Multi-scale Space Research Articles

Study of perforated well productivity by a multi-scale coupling flow model with Darcy-brinkman-Navier/Stokes

The study of oil and gas flow patterns after perforation completion is essential for well productivity prediction and solutions optimization. Due to the multi-scale flow characteristics of production fluids after perforation completion, we have developed a multi-scale coupled flow model incorporating Darcy, Brinkman, and Navier/Stokes equations based on mass conservation and pressure equilibrium principles. The oil and gas flow after perforation completion is simulated, and the multi-scale coupled model is solved by numerical methods, revealing the flow law of oil and gas in the multi-scale space formed by perforation. Additionally, sensitivity analysis on factors influencing perforation completion productivity was conducted. Results indicate that the most influential parameters on well productivity, in descending order, are: blocking perforation permeability > perforation diameter > perforation density > perforation depth > perforation phase > crushed zone thickness. The selection of appropriate internal flow models for perforations is critical for accurate well productivity estimation. Given the significant impact of perforation permeability on completion productivity, measures should be taken to prevent perforation blockage during production, and clear blockages promptly upon detection. This research provides a theoretical foundation for rational selection of perforation methods, gun bullets, perforation parameters, and completion techniques.

Detective generalized multiscale hybridizable discontinuous Galerkin(GMsHDG) method for porous media

The Detective Generalized Multiscale Hybridizable Discontinuous Galerkin (Detective GMsHDG) method aims to further reduce the computational cost of the GMsHDG method. The GMsHDG method itself reduces the computational cost of the HDG method by employing an upscaled structure on a two-grid mesh. Given a PDE within a specified domain, we subdivide the domain into polygonal subdomains and transforms a HDG problem into globular and local problems. Globular problem concerns whether the solutions on smaller domains glue well to form a globular solution. The process involves generation of multiscale spaces, which is a vector space of functions defined on edges of the polygonal regions. A naive approximation by polynomials fails, especially in porous media, necessitating the generation of problem-specific spaces. The Detective GMsHDG method improves this process by replacing the generation of the multiscale space with the detective algorithm. The Detective GMsHDG method has two stages. First is called an offline stage. During the offline stage, we construct a detective function which, given a permeability distribution, it gives a multiscale space. Later stage is called the offline stage where, given the multiscale space, we use GMsHDG method to solve a given PDE numerically. We show numerical results to argue the liability of the solution using the detective GMsHDG method.

Progressive cross-level fusion network for RGB-D salient object detection

Depth maps can provide supplementary information for salient object detection (SOD) and perform better in handling complex scenes. Most existing RGB-D methods only utilize deep cues at the same level, and few methods focus on the information linkage between cross-level features. In this study, we propose a Progressive Cross-level Fusion Network (PCF-Net). It ensures the cross-flow of cross-level features by gradually exploring deeper features, which promotes the interaction and fusion of information between different-level features. First, we designed a Cross-Level Guide Cross-Modal Fusion Module (CGCF) that utilizes the spatial information of upper-level features to suppress modal feature noise and to guide lower-level features for cross-modal feature fusion. Next, the proposed Semantic Enhancement Module (SEM) and Local Enhancement Module (LEM) are used to further introduce deeper features, enhance the high-level semantic information and low-level structural information of cross-modal features, and use self-modality attention refinement to improve the enhancement effect. Finally, the multi-scale aggregation decoder mines enhanced feature information in multi-scale spaces and effectively integrates cross-scale features. In this study, we conducted numerous experiments to demonstrate that the proposed PCF-Net outperforms 16 of the most advanced methods on six popular RGB-D SOD datasets.

A transfer learning model for cognitive electronic reconnaissance of unmanned aerial vehicle: Experiments

