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Results for “failure temperature”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Using optical fibers to examine thermal mixing of liquid sodium in a pool-type geometry

Sodium fast reactors (SFR) are poised to be a leading candidate for the next generation of commercial nuclear reactor deployment. Companies are commissioning designs for SFR increasing the need for experimental and computational analysis to improve the economics and safety of these reactors. Analysis of SFR behavior during reactor transients directly informs the understanding of reactor safety. Particularly, investigating the thermal transients associated with the reactor loss of flow transient allows for informed design decisions to be made regarding reactor safety. During loss of flow transients the flow rate of the coolant is significantly reduced, but not necessarily stopped. The reduction in flow rate results in coolant temperatures exiting the reactor core that can be drastically different than the coolant in upper pool. When these different temperature fluids interact, thermally stratified layers can form causing cyclic thermal fatigue on the reactor vessel potentially leading to pre-material failure. Past experimental studies have lacked high resolution temperature measurements. This research focuses on demonstrating the reliability of novel techniques for acquiring high resolution temperature measurements in a scaled liquid sodium facility. Tests are conducted that simulate postulated loss of flow transients and provide unprecedented spatial temperature distribution measurements.

42 ENGINEERING↗
DOE OSTI2025

Impact of molten salt inflow on the temperature distribution in thermal energy storage tanks at startup for central receiver concentrating solar power plants

Concentrating Solar Power (CSP) systems with molten salt thermal energy storage (TES) tanks are one of the most promising, renewable-based energy conversion technologies for larger-scale power generation. The TES tank is one of the most critical components in CSP plants due to its high-temperature operation (up to 565 °C), daily thermal cycling, and intermittent solar radiation conditions. The plant startup is one of the most challenging operation conditions that could lead to damaging thermal gradients due to low salt inventory levels. In this study an analytical model for the sparger ring was developed and integrated with a detailed computational fluid dynamics model of a commercial-scaled molten salt tank. The integrated model allows an accurate representation of the tank operation to evaluate the effect of molten salt inflow on the mixing process. The tank filling process during plant startup was analyzed considering sparger rings with variations in design features, including inlet orifice configurations, number of orifices, direction of the inlets, and orifice diameter. The results demonstrated that higher temperature gradients are obtained in the tank floor during the plant startup. A sparger ring configuration with a predetermined orifice inlet inclination (30°, 45° and 60°) leads to significant temperature differences in the floor, between 57 °C and 62 °C, but better homogeneity in the temperature of the salt inventory. Lower salt inflow velocities result in a more homogeneous floor temperature, with maximum temperature differences under 38 °C. The sparger ring configuration with 52 orifices of 1-in. diameter and vertical flow showed better homogeneity in the temperature differences as a function of the salt level and lower temperature gradients in the tank floor. Because large temperature gradients in the tank's floor have been identified as one of the main factors contributing to tank failures, assessing various sparger ring design features is fundamental to determining proper inflow conditions that lead to low-temperature gradients and reducing failure susceptibility.

14 SOLAR ENERGY↗
NASA NTRS1977

Cyclic and low temperature effects on microcircuits

Cyclic temperature and low temperature operating life tests, and pre-/post-life device evaluations were used to determine the degrading effects of thermal environments on microcircuit reliability. Low power transistor-transistor-logic gates and linear devices were included in each test group. Device metallization systems included aluminum metallization/aluminum wire, aluminum metallization/gold wire, and gold metallization/gold wire. Fewer than 2% electrical failures were observed during the cyclic and low temperature life tests and the post-life evaluations revealed approximately 2% bond pull failures. Reconstruction of aluminum die metallization was observed in all devices and the severity of the reconstruction appeared to be directly related to the magnitude of the temperature excursion. All types of bonds except the gold/gold bonds were weakened by exposure to repeated cyclic temperature stress.

