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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2015

Systematic Tuning and Multifunctionalization of Covalent Organic Polymers for Enhanced Carbon Capture

Porous covalent polymers are attracting increasing interest in the fields of gas adsorption, gas separation, and catalysis due to their fertile synthetic polymer chemistry, large internal surface areas, and ultrahigh hydrothermal stabilities. While precisely manipulating the porosities of porous organic materials for targeted applications remains challenging, we show how a large degree of diversity can be achieved in covalent organic polymers by incorporating multiple functionalities into a single framework, as is done for crystalline porous materials. In this work, we synthesized 17 novel porous covalent organic polymers (COPs) with finely tuned porosities, a wide range of Brunauer–Emmett–Teller (BET) specific surface areas of 430–3624 m 2 g –1 , and a broad range of pore volumes of 0.24–3.50 cm 3 g –1 , all achieved by tailoring the length and geometry of building blocks. Furthermore, we are the first to successfully incorporate more than three distinct functional groups into one phase for porous organic materials, which has been previously demonstrated in crystalline metal–organic frameworks (MOFs). COPs decorated with multiple functional groups in one phase can lead to enhanced properties that are not simply linear combinations of the pure component properties. For instance, in the dibromobenzene-lined frameworks, the bi- and multifunctionalized COPs exhibit selectivities for carbon dioxide over nitrogen twice as large as any of the singly functionalized COPs. These multifunctionalized frameworks also exhibit a lower parasitic energy cost for carbon capture at typical flue gas conditions than any of the singly functionalized frameworks. Despite the significant improvement, these frameworks do not yet outperform the current state-of-art technology for carbon capture. Nonetheless, the tuning strategy presented here opens up avenues for the design of novel catalysts, the synthesis of functional sensors from these materials, and the improvement in the performance of existing covalent organic polymers by multifunctionalization.

mathematical methods↗
DOE OSTI2020

Neutral gas analysis for JET DT operation

Neutral gas analysis, the measurement and evaluation of total and partial pressures, is a key technique to study the impact of neutral gas dynamics on retention, recycling and release processes of fuel or impurity species in fusion devices. At JET, the experiment closest to ITER in terms of operating parameters and size, various detectors and techniques for partial pressure and total pressure measurements are deployed together to characterise neutral gas dynamics during and after plasma operation on various toroidal and poloidal locations. An extensive modification of JET's sub-divertor neutral gas diagnostic system aims at retaining and extending established measurement capabilities in the forthcoming Deuterium-Tritium (DT) experiments (DTE2). To achieve DT compatibility, a separation of radiation-sensitive electronics from the sensor and adequate radiation shielding is required, as well as utilisation of a DT compatible differential pumping system with adjustable throughput to account for the strong pressure variation in the sub-divertor region. Finally, the sub-divertor neutral gas diagnostic will be equipped with multiple Residual Gas Analysers (RGAs), utilising quadrupole mass spectrometry and electrostatic ion-trap-principles, all operating with remote electronics located behind the biological radiation shield. These RGAs will record data in a fast selected discrete mass mode during plasma pulses (cycle ~2 s) and will automatically switch back to continuous data recording (cycle ~100 s) afterwards. They will be complemented by a newly improved Penning gauge spectroscopy configuration in particular supporting the He and D 2 separation relevant for DT operation. The distance between these devices and their associated control unit is typically 15m . A newly developed RGA with a cable length of 80 m, compatible with the ITER environment, will also be employed for the first time. Finally, this set-up and its operation in DTE2 will provide vital input to the development of the ITER divertor RGA in the most relevant environment currently available.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗
DOE OSTI2024

Analytical methods for online data quality assessment

This chapter provides a comprehensive overview of the main steps for algorithmic sensor signal quality assessment, which can enhance the decision-making process for water resource recovery facility (WRRF) operation and optimization. It introduces the concept of redundancy as the basis for data quality assessment. It also explains the typical data processing pipeline, which consists of preliminary analysis, data pre-processing, and specific algorithmic approaches. Each of these processes is presented and discussed in three separate sections. Importantly, this chapter introduces the main approaches for data quality assessment, provides guidelines for selecting the most suitable one and the key performance indicators to evaluate them and explains how to collect metadata through such an algorithmic approach.