Applying Deep Reinforcement Learning (DRL) technologies to Unmanned Aerial Vehicle (UAV) electronic reconnaissance is one of the current research hotspots. However, simulation and engineering practice show that due to poor generalization of DRL models, the performance of Cognitive Electronic Reconnaissance (CER) policies based on training will significantly decrease when the mission scene undergoes slight changes. To address this issue, we thoughtfully combine the mission area segmentation technique with transfer DRL and propose a difference-adaptive transfer DRL algorithm. This algorithm involves mission subarea segmentation, subarea pre-training, multi-subarea policy transfer, and multi-subarea splicing, providing an efficient solution to the convergence problem of the DRL algorithm caused by mission space expansion and reward sparsity. Additionally, a general CER transfer learning simulator is developed based on the analysis of the capabilities of the maneuvering platform and electronic reconnaissance payload. Multiple sets of CER policy transfer learning experiments are designed for different mission spaces, mission difficulties, and UAV characteristics. Compared with the algorithm baseline, our designed policy model significantly outperforms: the mission completion rate of UAVs in multi-scale mission spaces improves by up to 37.4%, reaching 97.5%, while the training time is reduced by 2.46 h. Further behavior analysis shows that this policy model enables UAVs to exhibit target tracking behaviors such as hovering and sustained approach.

Micro-CT structures facilitated lattice Boltzmann simulations on the water dynamics in PEMFCs GDL-Gas channel

This study employed Micro X-ray computed tomography (Micro-CT) to reconstruct the gas diffusion layer (GDL) in proton exchange membrane fuel cells (PEMFCs) and used lattice Boltzmann method (LBM) simulations to investigate the dynamic behavior of liquid water in the multiscale space of the GDL and gas channel (GC). The results showed that the transport of liquid water from GDL to GC through the interface of GDL-land and GDL-GC were divided into four stages: large pore filling, through-plane breakthrough, in-plane diffusion, and rapid transport into GC. The wettability of GC wall and GDL affects the transport and distribution of liquid water. When the contact angle on the GC wall was less than 90°, it promoted liquid water transport to the top GC wall, resulting in faster internal flow formation. Conversely, contact angles exceeding 90° caused liquid water to break through the GDL-GC interface from pores away from the GC wall. When the contact angle of the GDL was in the range of 110°–150°, both excessively large and excessively small contact angles hindered water transport and management. Maintaining the contact angle of the GDL around 130° enhanced the breakthrough path and transport speed of liquid water into the GC, and it also reduces localized liquid water accumulation.

(Digital Presentation) Multiscale Modeling of Two-Phase Transport in the Membrane Electrode Assembly of Polymer Electrolyte Membrane Fuel Cells: Effect of Relative Humidity and Temperature

Water management plays an important role in the performance and durability of polymer electrolyte membrane fuel cells (PEMFCs). Achieving enhanced water removal at high current density without membrane dehydration requires an optimal design of the membrane electrode assembly (MEA) and MEA-channel interaction depending on operating conditions. The analysis of two-phase transport is challenged by the wide range of pore sizes found in porous layer assemblies, varying from 1-100 nm in the catalyst layer (CL), 50-1000 nm in the microporous layer (MPL) and 10 µm in the gas diffusion layer (GDL), up to the millimeter-sized channel. In this work, a hybrid multiscale model is presented to describe two-phase transport and performance of a representative unit cell as a function of relative humidity (RH) and temperature [1-3]. A control volume (CV) decomposition is used to represent the multiscale pore space of the GDL, MPL and CL in terms of local effective transport properties (porosity, effective diffusivity, permeability, thermal/electrical conductivity and entry capillary pressure) [4]. The model incorporates a macroscopic continuum formulation for gas flow and species, energy, liquid-phase pressure, water dissolved in ionomer, and electronic and ionic potentials [5]. Liquid water transport is modeled by means of a multi-cluster invasion-percolation algorithm, which is coupled to the macroscopic continuum formulation by the phase-change source term of water (evaporation/condensation). Water clusters with a net condensation rate grow by invading the adjacent dry CV with the lowest entry capillary pressure, while clusters with a net evaporation rate shrink by drying the wet CV of the cluster with the lowest entry capillary pressure. An all-or-nothing invasion law is adopted for saturation in the GDL (one pore per CV), while a macroscopic description in terms of capillary pressure curve at the representative elementary volume scale (REV) is used for the MPL and the GDL (thousands to millions of pores per CV). An example of the saturation distribution in the cathode MEA is shown in Figure 1. Local saturation remains between 0.1-0.4 in REVs of the CL and the MPL, increasing up to 1 in macropores of the GDL. Water condenses preferentially under the ribs and transport by capillary action to the GDL/channel interface, where the water flow is released to the channel. The channel saturation remains low, around 0.1, due to the large gas flow velocity used in the differential cell.[1] P.A. García-Salaberri, Modeling diffusion and convection in thin porous transport layers using a composite continuum-network model: Application to gas diffusion layers in polymer electrolyte fuel cells, 167 (2021) 120824.[2] D. Zapardiel, P.A. García-Salaberri, Modeling the interplay between water capillary transport and species diffusion in gas diffusion layers of proton exchange fuel cells using a hybrid computational fluid dynamics formulation, J. Power Sources 520 (2022) 230735.[3] P.A. García-Salaberri, Effect of thickness and outlet area fraction of macroporous gas diffusion layers on oxygen transport resistance in water injection simulations, Transp. Porous Media 145 (2022) 413-440.[4] P.A. García-Salaberri, I.V. Zenyuk, A General-Purpose Tool for Modeling Multifunctional Thin Porous Media (POREnet): From Pore Network To Effective Property Tensors, Heliyon (2023), submitted.[5] P.A. García-Salaberri, A. Sánchez-Ramos, A numerical analysis of the effect of layer-scale and microscopic parameters of membrane electrode assembly in proton exchange fuel cells under two-phase conditions, J. Power Sources (2023), accepted. Figure 1. Saturation distribution in the cathode MEA of a PEMFC operated at 70 oC and RH=0.95 at high stoichiometric conditions. The current density is I=0.5 A cm-2.- Figure 1

Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and variational deep neural network (DNN) framework relying on expectation maximization (EM) based learning, termed adversarial EM (AdvEM). AdvEM proposes an encoder–decoder architecture with a multiscale latent space, and generalized-Gaussian models enabling datum-specific robust statistical modeling in latent space and image space. The model robustness is further enhanced by including adversarial learning in the training protocol. Unlike typical variational-DNN learning, AdvEM proposes latent-space sampling from the posterior distribution, and uses a Metropolis–Hastings scheme. Unlike existing schemes for PET or CT image enhancement which train using pairs of low-dose images with their corresponding normal-dose versions, we propose a semi-supervised AdvEM (ssAdvEM) framework that enables learning using a small number of normal-dose images. AdvEM and ssAdvEM enable per-pixel uncertainty estimates for their outputs. Empirical analyses on real-world PET and CT data involving many baselines, out-of-distribution data, and ablation studies show the benefits of the proposed framework.

Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process.

PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics.

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network's advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.

Reliable object tracking by multimodal hybrid feature extraction and transformer-based fusion

Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tracking. However, the existing approaches usually integrate multimodal inputs through adaptive local feature interactions, which cannot leverage the full potential of visual cues, thus resulting in insufficient feature modeling. In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities and then uses a unified encoder to align the features across different domains. Moreover, we propose an enhanced transformer-based module to fuse multimodal features using attention mechanisms. With these methods, the MMHT model can effectively construct a multiscale and multidimensional visual feature space and achieve discriminative feature modeling. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with that of other state-of-the-art methods. Overall, our results highlight the effectiveness of the MMHT model in terms of addressing the challenges faced in visual object tracking tasks.

High Efficiency Deep-learning Based Video Compression

Although deep learning technique has achieved significant improvement on image compression, but its advantages are not fully explored in video compression, which leads to the performance of deep-learning-based video compression (DLVC) is obviously inferior to that of hybrid video coding framework. In this article, we proposed a novel network to improve the performance of DLVC from its most important modules, including Motion Process (MP), Residual Compression (RC), and Frame Reconstruction (FR). In MP, we design a split second-order attention and multi-scale feature extraction module to fully remove the warping artifacts from multi-scale feature space and pixel space, which can help reduce the distortion in the following process. In RC, we propose a channel selection mechanism to gradually drop redundant information while preserving informative channels for a better rate-distortion performance. Finally, in FR, we introduce a residual multi-scale recurrent network to improve the quality of the current reconstructed frame by progressively exploiting temporal context information between it and its several previous reconstructed frames. Extensive experiments are conducted on the three widely used video compression datasets (HEVC, UVG, and MCL-JVC), and the performance demonstrates the superiority of our proposed approach over the state-of-the-art methods.

