Emission Energy Research Articles

A physics-based acoustic emission energy method for mixed-mode impact damage prediction of composite laminates

In-service composite laminates are susceptible to impact-induced damage, which can substantially reduce its integrity and service life. The damage prediction remains a great challenge due to mixed damage modes and varying damage patterns. This study develops a novel acoustic emission (AE) energy method for predicting damage areas under three typical damage modes. Laboratory testing of composite laminate specimens subject to quasi-static indentation is performed in conjunction with in-situ AE monitoring to acquire AE data. By bridging two sets of energy formulations developed, namely, the one that correlates the damage area and the released strain energy of each damage mode and another that relates the released strain energy to the AE energy, an analytical model for predicting damage areas using AE energy components is derived. Proper signal procedure procedures are established to extract the energy components from AE monitoring data, and numerical and testing data are used to calibrate the model parameters. The effectiveness of the proposed model is further validated by comparing the prediction results of the damage areas with the actual damage areas of specimens tested under different indentation depths. The result indicates that the proposed AE energy method can yield reliable predictions of the damage area under mixed damage modes.

Assessing the energy system's greenhouse emissions via the health of Smart Territories and Cities

The aim of this paper is to understand how the main drivers of air pollution and greenhouse emissions impact public health in a Smart Territory — meaning a technologically integrated city or neighborhood — as well as how to design an optimized energy system model beyond only data by including the holistic experience of its people through phenomenology and ethics. We understand that a Territory and a City is a complex system where mathematical tool modeling known as Design of Experiment (DOE) and its optimization solutions are required to establish causality and identify the variables that have a broader impact on public health so that mortality rates due to air pollution are reduced. DOE's statistical branch is a novel methodology when applied to energy systems and the design of Smart Territories and Cities. Because of it, we have been able to demonstrate how energy emissions, capacity and Levelized Cost of Energy (LCOE) affect the mortality rate and create the foundations for building fully decarbonised energy systems enhanced by mathematical solutions. However, the Cartesian approach of extrapolating from models which align themselves with economic growth alone will not solve the energy dilemma. In fact, the current energy transition project is based on outdated archetypes that lead to scary futures. To course-correct the problematic energy model of a Smart Territory, phenomenologists and creative imagination need to be cultivated and put into action together with mathematical modeling, leading to vectors of positive change and adaptation. Only then might the energy models designed to be reproducible could became ethically virtuous and therefore accepted by the Territory's co-dwellers within the chain of causalities.

Excavation-induced cracking of clastic rock: A true triaxial instantaneous unloading study with varied levels of initial damage

The cracking and squeezing problem in deep engineering significantly constrains project construction. To reveal the cracking mechanism of rock under instantaneous unloading, a self-designed rigid true triaxial experimental apparatus, equipped with groundbreaking electromagnetic unloading and high-speed camera functions, was utilized to conduct instantaneous unloading tests on clastic rock with varied levels of initial damage. The results indicate that samples undergo severe and irreversible lateral dilation during instantaneous unloading, with dilational strain rapidly increasing at higher initial damage levels. Instantaneous unloading induces the generation of numerous tensile microcracks, which propagate and coalesce rapidly. The crack propagation speed, as well as the length and width of the cracks at failure, increase with the rise of the initial damage level. The samples eventually exhibit two distinct macroscopic fracture zones: a tensile fracture zone and a tensile-shear mixed fracture zone. Analysis of acoustic emission hits and energy indicates that higher initial damage levels correspond to greater unloading damage, with a higher overall proportion of tensile cracks throughout the experiment. Under the induction of blasting excavation, radial stress is rapidly unloaded, and dense tensile cracks emerge in the immediate surrounding rock, leading to cracking and squeezing towards the free face. Importantly, higher initial damage levels intensify the squeezing effect. The self-developed equipment serves as a crucial technical tool for researching excavation unloading, and the insights derived from this study provide valuable guidance for understanding cracking mechanisms and informing the construction of deep engineering projects.