Aguado, Daniel↗
DOE OSTI2014

Measure Guideline: Condensing Boilers - Optimizing Efficiency and Response Time During Setback Operation

Conventional wisdom surrounding space heating has told us a couple of things consistently for several years now: size the mechanical systems to the heating loads and setting the thermostat back at night will result in energy savings. The problem is these two recommendations oppose each other. A system that is properly sized to the heating load will not have the extra capacity necessary to recover from a thermostat setback, especially at design conditions. The implication of this is that, for setback to be successfully implemented, the heating system must be oversized. This issue is exacerbated further when an outdoor reset control is used with a condensing boiler, because not only is the system matched to the load at design, the outdoor reset control matches the output to the load under varying outdoor temperatures. Under these circumstances, the home may never recover from setback. Special controls to bypass the outdoor reset sensor are then needed. Properly designing a hydronic system for setback operation can be accomplished but depends on several factors. Determining the appropriateness of setback for a particular project is the first step. This is followed by proper sizing of the boiler and baseboard to ensure the needed capacity can be met. Finally, control settings must be chosen that result in the most efficient and responsive performance. This guide provides step by step instructions for heating contractors and hydronic designers for selecting the proper control settings to maximize system performance and improve response time when using a thermostat setback.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Hydronic Systems: Designing for Setback Operation, Ithaca, New York [Fact Sheet]

Conventional wisdom surrounding space heating has told us a couple of things consistently for several years now: size the mechanical systems to the heating loads and setting the thermostat back at night will result in energy savings. The problem is these two recommendations oppose each other. A system that is properly sized to the heating load will not have the extra capacity necessary to recover from a thermostat setback, especially at design conditions. The implication of this is that, for setback to be successfully implemented, the heating system must be oversized. This issue is exacerbated further when an outdoor reset control is used with a condensing boiler, because not only is the system matched to the load at design, the outdoor reset control matches the output to the load under varying outdoor temperatures. Under these circumstances, the home may never recover from setback. Special controls to bypass the outdoor reset sensor are then needed. Properly designing a hydronic system for setback operation can be accomplished but depends on several factors. Determining the appropriateness of setback for a particular project is the first step. This is followed by proper sizing of the boiler and baseboard to ensure the needed capacity can be met. Finally, control settings must be chosen that result in the most efficient and responsive performance. This guide provides step by step instructions for heating contractors and hydronic designers for selecting the proper control settings to maximize system performance and improve response time when using a thermostat setback.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗
DOE OSTI2018

Measurements toward refined methane emission factors from industrial and commercial natural gas meters

The methane emission factors used in the U.S. EPA Greenhouse Gas Inventory (GHGI) for several categories of equipment in the natural gas supply chain currently have large uncertainties. The purpose of this project was to focus on one of those categories, industrial/commercial customer meters. The potential effect of the uncertainty for this category could be large due to the 5.6 million meters nationwide. We divided the country into six regions and worked extensively with local natural gas distribution companies to gain access to the meters. Ten weeks of measurements were conducted with an additional three weeks spent later revisiting the first sites. Sites were selected pseudo-randomly, by first randomly selecting a location then driving to as many meters as possible during that day, then randomly selecting another starting meter for the next day. At each site, every component was scanned with a combustible gas indicator (CGI), all components were counted, and all leaks with indications above 100ppm were logged. For 3 of the 10 weeks of measurements all leaks above 100ppm were quantified and for the remaining seven only leaks with an indication concentration above 22,500 ppm (45% of lower explosive limit) were quantified. Leaks were quantified using an enclosure, a vacuum pump, and methane sensor (either a CGI or Los Gatos Research Greenhouse Gas Analyzer). Through the initial visits 18,934 components were scanned, resulting in 1,170 leak indications above 100ppm, 351 leaks quantified at 174 different meter sets. The data was collected in a way so that it could be stratified by region, meter type, and component type. The largest number of components scanned were valves (4908) and flanges (4571). Very preliminary analysis of the data indicates that the mean leak size (not by meter) by region was Southeast (0.78 ± 2.2 lpm) Southwest (0.63 ± 1.5 lpm), Rocky Mountain (0.54 ± 1.3 lpm) Northeast (0.14 ± 0.56 lpm), Midwest (0.14 ± 0.56 lpm), and Pacific (0.07 ± 0.56 lpm). We also have determined that of the component types logged, roughly 2 – 5% of all components leaked. Our results are only preliminary, but initial indications are that based on data from this study, the current emission factors could increase by a factor of 5 to 10. Data analysis is ongoing to verify.

