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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sensing Modalities Deployed - Acoustic

Acoustic approaches to OLM are based on analyzing changes in various forms of pressure waves propagating in solids, fluids or gases. These pressure waves can vary in frequency content from infrasound (seismic events) to the megahertz region (acoustic emissions). A wide variety of commercially available pressure or stain sensing devices are used to make acoustic measurements. These sensors differ in the transduction mechanism used to convert the pressure or strain signal to an electrical output, the sensitivity, dynamic range, bandwidth, directional characteristics, and physical size. All acoustic sensors must be integrated with signal conditioning electronics to convert the sensors’ analog output to discrete digital signals that can then be further analyzed for OLM. Ultimately, the sensing properties listed above will be a function of the sensor transduction mechanism, the sensor design and the conditioning electronics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Method and system to measure gas flow

A system for measuring gas flow generally including a passive acoustic wave generator disposed in a gas flow stream to passively generate an audio signal through vortex shedding, a sound capturing instrument disposed outside the gas stream to produce an electrical signal representative of the acoustic signal, a temperature sensor to obtain temperature measurements indicative of the temperature of the gas flow stream and a control system for determining the gas flow, such as velocity or flow rate, as a function of the acquired acoustic and temperature measurements. The acoustic wave generator includes a corrugated flow channel whose geometric design is so tuned to generate an acoustic emission whose frequency signature varies as a function of the gas flow velocity. The control system may acquires time-domain acoustic data, and process that data to obtain a frequency-domain representation from which gas velocity or gas flow rate can be determined.

Cetiner, Mustafa S.↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Aeroacoustic Assessment of Wind Plant Controls

The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.

17 WIND ENERGY↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
DOE OSTI2020

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

58 GEOSCIENCES↗
DOE OSTI2020

Distributed fiber sensing systems for 3D combustion temperature field monitoring in coal-fired boilers using optically generated acoustic waves (Final Report)

In this project, we have developed and tested three kinds of fiber optic sensing systems for real time monitoring of temperature variations within an industrial scale boiler furnace. The fiber optic sensing systems target spatial and temporal distributions of high temperature profiles in a boiler furnace in fossil power plants. The reconstructed temperature profile will provide critical input for the control mechanisms to optimize the combustion process. This temperature profile will address the essential problem for fossil power plants in achieving higher efficiency and fewer pollutant emissions. Acoustic pyrometer systems have been used to reconstruct temperature field of power plant boilers based on measuring TOF (times-of-flight) of sound waves along some straight paths in a 2D cross-section of the boiler. In this project, optically generated acoustic signals from a fiber optic sensing system have replaced the acoustic signals generated from an electrical transducer. A 3D reconstruction algorithm replaced the previous 2D model. In this project, three kinds of fiber optic sensing systems have been developed and tested. They are fiber optic sensing system I, fiber optic sensing system II (Distributed Sensing System I) and fiber optic sensing system III (Distributed Sensing System II). For fiber optic sensing system I, the fiber optic ultrasound generator acts as a signal generator. A microphone, hydrophone or other electronic devices serve as a signal receiver. In this system, there are one generator and one receiver. Distance test, water temperature test, air temperature test, air temperature reconstruction, and GE ISBF pilot test were performed by Fiber optic sensing system I. The fiber optic sensing system I successfully detected temperature in all these tests. We got 2D temperature reconstruction results by using the fiber optic sensing system I and it matched the reference data. The fiber optic sensing system I successfully survived in GE ISBF boiler environment (480 °F). For fiber optic sensing system II (Distributed Sensing System I), it is an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Fiber Bragg Grating (FBG) and Fabry-Perot (FP) sensor act as a signal receiver. In this system, there is one generator and one receiver. Aluminum plate temperature test, furnace high temperature test, and GE ISBF pilot test were performed by the fiber optic sensing system II. Fiber optic sensing system II successfully detected the temperature in all these tests. The fiber optic sensing system II successfully survived at up to 700 °C furnace environment and 320 °C GE ISBF boiler environment. For fiber optic sensing system III (Distributed Sensing System II), it is also an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Multiple FP fiber sensors act as signal receivers. In this system, there is one generator and three receivers. Three GE ISBF pilot tests were performed by fiber optic sensing system III. The fiber optic sensing system III survived in the cold flow tests in GE’s ISBF pilot test facility. However, we didn’t get high temperature data by using this system since the nanosecond laser issues. During the period of the project, test trials, data simulation and algorithm optimization was performed successfully. For real time temperature field construction, the sampling rate must be fast enough to capture the field variations. The technology of Code-division multiple access (CDMA) is well studied which could allow parallel multiplexing, even if signals overlap in time or frequencies. Moreover, it has been known that extending the length of signal significantly improves SNR. For acoustic signals, these multiplexing techniques have also been widely used, mainly for sonar and acoustic communications. The CDMA modulation technique has been proposed and studied to guarantee high network throughput, low channel access delay and low energy consumption. We have studied the temperature field reconstruction using Gaussian Radial Basis Functions (GRBF)-based approximation approach. Reconstruction of 3D temperature field using Neural Networks with measured TOF and known propagation paths is feasible. 2D and 3D temperature field reconstruction simulation results are achieved. The milestone status is shown in Table 1. We finished milestone 1-8 and milestone 10. For milestone 9, we did three pilot tests by using the fiber optic sensing system III (Distributed Sensing System II) at GE Power. However, due to the failure of the ns laser, we did not get the temperature results. We conducted some additional tasks that were not originally proposed: 1) We fabricated a fiber optic sensing system I and did a pilot test based on this system. 2) In the proposal, we proposed two pilot tests at GE Power. In reality, we finished at least seven pilot tests at GE Power. GE Power has made a lot of efforts for supporting the pilot tests. 3) We got a simulation results based on CDMA. In summary, most of the tasks have been accomplished. The outcome of this project removed a few barriers that hinder the achievement of the final product of the distributed sensing systems. With the successful accomplishment of this project, a prototype of the fiber optic sensing system can be fabricated to attract more interests from companies and other funding agencies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Passive Acoustic Monitoring of a Riverine Turbine with Stationary Hydrophones