Hierarchical nested riblet surface for higher drag reduction in turbulent boundary layer

Riblets can be potentially employed to passively reduce the turbulent friction drag. However, the drag reduction performance of riblets does not currently meet expectations, which could assist in emission reduction and energy conservation in green transportation. This study proposes and validates a topological form of hierarchical nested riblets (HNR) that significantly enhances the drag reduction performance. To explore the drag reduction enhancement mechanism, direct numerical simulations are performed for flow simulation on the riblet surface under different Reynolds numbers. The results show that under the riblet dimensionless spacing of the riblet s+≈21, the drag reduction performance of the HNR surface improves by about 70% compared to that of the uniform riblet surface, which is inspired by shark skin. From the perspective of turbulence statistics, the HNR surface reduces the turbulent mixing near the wall, weakening the momentum transfer. Furthermore, the transient flow field shows that the secondary riblet in HNR prevents some turbulent flow and streamwise vortices from entering the groove, considerably weakening the dispersive stress induced by the secondary flow. Moreover, owing to the influence of the secondary riblet, small-scale turbulence develops and strengthens into large-scale turbulent motion, which is advantageous to the boundary layer flow and results in drag reduction improvement.

Failure monitoring and classification from acoustic emission tests on impact-damaged hydrogen composite pressure vessels

Composite overwrapped pressure vessels (COPV) for hydrogen will play an important role in emission free mobility and energy storage. The DELFIN project (2018 – 2022) was set up with the goal of developing improved COPVs that are lighter, cheaper and more durable. A key aspect of this project was to conduct full-scale impact tests with different gas pressure levels that were used to evaluate the vessels’ crash performance and to verify numerical impact simulations. These tests were followed by controlled pressure experiments until the burst pressure was reached. The procedure was monitored with acoustic emission testing (AT) and subsequent computer tomography (CT). The residual COPV stability and the AT results are compared to different scenarios regarding the impact energies and impact angles. The event localization from AT shows a clear correlation of the emitted energy with the damaged areas. We classify the recorded signals into groups that can be related to the failure mechanism and compare those to the pressure evolution before failure of the COPV. All tests indicate that large impact energies lead to a significant reduction of the burst pressure of COPVs, whereas a higher internal pressure can have a stabilizing effect that reduces the damaging effect.

Superhydrophobic FeNi3/Al2O3 multifunctional hybrid Janus particles for the catalytic degradation of azo dye, oil/water separation and microplastics removal

Emerging pollutants are causing global health and environmental challenges, necessitating the advancement of methodologies for their efficient removal. In this study, we present the efficacy of superhydrophobic FeNi3/Al2O3 Janus particles in the removal of oils and microplastics from water, combined with the degradation of azo dyes, leveraging their surface properties. Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray Spectroscopy (EDS) revealed the morphology and elemental composition of these particles, which were further complemented by X-ray diffraction (XRD) for phase identification. After surface functionalization of the FeNi3/Al2O3 Janus particles with lauric acid, they exhibited superhydrophobicity (152 ± 1°) and superoleophilicity (0°). High-resolution X-ray photoelectron spectroscopy (HR-XPS) showed that the alumina face was modified with lauric acid, while the intermetallic face remained as FeNi3. Significantly, the alumina face demonstrated 99 % efficiency in separating oils and microplastics, while the FeNi3 face facilitated the complete degradation (100 %) of methyl red within three minutes, as shown by ultraviolet-visible spectrophotometry. These results highlight the multifunctional properties of superhydrophobic FeNi3/Al2O3 Janus particles in removing and degrading various pollutants.

Hydrothermally synthesized Sr-doped In2S3 microspheres for efficient degradation of noxious RhB pollutants in visible light exposure

A series of SrxIn2-xS3 (x = 0, 2, 4, 6 and 8 mol%) microspheres have been prepared at 160°C using hydrothermal approach and examined the impact of Sr doping on its structural, morphological, optical and photocatalytic properties. Structural properties have been investigated using X-ray diffraction (XRD) with Rietveld refinement. Morphology and elemental composition have been analysed using Field emission scanning electron microscopy (FESEM) and Energy dispersive X-ray Spectroscopy (EDX), respectively. Optical band gap energy (Eg) has been determined using Diffuse reflectance Spectroscopy (DRS) and found to range between 2.15 eV to 2.07 eV. Photoluminescence (PL) emission spectra confirm a decrease in charge carrier recombination rate with Sr doping. The negative surface potential has been assessed by the Zeta analyzer, which indicates its suitability for the degradation of cationic dyes such as Rhodamine-B (RhB). Among the SrxIn2-xS3 microspheres, Sr0.04In1.96S3 demonstrated highest photocatalytic efficiency ∼98 % degradation of RhB pollutant (10 ppm) in 240 min. using 10 mg of catalyst per 100 ml. The adsorption kinetics in the presently studied samples obeys the pseudo 2nd order kinetic model. This study highlights the significant impact of Sr doping on the photocatalytic efficiency of SrxIn2-xS3 microspheres, offering promising applications in removal of RhB dye.