03 NATURAL GAS↗
DOE OSTI2016

Collection of low-grade waste heat for enhanced energy harvesting

Enhanced energy harvesting through the collection of low-grade waste heat is experimentally demonstrated. A structural optimization technique is exploited in the design of a thermal-composite substrate to guide and gather the heat emanating from multiple sources to a predetermined location. A thermoelectric generator is then applied at the selected focusing region to convert the resulting low-grade waste heat to electrical power. The thermal characteristics of the device are experimentally verified by direct temperature measurements of the system and numerically validated via heat conduction simulations. Electrical performance under natural and forced convection is measured, and in both cases, the device with optimized heat flow control plus energy harvesting demonstrates increased power generation when compared with a baseline waste heat recovery system. Electronics applications include energy scavenging for autonomously powered sensor networks or self-actuated devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Controlled Fabrication of Quality ZnO NWs/CNTs and ZnO NWs/Gr Heterostructures via Direct Two-Step CVD Method

A novel and advanced approach of growing zinc oxide nanowires (ZnO NWs) directly on single-walled carbon nanotubes (SWCNTs) and graphene (Gr) surfaces has been demonstrated through the successful formation of 1D–1D and 1D–2D heterostructure interfaces. The direct two-step chemical vapor deposition (CVD) method was utilized to ensure high-quality materials’ synthesis and scalable production of different architectures. Iron-based universal compound molecular ink was used as a catalyst in both processes (a) to form a monolayer of horizontally defined networks of SWCNTs interfaced with vertically oriented ZnO NWs and (b) to grow densely packed ZnO NWs directly on a graphene surface. We show here that our universal compound molecular ink is efficient and selective in the direct synthesis of ZnO NWs/CNTs and ZnO NWs/Gr heterostructures. Heterostructures were also selectively patterned through different fabrication techniques and grown in predefined locations, demonstrating an ability to control materials’ placement and morphology. Several characterization tools were employed to interrogate the prepared heterostructures. ZnO NWs were shown to grow uniformly over the network of SWCNTs, and much denser packed vertically oriented ZnO NWs were produced on graphene thin films. Such heterostructures can be used widely in many potential applications, such as photocatalysts, supercapacitors, solar cells, piezoelectric or thermal actuators, as well as chemical or biological sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Redox-Triggered Orientational Responses of Liquid Crystals to Chlorine Gas

Surface-supported liquid crystals (LCs) that exhibit orientational and thus optical responses upon exposure to ppb concentrations of Cl 2 gas are reported. Computations identified Mn cations as candidate surface binding sites that undergo redox-triggered changes in the strength of binding to nitrogen-based LCs upon exposure to Cl 2 gas. Guided by these predictions, μm-thick films of nitrile- or pyridine-containing LCs were prepared on surfaces decorated with Mn 2+ binding sites as perchlorate salts. Following exposure to Cl 2 , formation of Mn 4+ (in the form of MnO 2 microparticles) was confirmed and an accompanying change in the orientation and optical appearance of the supported LC films was measured. In unoptimized systems, the LC orientational transitions provided the sensitivity and response times needed for monitoring human exposure to Cl 2 gas. Finally, the response was also selective to Cl 2 over other oxidizing agents such as air or NO 2 and other chemical targets such as organophosphonates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Feature issue introduction: light, energy and the environment, 2017