Weissflug, V. A.↗
NASA NTRS2016

Yield and Failure Behavior Investigated for Cross-Linked Phenolic Resins Using Molecular Dynamics

Molecular dynamics simulations were conducted to fundamentally evaluate the yield and failure behavior of cross-linked phenolic resins at temperatures below the glass transition. Yield stress was investigated at various temperatures, strain rates, and degrees of cross-linking. The onset of non-linear behavior in the cross-linked phenolic structures was caused by localized irreversible molecular rearrangements through the rotation of methylene linkers followed by the formation or annihilation of neighboring hydrogen bonds. The yield stress results, with respect to temperature and strain rate, could be fit by existing models used to describe yield behavior of amorphous glasses. The degree of cross-linking only indirectly influences the maximum yield stress through its influence on glass transition temperature (Tg), however there is a strong relationship between the degree of cross-linking and the failure mechanism. Low cross-linked samples were able to separate through void formation, whereas the highly cross-linked structures exhibited bond scission.

Molecular dynamics↗
NASA NTRS2016

Yield and Failure Behavior Investigated for Cross-linked Phenolic Resins Using Molecular Dynamics

Molecular dynamics simulations were conducted to fundamentally evaluate the yield and failure behavior of cross-linked phenolic resins at temperatures below the glass transition. Yield stress was investigated at various temperatures, strain rates, and degrees of cross-linking. The onset of non-linear behavior in the cross-linked phenolic structures was caused by localized irreversible molecular rearrangements through the rotation of methylene linkers followed by the formation or annihilation of neighboring hydrogen bonds. The yield stress results, with respect to temperature and strain rate, could be fit by existing models used to describe yield behavior of amorphous glasses. The degree of cross-linking only indirectly influences the maximum yield stress through its influence on the glass transition temperature (Tg), however there is a strong relationship between the degree of cross-linking and the failure mechanism. Low cross-linked samples were able to separate through void formation, whereas the highly cross-linked structures exhibited bond scission.

Stress-strain curves↗
DOE OSTI2021

$\mathrm{REX}$: An analytical tool for reactor operating envelope expansion through fuel-clad thermo-mechanics

Here REX, an analytical reactor design tool for the identification and expansion of reactor operating envelopes, is introduced and detailed. Written in MATLAB, REX takes core geometries and axially discretized detector and depletion output from the Serpent neutron transport code and performs full-core, pin-specific thermal-hydraulic and thermo-mechanical calculations. REX identifies the operating envelopes of advanced, solid-fuel nuclear reactors on the basis of their fuel-cladding, fuel-gas, and coolant-cladding interfacial thermo-mechanics. It iteratively pushes coolant inlet temperatures and modifies fuel pin geometries to induce mechanical failure in their original cladding materials at the fullest extent of their fuel cycle lengths and permissible coolant inlet temperatures. It then attempts to expand their operating envelopes by determining the mechanical responses of alternative cladding materials under the same geometric conditions. The result of the REX calculation sequence is a set of 5-D variables describing the temperatures, pressures, geometries, and mechanics of the core as functions of assembly, fuel pin, axial zone, depletion step, and coolant inlet temperature for each candidate cladding material with the limiting fuel pin geometry for the original material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Economic Analysis of Condition-Monitoring-Based Predictive Maintenance in Power Plants under Market Elasticity

Condition-monitoring-based predictive maintenance can increase the power plant availability by preventing forced outages. However, the actual on-stream time depends on market elasticity and dynamics, which are affected by cost and penetration of other power generation technologies. This paper develops a systematic approach for the economic analysis of investment in condition monitoring technologies with due consideration of market elasticity. Focus is on corrosion monitoring in coal-fired power plants (CFFPs) since corrosion in high-temperature coal-fired boilers is a leading cause of equipment failure. Investment in sensor networks for measuring corrosion and operating conditions like metal temperature and concentrations of O 2 and SO 2 is investigated. The unscented Kalman filter is used to estimate corrosion in the waterwall section of the boiler under multiple sensor networks. Electricity produced by CFPPs in the future in the U.S. due to changes in availability under market elasticity is studied. Sensitivity of the incremental net present value to factors like the number, type, and cost of sensors is analyzed.