Multi-Horizon Model Predictive Control for Energy Management in Zero-Emission High-Speed Passenger Vessels

Abstract High-speed passenger vessels (HSPVs) play an important role in coastal regions due to their high energy consumption and emissions. This study develops an optimal energy management system (EMS) for a zero-emission hybrid power system, powered by a fuel cell (FC) and a battery energy storage system (BESS). The global optimal solution for loading the FC and operating the BESS is computed using a linear programming (LP) approach, assuming a priori knowledge of the complete operational profile. Since the global solution is not realizable in an onboard EMS, the main objective of this paper is to develop an algorithm for real-time application. A multi-horizon model predictive control (MH-MPC) algorithm is applied to a model of the power system to solve the optimization problem with a prediction mechanism over the entire route. This approach is benchmarked against the global LP solution to evaluate the effectiveness of the proposed EMS. The results highlight the potential of the MH-MPC approach for achieving a near-optimal EMS in a zero-emission HSPV.

Effects of industrial hydrogen-rich gases on the performance of the spark ignition engine under various combustion modes

Hydrogen is emerging as a promising alternative to traditional fossil fuels for the transportation. Additionally, the hydrogen-rich gases (HRG) can produce on a large scale during the process of chemical production. In this study, the influences of HRG on the performance behaviors of the spark ignition (SI) engine are comprehensively analyzed under various combustion modes and operating conditions. The experimental results indicate that with increasing the lambda, the torque of the test engine can maintain the target value under equivalent ratio and lean combustion modes, while it sharply decreases under ultra-lean combustion. When the value of the lambda is 1.5, the brake thermal efficiency (BTE) and brake specific hydrogen consumption (BSHC) of the HRG SI engine respectively reach up to 33.6% and 89.1 g/kW.h, yielding superior fuel economy. The NOx emissions of the HRG SI engine firstly increase and then decrease with increasing lambda, reaching the peak value at the lambda of 1.1. In addition, the NOx, CO, and HC emissions of the HRG SI engine greatly reduce when the lambda is approximately 1.5. Consequently, using HRG partially substitute the traditional fossil gasoline in the SI engine can benefit to achieve energy conservation and emissions reduction in transportation section.

Bioinspired smart dual-layer hydrogels system with synchronous solar and thermal radiation modulation for energy-saving all-season temperature regulation

All-season thermal management with zero energy consumption and emissions is more crucial to global decarbonization over traditional energy-intensive cooling/heating systems. However, the static single thermal management for cooling or heating fails to self-regulate the temperature in dynamic seasonal temperature condition. Herein, inspired by the dual-temperature regulation function of the fur color changes on the backs and abdomens of penguins, a smart thermal management composite hydrogel (PNA@H-PM Gel) system was subtly created though an “on-demand” dual-layer structure design strategy. The PNA@H-PM Gel system features synchronous solar and thermal radiation modulation as well as tunable phase transition temperatures to meet the variable seasonal thermal requirements and energy-saving demands via self-adaptive radiative cooling and solar heating regulation. Furthermore, this system demonstrates superb modulations of both the solar reflectance (ΔR = 0.74) and thermal emissivity (ΔE = 0.52) in response to ambient temperature changes, highlighting efficient temperature regulation with average radiative cooling and solar heating effects of 9.6 °C in summer and 6.1 °C in winter, respectively. Moreover, compared to standard building baselines, the PNA@H-PM Gel presents a more substantial energy-saving cooling/heating potentials for energy-efficient buildings across various regions and climates. This novel solution, inspired by penguins in the real world, will offer a fresh approach for producing intelligent, energy-saving thermal management materials, and serve for temperature regulation under dynamic climate conditions and even throughout all seasons.