In this study, we characterize the sound generated by a cross-flow riverine turbine in the Kvichak River near Igiugig, Alaska, United States. To do this, we follow the International Electro-technical Commission (IEC) technical specification for characterization of the acoustic emissions from marine energy converters. While marine mammals do not inhabit the test site, the U.S. National Marine Fisheries Service (NMFS) guidelines for assessing the effects of anthropogenic sound on marine mammal hearing were implemented to provide context for the deployment of similar turbines in other areas. The results indicate that turbine sound at the measurement locations was not predicted to be harmful to marine mammals. Results also provide insight into the acoustic characteristics of current energy converters and the complex acoustic propagation in rivers.

13 HYDRO ENERGY↗
DOE OSTI2022

The stability of expanding reactive shocks in a van der Waals fluid

Despite the extensive literature accumulated since the pioneering works of D'yakov and Kontorovich in the 1950s, the stability of steady shocks is still an open question when realistic boundary conditions are accounted. The consideration of a supporting mechanism, which is indeed a necessary condition for shock steadiness, modifies the perturbation shock dynamics in the unstable range. The Noh problem is a suitable example to form steady expanding shocks. This configuration is of great interest to the high-energy-density-physics community because of its direct application to inertial confinement fusion and astrophysics, for which the stagnation of a supersonically converging material via an accretion shock front is ubiquitous. Here in this work, we extend the generalized Noh problem, both base-flow solution and linear stability analysis, to conditions where endothermic or exothermic transformations undergo across the shock. Within the spontaneous acoustic emission conditions found for a van der Waals gas, we find that cylindrical and spherical expanding shocks become literally unstable for sufficiently high mode numbers. Counterintuitively, the effect of exothermicity or endothermicity across the shock is found to be stabilizing or destabilizing, respectively.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Evolution of tensile and shear cracking in crystalline rocks under compression