The editors introduce the feature issue on 'Energy, Light and the Environment (LEE) 2017', which is based on the topics presented at a congress of the same name held in Boulder, CO, US, from November 6 to November 9. This feature issue presents 13 papers selected from the voluntary submissions by attendees who presented at the progress and have extended their work into complete research articles. Furthermore, the feature issue highlights contributions from authors who presented their research at this congress.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Site Selection for Field Testing of a Marine Hydrokinetic Turbine Platform to Serve as a Floating Unmanned Mobile Recharging Station for Aerial Drones

Abstract Expanding applications of unmanned aerial vehicles (UAVs) in coastal areas suggests a need for stations along or close to the coast where the UAV batteries can be easily recharged. A small-scale low flow marine current turbine in support of a floating autonomous recharge station for small UAVs has been designed and assembled at Florida Atlantic University and is being prepared for field testing. Surveys of identified test sites in the Intracoastal Waterways and at open ocean locations are being conducted to determine their suitability in meeting resource, system design and permitting requirements. A sensor platform has been developed for measuring local currents at the identified sites in support of resource characterization. Two out of the eight sites surveyed in the Intracoastal Waterways are found to meet the field-testing requirements for current resource, water depth, and sea bottom conditions. Planned field testing will include parametric characterization studies of the performance of the current turbine for a range of turbine blade configurations, current speeds, turbine wheel submergence levels, and test site location (channeled flow vs open ocean flow).

McKinney, Adriana Lynell↗
DOE OSTI2014

Measure Guideline: Condensing Boilers-Optimizing Efficiency and Response Time During Setback Operation

Conventional wisdom surrounding space heating has consistently stated two things: size the mechanical systems to the heating loads, and setting the thermostat back at night will result in energy savings. The problem is these two recommendations oppose each other. A system that is properly sized to the heating load will not have the extra capacity necessary to recover from a thermostat setback, especially at design conditions. The implication of this is that, for setback to be successfully implemented, the heating system must be oversized. This issue is exacerbated further when an outdoor reset control is used with a condensing boiler, because not only is the system matched to the load at design, the outdoor reset control matches the output to the load under varying outdoor temperatures. Under these circumstances, the home may never recover from setback. Special controls to bypass the outdoor reset sensor are then needed. Properly designing a hydronic system for setback operation can be accomplished but depends on several factors. The first step is to determine the appropriateness of setback for a particular project. This is followed by proper sizing of the boiler and baseboard to ensure the needed capacity can be met. Finally, control settings must be chosen that result in the most efficient and responsive performance. This guide provides step-by-step instructions for heating contractors and hydronic designers for selecting the proper control settings to maximize system performance and improve response time when using a thermostat setback.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Laboratory Evaluation of the Thermal Breakout Method for Maximum Horizontal Stress Measurement

Measuring in situ stress is essential for many problems in geomechanics, and the maximum horizontal stress is the most difficult to constrain. We are developing an extension of the breakout method to measure maximum horizontal stress in regions where natural breakouts do not occur. In the novel thermal breakout method, additional compression which leads to breakout development is induced by heating the borehole wall. In the present study, we validated the method experimentally in a true-triaxial apparatus on samples with predrilled boreholes. Two rocks were selected for laboratory testing: high-porosity Berea sandstone and low-porosity Niagaran dolomite. Prior to main true-triaxial tests, we carried out standard testing to characterize the strength, elasticity and thermal properties. The true-triaxial experiments consisted of: (1) room-temperature tests where samples were first loaded mechanically until the breakout formed, and (2) elevated-temperature tests where samples were loaded mechanically within the elastic range with additional compression induced thermally. Breakout initiation was monitored by acoustic emission sensors mounted on the pistons that applied horizontal stresses. Furthermore, the magnitude of induced thermal stress was calculated from temperature measurements around the borehole wall. In both rock types, we created thermally induced breakouts and examined analytical expressions to constrain maximum horizontal stress based on strength, elastic and thermal properties of the rocks.

58 GEOSCIENCES↗