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Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2024, 146(3): 031021.
Paper No: GTP-23-1392
Published Online: December 1, 2023
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. April 2024, 146(4): 041002.
Paper No: GTP-23-1422
Published Online: December 1, 2023
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2024, 146(3): 031020.
Paper No: GTP-23-1391
Published Online: December 1, 2023
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. April 2024, 146(4): 041001.
Paper No: GTP-23-1388
Published Online: December 1, 2023
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 1 A comparison of conventional and future load profiles for power turbines in load-leveling applications, showing the challenges in transitioning from baseload to flexible operation [ 5 ] More about this image found in A comparison of conventional and future load profiles for power turbines in...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 2 The hybrid methodology enabling features, highlighting the contribution of this paper in gray More about this image found in The hybrid methodology enabling features, highlighting the contribution of ...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 3 The fidelity range and limitations bridged by the hybrid methodology More about this image found in The fidelity range and limitations bridged by the hybrid methodology
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 4 Comparison of the two selection point sample methods. Left: the baseline process, using uniform 3D spacing to maximize the distance between samples. Right: the modified curvature biased method, including the node surface normal in the squared Euclidean distance to allow spatially close but ... More about this image found in Comparison of the two selection point sample methods. Left: the baseline pr...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 5 Histogram comparison of the time averaged surface temperature error when using different equally spaced density inputs (N) to the Universal Kriging model, showing the mean error across all nodes ( μ ) and the three standard deviation range ( ± 3 σ ) covering approximately 99.7% of t... More about this image found in Histogram comparison of the time averaged surface temperature error when us...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 6 Histogram comparison of the time averaged surface temperature error when using different curvature biased density inputs (N) to the Universal Kriging model, showing the mean error across all nodes ( μ ) and the three standard deviation range ( ± 3 σ ) covering approximately 99.7% of... More about this image found in Histogram comparison of the time averaged surface temperature error when us...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 7 3D surface plots showing the time averaged error of the Kriging reconstructed thermal profile, greater than 2 K, calculated using the 512 input spatial only selection method More about this image found in 3D surface plots showing the time averaged error of the Kriging reconstruct...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 8 Example mesh decimation using the research standard Stanford bunny geometry, demonstrating the disadvantage in the poor resolution of the high curvature regions near the ears, eyes, and feet More about this image found in Example mesh decimation using the research standard Stanford bunny geometry...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 9 Schematic of the exhaustive Euclidean nearest neighbor search algorithm, showing the distance calculations for one point of the coarse mesh More about this image found in Schematic of the exhaustive Euclidean nearest neighbor search algorithm, sh...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 10 Schematic of the coordinate-based hash table method, showing the resulting two hash tables with fine point grouping for the localized nearest neighbor search and the final unique solution for the one-to-one nearest node mapping More about this image found in Schematic of the coordinate-based hash table method, showing the resulting ...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 11 The heat transfer coefficient validation case, down-sampling a high fidelity CHT simulation to a lower resolution mesh via the face-centered hash table nearest neighbor method, to extract internal wall HTC components allowing automatic thermal network model construction More about this image found in The heat transfer coefficient validation case, down-sampling a high fidelit...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 12 Barycentric coordinate calculation for interpolation of an internal point in a 2D triangle facet and 3D tetrahedral cell, showing the interpolation point P , along with the weighting area and volume fractions More about this image found in Barycentric coordinate calculation for interpolation of an internal point i...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part I: Novel Autoencoder Methods for the Automated Optimization of Thermal Probes and Fast Sparse Data Reconstruction, Enabling Real-Time Thermal Analysis
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 1 UK power generation by resource as a percentage of demand, scaled to predicted 2050 renewable contribution, showing the net demand residual required for load-levelling in flexible power turbines and UK IC links; alongside the challenges in transitioning power turbines from baseload to flexi... More about this image found in UK power generation by resource as a percentage of demand, scaled to predic...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part I: Novel Autoencoder Methods for the Automated Optimization of Thermal Probes and Fast Sparse Data Reconstruction, Enabling Real-Time Thermal Analysis
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 2 The hybrid methodology enabling features, highlighting the contribution of this paper in gray More about this image found in The hybrid methodology enabling features, highlighting the contribution of ...
Image
in The Hybrid Pathway to Flexible Power Turbines, Part I: Novel Autoencoder Methods for the Automated Optimization of Thermal Probes and Fast Sparse Data Reconstruction, Enabling Real-Time Thermal Analysis
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 3 The fidelity range and limitations bridged by the hybrid methodology More about this image found in The fidelity range and limitations bridged by the hybrid methodology
Image
in The Hybrid Pathway to Flexible Power Turbines, Part I: Novel Autoencoder Methods for the Automated Optimization of Thermal Probes and Fast Sparse Data Reconstruction, Enabling Real-Time Thermal Analysis
> Journal of Engineering for Gas Turbines and Power
Published Online: December 1, 2023
Fig. 4 Outer casing of the representative turbine geometry, unwrapped circumferentially to show the X − Θ surface on which the triangulation is applied More about this image found in Outer casing of the representative turbine geometry, unwrapped circumferent...
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