Multi-scale image recognition strategy based on convolutional neural network

The accurate recognition and interpretation of multi-scale visual information is a critical focus within contemporary computer vision research. To this end, this study explores and innovatively constructs a multi-scale image recognition strategy based on a Convolutional Neural Network (CNN) with a multi-level and multi-resolution perception domain. This strategy is embedded with an advanced multi-level convolutional operation mechanism, which enables the model to intelligently explore and learn the multi-scale feature representation space of images from tiny texture to grand structure, from shallow simple features to deep semantic abstraction. The core technology path of this paper is to design a deep separable convolutional architecture and combine pyramid pool technology to form a unique network module. This modular design not only ensures the computational efficiency of the model but also improves the ability of extracting and integrating multi-scale image features. Following intensive experimentation on an array of extensively recognized and substantial image datasets, the multi-scale image recognition approach introduced in our study has demonstrated marked enhancements in both recognition capability and stability, manifesting clear superiority compared to conventional, single-scale image recognition methodologies. This research not only enriches the theoretical framework of image recognition, but also provides a new and efficient solution for dealing with complex multi-scale image recognition challenges in practical applications, and further promotes the development of image understanding and recognition technology.

Mitigating spectral bias for the multiscale operator learning

Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs). In this work, we focus on multiscale PDEs that have important applications such as reservoir modeling and turbulence prediction. We demonstrate that for such PDEs, the spectral bias towards low-frequency components presents a significant challenge for existing neural operators. To address this challenge, we propose a hierarchical attention neural operator (HANO) inspired by the hierarchical matrix approach. HANO features a scale-adaptive interaction range and self-attentions over a hierarchy of levels, enabling nested feature computation with controllable linear cost and encoding/decoding of multiscale solution space. We also incorporate an empirical H1 loss function to enhance the learning of high-frequency components. Our numerical experiments demonstrate that HANO outperforms state-of-the-art (SOTA) methods for representative multiscale problems.

Multi-dimensional distribution network voltage collaborative estimation method using wavelet transform based multiscale analysis

Conventional methods for distribution network voltage estimation may encounter resource contention issues when performing noise reduction, harmonic calculation, and singular inspection. To address such problems, this study presents a wavelet transform based multiscale analysis (WT-MSA) method for achieving multifunctional integration of multi-objective collaborative voltage estimation in distribution networks. First, the basic concepts of multiscale space and wavelet function are described, mainly used for decomposing the original signal under different scales of wavelet spaces and then conducting filtering. Second, a voltage coordinated estimation method is established, with a marginal threshold function employed as the criterion for correcting wave coefficients. Following that, an interpolation-energy based method for gradually estimating both fundamental and harmonic effective information is developed. Finally, three different types of Cases are set up for simulation verification, demonstrating that the proposed method meets the accuracy requirements for voltage estimation in distribution networks.

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (Hs) and wave period (Ts) from X-band marine radar images. Since the shore-based radar image in this study is interfered with by other radar radial noise lines and solid target objects, to ensure that the proposed convolutional neural network (CNN) extracts the image features accurately, it is necessary to pre-process the radar image to eliminate interference. Firstly, a pre-trained GoogLeNet is used to extract multi-scale depth space features from the radar images to estimate Hs and Ts. Since CNN-based models cannot analyze the temporal behavior of wave features in radar image sequences, self-attention is connected after the deep convolutional layer of the CNN to construct a convolutional self-attention (CNNSA)-based model that generates spatial-temporal features for Hs and Ts estimation. Simultaneously, Hs and Ts measured by nearby buoys are used for model training and reference. The experimental results show that the proposed CNNSA model reduces the RMSD by 0.24 m and 0.11 m, respectively, in Hs estimation compared to the traditional SNR-based and CNN-based methods. In Ts estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively.

Spatial Relationship Analysis of Geographic Elements in Sketch Maps at the Meso and Micro Spatial Scales

Sketch maps are an abstract and conceptual expression of humans’ cognition of geographic space. Humans perceive geographical space at different spatial scales. However, few researchers have considered the spatial relationships of geographic elements in sketch maps at multiple spatial scales. Considering the meso and micro spatial scales, this study analyses the accuracy of the spatial relationships depicted in 52 sketch maps of urban areas, including qualitative orientation, order, qualitative distance, and topological relationships. We utilized OpenStreetMap (OSM) to assess the accuracy of the four spatial relationship representations in the sketch maps. This study evaluates the reliability of spatial relationships in capturing the invariant spatial information of geographic elements in sketch maps. It helps to understand the differences in human cognition of multi-scale space.