Here we performed laboratory-scale unconfined compression experiments on Barre granite specimen with a single pre-existing flaw to study the evolution of tensile and shear cracking that occurs during different stress levels. Moment tensor inversion of acoustic emission (AE) events in combination with the nonelastic strain component obtained through the 2D digital image correlation (DIC) technique are used to track the changes in the source mechanisms of the stress-induced cracks. The mode of deformation computed from the image based strain profiles, enables visual comparison of the nucleation, growth and interaction of the tensile and shear cracks with the microseismic source mechanism observed by moment tensor inversion of the AE events. Comparing the results obtained from the two techniques on the same dataset demonstrates a quantitative correlation between the acoustic and visual observations in terms of the cracking mechanisms obtained at different stages of cracking from crack initiation at the flaw tips to the failure of the rock specimen.

58 GEOSCIENCES↗
DOE OSTI2026

Fracture Analysis of Cohesive Zone Models for Modeling Residual Stress Induced Delamination in Composite Structures

A fracture study of coupon-scale composite cylinders with embedded defects was conducted with an objective to assess and validate a modeling approach using two available cohesive material models. The study included experimental and simulation evaluations of initiation of crack growth and progression. Interrupted thermal experiments used acoustic emissions monitoring to identify the onset of crack progression during each cooling interval and ultrasonic scanning provided images of defect growth. Verification, validation, and uncertainty quantification (VVUQ) processes were performed in the assessment of the simulation predicted temperature at which crack propagation begins (quantity of interest). The Sobol sensitivity analysis identified the hoop direction elastic modulus in the carbon fiber reinforced polymer (CFRP) plies as the most influential parameter for simulations using both cohesive models, accounting for at least 70% of the variation in the temperature at crack propagation. The UQ temperature range for the Tvergaard-Hutchinson model was higher (more conservative) than the experimental acoustic measurement indicators of crack progression, while the temperature range for the Thouless-Parmigiani model enveloped the experimental data points for the primary defect size of 0.75 x 1 in. The simulations could not capture the stable crack growth indicated in the experiments. This is likely due to the models’ inability to represent anisotropic fracture toughness attributed to the structure of the orthotropic fiber weave in a woven composite laminate.

42 ENGINEERING↗
DOE OSTI2023

Aerosol jet printing of piezoelectric surface acoustic wave thermometer

Abstract Surface acoustic wave (SAW) devices are a subclass of micro-electromechanical systems (MEMS) that generate an acoustic emission when electrically stimulated. These transducers also work as detectors, converting surface strain into readable electrical signals. Physical properties of the generated SAW are material dependent and influenced by external factors like temperature. By monitoring temperature-dependent scattering parameters a SAW device can function as a thermometer to elucidate substrate temperature. Traditional fabrication of SAW sensors requires labor- and cost- intensive subtractive processes that produce large volumes of hazardous waste. This study utilizes an innovative aerosol jet printer to directly write consistent, high-resolution, silver comb electrodes onto a Y-cut LiNbO 3 substrate. The printed, two-port, 20 MHz SAW sensor exhibited excellent linearity and repeatability while being verified as a thermometer from 25 to 200 ∘ C. Sensitivities of the printed SAW thermometer are $$-96.9\times 1{0{}^{-6}}^{\circ }$$ − 96.9 × 1 0 − 6 ∘ C −1 and $$-92.0\times 1{0{}^{-6}}^{\circ }$$ − 92.0 × 1 0 − 6 ∘ C −1 when operating in pulse-echo mode and pulse-receiver mode, respectively. These results highlight a repeatable path to the additive fabrication of compact high-frequency SAW thermometers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

GeoSoilEnviroCARS (Sector 13) at the Advanced Photon Source: a comprehensive synchrotron radiation facility for Earth science research at ambient and extreme conditions

