models.

Aviation contrail climate effects in the North Atlantic from 2016 to 2021

Abstract. Around 5 % of anthropogenic radiative forcing (RF) is attributed to aviation CO2 and non-CO2 impacts. This paper quantifies aviation emissions and contrail climate forcing in the North Atlantic, one of the world's busiest air traffic corridors, over 5 years. Between 2016 and 2019, growth in CO2 (+3.13 % yr−1) and nitrogen oxide emissions (+4.5 % yr−1) outpaced increases in flight distance (+3.05 % yr−1). Over the same period, the annual mean contrail cirrus net RF (204–280 mW m−2) showed significant inter-annual variability caused by variations in meteorology. Responses to COVID-19 caused significant reductions in flight distance travelled (−66 %), CO2 emissions (−71 %) and the contrail net RF (−66 %) compared with the prior 1-year period. Around 12 % of all flights in this region cause 80 % of the annual contrail energy forcing, and the factors associated with strongly warming/cooling contrails include seasonal changes in meteorology and radiation, time of day, background cloud fields, and engine-specific non-volatile particulate matter (nvPM) emissions. Strongly warming contrails in this region are generally formed in wintertime, close to the tropopause, between 15:00 and 04:00 UTC, and above low-level clouds. The most strongly cooling contrails occur in the spring, in the upper troposphere, between 06:00 and 15:00 UTC, and without lower-level clouds. Uncertainty in the contrail cirrus net RF (216–238 mW m−2) arising from meteorology in 2019 is smaller than the inter-annual variability. The contrail RF estimates are most sensitive to the humidity fields, followed by nvPM emissions and aircraft mass assumptions. This longitudinal evaluation of aviation contrail impacts contributes a quantified understanding of inter-annual variability and informs strategies for contrail mitigation.

Numerical investigation on temperature–humidity field under mechanical ventilation in the construction period of hot-humid tunnel along the Sichuan–Tibet Railway

High temperature–humidity environment is one of the severe challenges in constructing Sichuan–Tibet railway tunnels. For enhancing construction efficiency and guaranteeing tunnel worker safety, addressing the high temperature–humidity environment has irreplaceable significance. This study innovatively investigated the distribution law and evolution process of the thermal–humidity coupling field during tunnel construction. Besides, the corresponding ventilation measures were proposed based on the evaluation heat index (HI). If the initial temperature of the surrounding rock is constant, the distance between the ventilation duct outlet and the tunnel working face should not be greater than 30 m. Otherwise, the cooling performance of ventilation will be significantly weakened; If this temperature ranges in 65–85 °C, the recommended ventilation velocity is 10–25 m/s, and the ventilation distance should not exceed 20 m. The conclusions in this study are beneficial to better understand the high temperature–humidity environment in tunnel construction and can be a guideline for practical engineering application.

The Correlation between Rainfall, Temperature, Relative Humidity, and Rice Field Productivity in Aceh Besar

Various factors could affect rice field productivity, such as climate, management practices, and soil properties. Aceh Besar had experienced long drought, higher temperature, shifted seasons, and the decrease yield of rice productivity due to climate change. This research aimed to analyze the correlation between climate variables and rice field productivity, such as rainfall and mean, minimum, and maximum temperatures, relative humidity in Aceh Besar District. The monthly climate data and the rice field productivity data were employed for 10 (2011-2020) and 8 (2011-2018) consecutive years, respectively. The correlation between the climate variables were calculated using Pearson coefficient correlation. The results showed that rainfall and maximum temperature were positively correlated, as well as rainfall and relative humidity. In contrary, rainfall and mean temperature, rainfall and minimum temperature, rainfall and rice field productivity were negatively correlated. The latest indicating that rainfall did not impact the rice field productivity in Aceh Besar. It was quite contradictive to the reality in the field that significantly experiencing the long drought, higher temperature, shifted seasons and the decrease yield of rice field productivity. This was due to the lack of climate data employed that required longer period preferably 30 to 50 years which was not available.