A review of the application development and key technologies of rotary engines under the background of carbon neutrality

Rotary engines possess advantages such as light weight, high power density, and strong fuel adaptability, which are essential for powertrain systems. They hold great potential in the field of power machinery. However, the development of rotary engines has been constrained by increasingly stringent emission regulations. Despite this, numerous scholars and institutions continue to optimize rotary engines, leading to a proliferation of related research. It is necessary to synthesize these findings to guide future promotion and application efforts. This review examines the current applications and development status of rotary engines globally. It summarizes the development of key technologies such as sealing techniques, rotor configurations, and ignition devices, which have achieved lower energy consumption and emissions. The suitability of rotary engines for automotive and unmanned aerial vehicle applications has also been reported. To further reduce carbon emissions, researchers have explored zero-carbon fuel rotary engines. Hydrogen-fueled rotary engines have demonstrated higher combustion efficiency. By integrating EGR, load control strategies, and ignition system layouts, they have addressed issues such as NOx emissions, knock, and leakage. The development of ammonia-hydrogen hybrid fuels is also a potential effective solution. Continuous innovative research indicates that, with their inherent advantages and the integration of alternative fuels and advanced ignition technologies, rotary engines have the potential to meet future energy and environmental requirements. This positions them as strong contenders in future diversified power systems.

Experimental study on fracture properties of heat-treated granite in I-II mixed mode suffered from water and liquid nitrogen cooling methods

Hot dry rock (HDR) development is significant for solving energy problems and realizing energy conservation and emission reduction. Liquid nitrogen (LN2) fracturing and hydraulic fracturing can form cracks in the HDR and improve the efficiency of geothermal energy mining. Therefore, it is necessary to study the fracture characteristics of high-temperature granite under different cooling methods. In this study, the deterioration of the physical and mechanical properties of granite subjected to high-temperature treatment under water cooling and LN2 cooling was studied. Two I/II mixed modes (tensile orientation mode and shear orientation mode) and a pure II mode fracture characteristics of cracked straight-through Brazilian disc (CSTBD) specimens made of granite were explored. The displacement and strain fields of cracked granite specimens were measured by using a digital image correlation (DIC) method. The results show that when the temperature is 25℃, 200℃, and 400℃, the loading angle and cooling method have a great influence on the fracture mechanical characteristics of the granite. In general, the increase of loading angle and LN2 cooling will lead to the decrease of peak load. For example, at 200℃, β = 15°, the deterioration degree of water-cooled and LN2-cooled specimens is 13.77 % and 16.69 %, respectively. When β = 23°, it increases to 14.62 % and 19.91 %, respectively. At the same time, an interesting phenomenon was found in the study. At 400℃, due to the Leidenfrost effect, the peak load of LN2-cooled specimens was higher than that of water-cooled specimens, and the further increase of temperature weakened the effect. When the temperature is 600℃, the difference between the loading angle and the cooling method is weakened.

Retrofitting of the Italian precast industrial building stock. LCA analysis as decision-making tool

The challenging European goal concerns the achievement of a carbon-free economy by 2050 must necessarily consider the improvement of the construction sector along with the redevelopment of the existing buildings. In fact, according to a European report concerning existing buildings, 80% of the built environment will remain the same by 2050. Nowadays buildings are responsible for 30% of the global final energy consumption and 37% of global energy and process emissions. As regards industrial buildings, they account for 33% of global final energy demand. The research aims to define some effective retrofitting measures to be applied in the existing Italian industrial facilities to improve their both energy and environmental performance. Several redevelopment scenarios and their possible combinations related to external envelope technological solutions and systems are analysed. The different interventions are applied to an existing Italian industrial building chosen as a case study and are compared by Life Cycle Assessment (LCA) analysis considering different environmental indexes (GWP, AP, PERT and PENRT). The calculation of the primary energy demand is also performed. Phase B6 accounts for about 80% of the whole environmental impact calculated considering 20 years as building lifetime. The redevelopment scenarios that foresee the intervention on the external envelope are the most impactful ones in terms of the production phase (A1-A3). Those related to the existing roof are characterised by a significant impact due to the demolition, disposal and landfill of finishing panels made of asbestos. The configurations that include the substitution of the existing heating system with an air-to-air heat pump coupled with renewables to produce electrical energy on-site are the most advantageous ones. The latter make the industrial building carbon-zero thanks to the considerable amount of surplus electrical energy produced on-site. In this case, phase D mostly recovers the environmental impact of the whole life cycle considering the different indexes.