GeoSoilEnviroCARS (GSECARS) is a comprehensive analytical laboratory for Earth and environmental science research using X-ray beams from the Advanced Photon Source, Argonne National Laboratory. State-of-the-art instruments are available for (1) high-pressure/high- or low-temperature diffraction, total scattering, and spectroscopy (Brillouin, Raman, and VIS-IR) using the laser heated diamond anvil cell (DAC); (2) high-pressure/high-temperature diffraction, scattering, and imaging as well as acoustic emission (AE) and ultrasonics using the large-volume press (LVP); (3) powder, single crystal, and surface/interface diffraction; (4) X-ray absorption fine structure spectroscopy; (5) X-ray fluorescence microprobe analysis; and (6) microtomography. As shown here, experiments are facilitated by senior level staff who collaborate on all aspects of the analytical work including experiment design, sample preparation, data collection, data interpretation, and publication preparation. Both technical and scientific synergies occur as a result of the intimate association of the various techniques and scientists experienced in the applications of synchrotron radiation to Earth, environmental, and planetary science problems. The facility includes state-of-the-art instrumentation designed and built in-house, including custom X-ray optics, online and offline laser-based systems, specialized sample environments and positioning systems, as well as pixel-array and multi-crystal energy dispersive X-ray detectors, which are available to be shared among the experimental stations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Micromechanics of Fracture Propagation During Multistage Stress Relaxation and Creep in Brittle Rocks

Time-dependent rock deformation caused by the initiation and growth of fractures leads to the weakening of the rock mass. Understanding the fracturing mechanisms involved in the time-dependent behavior in brittle rocks is very important and to achieve this goal, a systematic series of three types of experiments was performed on double-flawed prismatic Barre granite specimens under unconfined compression. The first series aimed to identify the failure mechanism in the short-term failure mode under monotonic loading, the, second series involved multistage relaxation (constant strain) experiments to analyze the damage at different strain levels, and the third series explored the fracture propagation under multistage creep (constant load) experiments. The spatial and temporal evolution of cracking mechanisms were evaluated using the acoustic emission (AE) and two-dimensional digital image correlation (2D-DIC) techniques to observe the whole crack growth process as well as the accumulated inelastic strain at the specified region of interest. Results suggest that in the case of multistage creep experiments, the time to failure was less compared to the multistage relaxation, when loaded above the crack damage threshold (CD) estimated from the monotonic testing. Furthermore, the frequency magnitude distribution of the AE events generated in the three loading conditions followed the Gutenberg Richter model. A relatively lower b-value was obtained for the creep experiments, indicative of high energy AE events and faster crack growth. In addition, the AE and DIC results also revealed high evolution of tensile cracks at-different stages of creep and relaxation compared to shear and mixed-mode cracks.

58 GEOSCIENCES↗
DOE OSTI2023

Energy Budget of Brittle Fracturing in Granite Under Stress Relaxation and Creep

Creep and relaxation are the two major time-dependent fracturing processes in rocks. While a considerable amount of research has been done in understanding these two mechanisms, critical gaps remain regarding how different energy components evolve during time-dependent fracturing processes in rocks. In this study, a series of relaxation and creep experiments were conducted on prismatic Barre granite specimens in the laboratory to estimate the energy budget of brittle fracturing in granite. For the input energy, the work done by the machine (W) is calculated and for the output energy the radiated seismic energy (E R ), released in the form of acoustic emissions (AEs), is calculated as the only measurable output energy component in the conducted experiments. The low-frequency plateau (Ω 0 ) and corner frequency (f 0 ) for each AE waveform was estimated by fitting the observed AE spectra with the theoretical spectra using the Omega model. These parameters were used to estimate the seismic moments (M 0 ) based on the radiation pattern for the double couple (shear) and non-double-couple (non-shear) events. The range of f 0 and M 0 varied from 150 to 750 kHz and 10 –4 to 10 –1 N m, respectively. Moment magnitude (M w ) varied in a wider range from –9 to –6 in creep and –8.5 to –7 in relaxation. Stress drops (Δ σ ) and source radius (r) were estimated for the AEs using Brune’s model. Here, the results report on three primary observations: (1) the effects of different source mechanisms on the estimated source parameters showed that M 0 and Δ σ were higher for DC events as compared to NDC in both relaxation and creep. (2) The radiation efficiency in the case of creep is 70% higher as compared to relaxation and, (3) the stress drop estimated in relaxation and creep demonstrated a breakdown in scaling with the seismic moment.

58 GEOSCIENCES↗