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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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133 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Impact of edge harmonic oscillations on the divertor heat flux in NSTX

Previously, ELM-free and inter-ELM divertor peak heat flux reduction induced by an edge harmonic oscillation (EHO) was observed in NSTX (K.F. Gan et al., 2017 Nucl. Fusion 57 126053). This paper introduces new analysis of the EHO impact on the divertor heat flux. It was found that enhanced edge turbulence significantly altered the divertor heat flux footprint in NSTX H-modes, relative to ELM-free discharges. When the background edge fluctuation level was low, the EHO significantly increased the heat flux width and decreased the divertor peak heat flux. When the background edge fluctuation level was high, the EHO actually increased the divertor peak heat flux. As a result, it was also found that the heat flux width increased with increasing frequency of the EHO.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Integrated control of edge localized modes and divertor flux using mixed toroidal harmonic resonant magnetic perturbations in EAST

Mixed harmonic resonant magnetic perturbations (RMPs) for integrated edge localized modes (ELMs) and divertor flux control are demonstrated in EAST target plasmas of low input torque and normalized beta β N ~1.7–1.9, which are close to the equivalent value in ITER high Q operation. The applied RMPs are designed to combine a static harmonic of the toroidal mode number n = 3 with a static or rotating harmonic of n = 2. ELM suppression is achieved without a drop of plasma energy confinement, and tungsten concentration is effectively reduced during the application of RMPs. With mixed harmonics, the toroidal varying steady state heat and particle fluxes on the divertor target can be modified with the rotating n = 2 harmonic, which agrees with the numerical modeling of three-dimensional magnetic topology, with plasma responses being taken into account. ELM suppression correlates with the times of larger n = 3 response with mixed n = 2 and n = 3 RMPs. The mixture of harmonics and the rotating n = 2 harmonic does not require additional coil current because the variation is only in the upper-lower coil current phase space. Furthermore, these results further affirm the effectiveness of integrated ELM and divertor flux control using RMPs with mixed harmonics and improve the understanding of the role of plasma responses in ELM suppression.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Simplified Model and Approach to Transform Infrared Surface Temperature to Film Effectiveness in a Conjugate Heat Transfer Experiment

In the pursuit of more efficient gas turbines, film cooling is a critical technology. This article describes a simplified engineering model based on a one-dimensional thermal resistance network. The model is used to relate film-cooling effectiveness and heat transfer augmentation to local overall cooling effectiveness in a conjugate flat plate experiment. Here, this article presents experimental proof-of-concept data to demonstrate the potential for this model. In contrast to previous approaches, neither the wall heat flux nor the adiabatic wall temperature is required to estimate the local film-cooling performance parameters. The model predicts surface temperatures that are within the experimental uncertainties over the range for which the model is trained and to within five percent when the model is extrapolated to higher coolant channel Reynolds numbers. This article is relevant to conjugate test rigs that can measure the hot-surface temperature distribution with and without film cooling. This information may also be relevant to designers as a method to approximate surface temperatures or used as an approximate heat transfer model for optimization studies.

advanced turbines↗
DOE OSTI2022

Infrared-Fused Vision-Based Thermoregulation Performance Estimation for Personal Thermal Comfort-Driven HVAC System Controls

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than 2% over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Scalable and Quench-Free Processing of Metal Halide Perovskites in Ambient Conditions

With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth-abundant materials; however, scalability, stability, and durability remain key challenges. In order to transition from small-scale processing in inert environments to higher throughput processing in ambient conditions, the fundamentals of perovskite crystallization must be understood. Classical nucleation theory, the LaMer relation, and nonclassical crystallization considerations are discussed to provide a mechanism by which a gellan gum (GG) additive—a nontoxic polymeric saccharide—has enabled researchers to produce quality halide perovskite thin-film blade coated in ambient conditions without a quench step. Furthermore, we report on the improved stability and durability properties inherent to these films, which feature improved morphologies and optoelectronic properties compared to films spin-coated in a glovebox with antisolvent. We tune the amount of GG in the perovskite precursor and study the interplay between GG concentration and processability, morphological control, and increased stability under humidity, heat, and mechanical testing. The simplicity of this approach and insensitivity to environmental conditions enable a wide process window for the production of low-defect, mechanically robust, and operationally stable perovskites with fracture energies among the highest obtained for perovskites.

14 SOLAR ENERGY↗