Investigating carbon peak prediction scenarios and implementation strategies in higher education institutions: A case study of Shandong Jianzhu university

The potential for energy conservation and emission reduction in Chinese universities is significant. The exploration of scenarios for predicting carbon peaking and identifying implementation pathways can offer valuable insights and demonstrations for achieving dual carbon goals. This paper develops a research methodology for carbon peaking in universities, with a focus on four main aspects: carbon emission accounting, the analysis of influencing factors, carbon emission prediction, and implementation pathways for carbon peaking. By utilizing the campus of Shandong Jianzhu University as a case study, the paper practically demonstrates the prediction of carbon peaking scenarios and offers technical solutions for universities. The results demonstrate that: (1) Using the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the influencing factors of carbon emissions in universities, we confirm that the energy structure effect, economic effect, and population effect have a promoting impact on the increase of carbon emissions at Shandong Jianzhu University, while the energy intensity effect exhibits a significant inhibitory effect. (2) By employing Partial Least Squares (PLS) regression analysis of annual carbon emission statistics from universities, we construct a STIRPAT model with high predictive accuracy for carbon emission regression. The carbon emission prediction model for Shandong Jianzhu University is: lnC^t=-25.899+3.571lnPt+0.966lnARt+0.716lnTMt-0.279lnTEt (3) Both the low-carbon scenario and the enhanced scenario for Shandong Jianzhu University are capable of achieving carbon peaking by 2027. From the perspectives of energy-saving renovation of existing building envelopes, improving the efficiency of lighting systems, utilizing solar photovoltaic buildings, and electrifying cooking energy, implementation pathways for carbon peaking under low-carbon and enhanced scenarios are delineated

Novel high-efficiency solid particle foam stabilizer: Effects of modified fly ash on foam properties and foam concrete

The preparation of foam concrete frequently encounters challenges such as foam collapse and stratification, which lead to a decline in material performance. Therefore, enhancing foam stability is paramount in the production of foam concrete. This study innovatively addresses this issue by investigating the use of waste fly ash particles (RFA) and modified fly ash (AMFA, BMFA, and CMFA) as foam stabilizers, and comparing their efficacy with that of traditional nano-silica stabilizers (NS), both independently and in combination. The results indicate that modified ultrafine fly ash particles (AMFA) exhibit foam stability properties (1-h settlement distance and bleeding rate) comparable to those of NS. Moreover, when combined with NS, the mixture surpasses the foam performance of NS alone. Utilizing these highly stable foams, lightweight foam concrete with a 600 kg/m³ density is produced, demonstrating exceptional mechanical properties (compressive strength of 3.42 MPa) and superior thermal insulation (thermal conductivity of 0.0914 W/m· K). The enhanced foam stability of the modified fly ash is primarily attributed to increased surface roughness, hydrogen bonding, and van der Waals forces. Developing highly stable foams holds significant potential, contributing to energy conservation, emissions reduction, and waste management.

Assessing economic efficiency of driving behavior within traffic flow dynamics

The widespread adoption of electric vehicles has a positive impact on energy conservation and emission reduction, significantly contributing to sustainable development. This paper proposes an economic evaluation method for drivers’ driving behaviors based on actual operational data of pure electric vehicles. This method aims to eliminate the influence of traffic conditions on the evaluation results, ensuring that the outcomes solely reflect the economic efficiency level of drivers’ behaviors. Initially, short trips are identified and traffic conditions are classified and identified based on real vehicle data. A random forest identification model is established to recognize the traffic conditions of short trips in real-time. Subsequently, characteristic variables are selected and calculated. Pearson correlation analysis wand Spearman correlation analysis are utilized to identify variables strongly correlated with energy consumption and traffic conditions, which are then used as evaluation indicators. Finally, eco-scores are calculated using the energy consumption per unit mileage for short trip segments under different traffic conditions. A neural network model is established and trained between the evaluation indicators and eco-scores, enabling real-time evaluation of the economic efficiency of drivers’ driving behaviors. The evaluation method proposed in this paper can objectively assess the economic efficiency of driving behavior, facilitating drivers to improve their driving practices based on their evaluation results.

Responding to green finance with emission reduction and value-added: The role of enterprise environmental investment

Responding to China’s green finance policy with enterprise environmental investment (EEI) constitutes a crucial link in achieving environmental governance objectives, exerting crucial influence on the nation’s green transformation and high-quality development. Taking the pilot policy of China’s Green Finance Reform and Innovation Experimental Zone (GFRP) in 2017 as a quasi-natural experiment, this study systematically evaluates the effect of GFRP policy on the decisions of EEI and further explore whether the investments are used for passive end-of-pipe treatment (EOP) or positive source prevention (SP) by using firm-level data. The results indicate that GFRP policy can significantly promote EEI, and mainly reflected in SP, rather than EOP. Through potential mechanism analysis, it can be concluded that GFRP policy facilitates EEI by alleviating financial constraint, reducing agency cost, and enhancing environmental information disclosure. Heterogeneity analysis suggests that there exists asymmetry in policy effects, with greater impacts in high green finance development areas, low concentration industries and large-scale enterprises. Furthermore, micro-level performance consequences examination reveals that the enterprises’ decision to increase EEI under GFRP policy not only effectively realizes energy conservation and emission reduction but also contribute to facilitating enterprise value, to achieve green transformation. This study holds significant policy implications, providing empirical evidence to policymakers for the refinement and dissemination of green finance policy, and offering valuable insights for enterprise investment and management decisions.