Analysis of airborne sputum droplets flow dynamic behaviors under different ambient conditions and aerosol size effects

The coronavirus (COVID-19) is becoming more threatening with the emergence of new mutations. New virus transmission and infection processes remain challenging and re-examinations of proper protection methods are urgently needed. From fluid dynamic viewpoint, the transmission of virus-carrying droplets and aerosols is one key to understanding the virus-transmission mechanisms. This study shows virus transmission by incorporating flow-evaporation model into the Navier-Stokes equation to describe the group of airborne sputum droplets exhaled under Rosin-Rammler distribution. Solid components and humidity field evolution are incorporated in describing droplet and ambient conditions. The numerical model is solved by an inhouse code using advection-diffusion equation for the temperature field and the humidity field, discretized by applying the total-variation diminishing Runge-Kutta method. The results of this study are presented in detail to show the different trends under various ambient conditions and to reveal the major viral-transmission routes as a function of droplet size.

Cover Image, Volume 139, Issue 32

The cover image, prepared by Yuhao Liu, Ying Lin, Liming Wang, and Yaying Xu, shows a silicone prepolymer prepared to modify a cycloaliphatic epoxy resin (CEP) with uniformly distributed solid dispersion phases. The toughness, hydrophobicity, and hydrophobic transfer properties of the CEP were simultaneously improved. This method has potential for applications in outdoor insulation or other waterproof and humidity fields. The proposed mechanism offers a guide on how to design hydrophobic CEP materials. DOI: 10.1002/app.52478

Characteristics of Fungal Communities and Internal Mildew Occurrence during the Stages of Planting and Storing of Sunflower Seed in China.

Internally mildewed sunflower seeds pose a significant risk to human health. To control internal mildew, it is imperative to study its source in the main production area of China, which has been little investigated. Here, high-throughput sequencing was used to characterize the fungal and fungus-seed communities. Alpha diversity and ANOSIM analyses showed mildew did not alter the fungal compositions significantly. STAMP analysis showed that the sunflower seeds were most vulnerable to internal mildew during the field-planting stage. Alternaria was the predominant mildew-causing pathogen of sunflower seeds for consumption, which may originate from seed transmission and colonize at the seed-development stage. Finally, only a few seeds developed internal mildew with a worrisome level of Alternaria contamination in the humid field climate. NMDS analysis showed that climatic factors also played important roles in shaping microbial change during storage, with a relative humidity (RH) of 67% being the critical threshold in normal-temperature warehouses. Internal mildew never occurred below the RH threshold for the microbial community structure, which hardly changed after an average storage duration. The results indicated that a combination of field management to combat Alternaria, pretreatment with 5 KGy γ-irradiation and drying at the time of storage will minimize or prevent internal mildew. This work also provides an empirical framework for studies of mildewing in other shelled seeds.

Electroactive Polymer-Based Composites for Artificial Muscle-like Actuators: A Review.

Unlike traditional actuators, such as piezoelectric ceramic or metallic actuators, polymer actuators are currently attracting more interest in biomedicine due to their unique properties, such as light weight, easy processing, biodegradability, fast response, large active strains, and good mechanical properties. They can be actuated under external stimuli, such as chemical (pH changes), electric, humidity, light, temperature, and magnetic field. Electroactive polymers (EAPs), called ‘artificial muscles’, can be activated by an electric stimulus, and fixed into a temporary shape. Restoring their permanent shape after the release of an electrical field, electroactive polymer is considered the most attractive actuator type because of its high suitability for prosthetics and soft robotics applications. However, robust control, modeling non-linear behavior, and scalable fabrication are considered the most critical challenges for applying the soft robotic systems in real conditions. Researchers from around the world investigate the scientific and engineering foundations of polymer actuators, especially the principles of their work, for the purpose of a better control of their capability and durability. The activation method of actuators and the realization of required mechanical properties are the main restrictions on using actuators in real applications. The latest highlights, operating principles, perspectives, and challenges of electroactive materials (EAPs) such as dielectric EAPs, ferroelectric polymers, electrostrictive graft elastomers, liquid crystal elastomers, ionic gels, and ionic polymer–metal composites are reviewed in this article.