Macroscale Gas–Water Two-Phase Transport Simulation in Shales considering Nanomicroscale Effects

As an effective approach to evaluate shale gas production, macroscale numerical simulations have been conducted; however, multiscale storage space and complex gas–water two-phase transport mechanisms are not systematically considered. To deal with this, a rigorous cross-scale gas–water two-phase transport simulation method that considers the pore, core, and field scale is proposed herein. First, to investigate the fluid storage state in different pore types during the gas–water two-phase transport process, a shale pore network gas–water transport simulation is conducted. Subsequently, a core-scale model that considers the distribution of organic matter is constructed, and the gas–water two-phase transport behaviors are upscaled from the pore to the core by incorporating nanomicroscale effects. Next, a shale gas–water two-phase macroscale numerical simulation is implemented based on the core-scale simulation results. Multiple interacting continua are used to describe the shale matrix and microfractures, whereas the embedded discrete fracture method is employed to represent hydraulic fractures. Finally, the manner by which nanomicroscale effects, stress sensitivity, and SRV area affect the production of shale gas reservoirs is discussed comprehensively. This study provides a practical method to estimate shale gas production by rigorously considering gas–water nanomicroscale effects to reduce uncertainties during productivity evaluation.

Hydrothermal fluid alteration of the Ordovician epigenetic karstification reservoir in the Tahe Oilfield, Tarim Basin, NW China

In recent years, the Ordovician carbonate reservoirs in the Tarim Basin have received attention due to continuous advancements in of deep strata exploration for oil and gas. The Ordovician carbonates in the Tahe Oilfield have experienced multiple phases of tectonic movement and ancient karst action to form reservoirs consisting of multi-scale spaces such as large caves, fractures, and dissolution pores. This study identifies indicators of atmospheric freshwater karst and hydrothermal karst in the Ordovician carbonate rocks of the Tahe Oilfield by comparing and analyzing lithological observation, geochemical data, fluid inclusions, logging interpretation, and seismic data. The spatial and temporal distribution of karst reservoirs are summarized, and the results show that the stage of tectonic movement and pulsating uplift of strata occurred in the early Caledonian and Hercynian orogenies, and the carbonate rocks were uplifted to the surface and large-scale atmospheric freshwater karstification.In the deeper strata, the thermal convection of fluids caused by volcanic activity accelerated the thermochemical sulfate reduction (TSR), and the generated H2S gas accompanied the upward transport of hydrothermal fluids, which further dissolved and modified the original karst system, increasing the storage space. However, away from the heat source, calcite (positive Eu anomaly, higher 87Sr/86Sr ratio, lower δ18O value,) reprecipitated due to the temperature reduction, so the role of hydrothermal activity in reservoir reconstruction is limited. The two karst action patterns indicated that epigenetic karstification is an important process for forming carbonate reservoirs in the Tahe Oilfield and the basis of hydrothermal karst reservoir formation. This study demonstrates the research and exploration value of karstification for the geo-energy field, which could benefit sustainable development in the Tarim Basin.

A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement

Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models.

Excitation of proton firehose instability in magnetospheric cold and hot proton plasma: a quasilinear approach

Abstract Unstable states of different charged species in the solar wind and Earth’s magnetosphere are governed with the collective and collisional processes. For these dilute plasmas, the contribution of microinstabilities driven by the anisotropic particle distribution and heat flux becomes important in defining the stable/equilibrium states of electrons and ions/protons. The present paper highlights the key role of proton firehose instability to regulate an unchecked rise in the temperature anisotropy in these solar wind and magnetospheric environments. Right-handed circularly polarized proton firehose mode becomes unstable when the temperature condition of T ‖p > T ⊥p is satisfied, where the directional subscripts denote directions with respect to the ambient magnetic field. Based on the observations of magnetospheric multi-scale (MMS) space mission, we assume the bi-Maxwellian nature of the model distribution for the multi-component proton plasma. To study the time evolution of the unstable mode, we further allow the time variation in the cold and hot proton temperatures. For the choice of the initial conditions related with observations, we unveil the wave properties (growth and unstable wave number domain) corresponding to the cold/hot proton temperature anisotropy and the plasma betas of constituents proton components. In the back action of proton firehose instability, we highlight the time-scale modifications and saturation of initial bi-Maxwellian distributions and resulting wave-energy densities for various choices of initial cold-hot temperature anisotropy and plasma betas.