Electrochemistry↗
DOE OSTI2020

Development of Thermal Tomography Imaging System for In-Service Nondestructive Evaluation of Additively Manufactured Components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin. MIT research reactor (MITR) has been identified as a representative environment to perform investigation of feasibility of in-service NDE during shutdown. Preliminary study of radiation level in the MITR viewport, with geometric parameters potentially compatible with compact TT system, has been conducted.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

NEAMS Model Contributions in 2023 to the National Reactor Innovation Center Virtual Test Bed for Use by Industry and Other Stakeholders

The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. This work documents NEAMS efforts to support industry adoption of advanced modeling tools through contribution of 10 NEAMS models to the NRIC Virtual Test Bed including models for the High Temperature Test Facility (HTTF), TRISO fuel failure in a microreactor, and multiphysics models of a molten chloride fast reactor, among others. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. In particular, the HTTF model is being used for code validation and benchmarking activities. The microreactor and molten chloride fast reactor models are representative of analysis that may be useful for current candidates of DOME and LOTUS, NRIC’s physical testbeds. This report summarizes and provides links to these new models, among others.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

09 BIOMASS FUELS↗
NASA NTRS1973

Low temperature mechanical properties, fractographic and metallographic evaluation of several alloy steels

The mechanical properties are presented of alloy steels, 4130, 4140, 4340, 6150, and 8740. Test specimens were manufactured from approximately 1.00 inch (2.54 cm) diameter bar stock which had been heat treated to two different hardness levels. The following mechanical tests were performed at temperatures of 80 F (+26.7 C), 0 F (-17.8 C), -100 F (-73 C), and -200 F (-129 C): (1) tensile test (Ultimate, yield, modulus, elongation, and reduction of area), (2) notched tensile test, (3) charpy V-notched impact test (impact energy), and (4) double shear strength test (ultimate and yield). The test data indicate excellent tensile strength, notched/unnotched tensile ratios, ductility, impact, and shear properties at all test temperatures, except at -200 F (-129 C) where the impact strength of the higher strength group of alloy steels, 4130 (Rc-37) and 4140 (Rc-44) decreased to approximately 9 ft. lbs. (12 joules) and 6 ft. lbs. (8 joules), respectively. Chemical, metallographic, and fractographic analyses were also performed to evaluate microstructure, microhardness and the effect of decrease in temperature on the ductile to brittle failure transition.

Montano, J. W.↗
NASA NTRS1979

Diode step stress program for JANTX1N2970b

Data for the purpose of evaluating the effect of power/temperature step stress was studied when applied to the zener diode JANTX1N2970B manufactured by Siemens and General Semiconductor, Inc. A total of 48 samples from each manufacturer was submitted to the process. In addition, two control sample units were maintained for verification of the electrical parametric testing. The tests for determining power/temperature stresses are described, and test results are discussed. Failure analyses for power stress, and temperature stress are presented.

Source record↗
NASA NTRS1979

Diode step stress program for JANTX1N5415

Diode JANTX1N5415 was tested to evaluate the reliability of these devices from different manufacturers, and to gain more knowledge of stress testing. The tests for determining power/temperature step stresses are described, and the test results are discussed. Failure analyses for power stress, and temperature stress are presented.

Source record↗
NASA NTRS1979

Transister step stress testing program for JANTX2N3811

The effect was studied of power/temperature step stress when applied to the dual transistor JANTX2N3811 manufactured by Motorola and National Semiconductor. A total of 48 samples from Motorola and 47 from National Semiconductor was submitted to the processes. In addition, two control sample units were maintained for verification of the electrical parametric testing. The tests for determining power/temperature stresses are described, and test results are discussed. Failure analyses for power stress, and temperature stress are presented.

Source record↗