Bio-Conversion Of Plastic Waste Into Sustainable Biofuels: A Comprehensive Review Of Microbial Degradation Approaches

Plastic's pervasive presence in contemporary society has revolutionized industries and daily life but has concurrently birthed environmental concerns necessitating innovative and sustainable solutions. This review navigates recent strides in plastic-to-fuel conversion, with a particular focus on the transformative potential of microbial degradation. Beginning with a critical assessment of environmental challenges linked to conventional plastic disposal, this review meticulously dissects microbial degradation. Unveiling the intricate processes orchestrated by bacteria and fungi, it delves into the unique advantages this method offers, including reduced land dependency, cost efficiency, and minimal environmental impact, setting it apart from traditional approaches like pyrolysis and gasification. Navigating challenges inherent in plastic-to-fuel conversion, such as energy consumption and emissions, this review synthesizes the latest research, technological breakthroughs, and ongoing initiatives. It culminates in a comprehensive overview of microbial degradation's potential for sustainable plastic waste management and biofuel production. As a synthesis of cutting-edge knowledge, this review not onlyq1 informs current scientific discourse but also charts course for future research, policy, and industrial practices, steering towards a harmonious equilibrium between the benefits of plastic and environmental considerations. This work serves as a beacon for those dedicated to fostering a sustainable future through a nuanced understanding of plastic waste management, environmental sustainability, and the evolving landscape of biofuel production.

EU building stock characterization for whole life cycle assessment: Data challenges and key insights

Abstract The building sector plays an important role in achieving the climate objectives of the EU Green Deal. While prioritizing measures to reduce operational energy and GHG emissions has proven beneficial, it has shifted burdens by increasing embodied emissions. Quantifying and regulating emissions throughout the entire building life cycle is therefore crucial. An ongoing DG GROW project is investigating strategies to reduce life cycle GHG emissions within the EU. Various steps are being carried out to achieve the research goals: identification of data needs and sources, baseline analysis of the existing whole life carbon emissions of the EU building stock, modelling of future scenarios. This paper elaborates on the building stock characterization, demonstrating innovation through its level of granularity. Firstly, key data sources are chosen to provide the desired granularity. Secondly, archetypes are defined based on the data sources. Thirdly, attributes are chosen to describe the building stock in terms of geometry, building element composition, energy use, etc. The paper concludes by discussing challenges related to collecting attribute information and managing data gaps. The insights derived offer valuable recommendations for establishing a future data repository dedicated to environmental LCA of the EU building stock.

Acoustic Emission Energy Release Rate Model for Classification of Damage Development in Large Fiber Reinforced Plastic Composite Structures

Acoustic emission identification of fracture mechanisms in composite materials is of great importance for the practical assessment of the serviceability of aerospace structures, such as unmanned aerial vehicles, rocket motors, and others. Many scientific research studies conducted in recent decades have demonstrated the unique capabilities of the Acoustic Emission (AE) method to detect and identify different failure mechanisms, including fiber breakage, matrix cracking, and delaminations in fracture tests of composite specimens. However, testing large composite structures, in many cases, can be accompanied by multiple time-overlapped fracture events activated simultaneously, resulting in considerable difficulties in the classification and assessment of combined prolonged AE signals. In this work, we propose the Energy Release Rate (ERR) model for the classification of AE activity related to fiber-reinforced plastic (FRP) composite fracture. The model identifies accurately both micro-scopic and macro-scopic fracture mechanisms in large aerospace structures having complex composition of materials. The model is practically applied using several machine learning methods and provides a simple means to identify and discriminate typical fracture mechanisms and their combinations. Specifically, we describe ERR model, the choice of most informative AE parameters, the choice of suitable machine learning classification methods, their training and testing on FRP specimens, and validating the model in practical tests of large full-size FRP composite structures.