Christopher J. Bay

1.9k total citations
63 papers, 1.1k citations indexed

About

Christopher J. Bay is a scholar working on Aerospace Engineering, Environmental Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Christopher J. Bay has authored 63 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Aerospace Engineering, 26 papers in Environmental Engineering and 20 papers in Electrical and Electronic Engineering. Recurrent topics in Christopher J. Bay's work include Wind Energy Research and Development (42 papers), Wind and Air Flow Studies (26 papers) and Fluid Dynamics and Vibration Analysis (15 papers). Christopher J. Bay is often cited by papers focused on Wind Energy Research and Development (42 papers), Wind and Air Flow Studies (26 papers) and Fluid Dynamics and Vibration Analysis (15 papers). Christopher J. Bay collaborates with scholars based in United States, United Kingdom and Netherlands. Christopher J. Bay's co-authors include Jennifer King, Paul Fleming, Rafael Mudafort, Kathryn Johnson, Eric Simley, Jennifer Annoni, Luis A. Martínez‐Tossas, Lucy Y. Pao, Andrew P. J. Stanley and Mayank Chetan and has published in prestigious journals such as Applied Energy, Energy Conversion and Management and Renewable Energy.

In The Last Decade

Christopher J. Bay

60 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Christopher J. Bay United States 20 926 455 452 285 118 63 1.1k
Jennifer King United States 21 949 1.0× 479 1.1× 552 1.2× 280 1.0× 178 1.5× 70 1.3k
Tuhfe Göçmen Denmark 15 779 0.8× 425 0.9× 409 0.9× 245 0.9× 82 0.7× 46 982
Ahmad Vasel‐Be‐Hagh United States 13 605 0.7× 316 0.7× 260 0.6× 222 0.8× 61 0.5× 35 829
Jennifer Annoni United States 20 1.4k 1.6× 730 1.6× 683 1.5× 523 1.8× 192 1.6× 43 1.6k
Haiying Sun Hong Kong 20 986 1.1× 571 1.3× 343 0.8× 351 1.2× 84 0.7× 29 1.2k
Filippo Campagnolo Germany 20 1.3k 1.4× 654 1.4× 367 0.8× 531 1.9× 173 1.5× 73 1.4k
Peter Fuglsang Denmark 21 1.2k 1.3× 623 1.4× 248 0.5× 496 1.7× 212 1.8× 42 1.5k
Vlaho Petrović‬ Germany 19 906 1.0× 362 0.8× 820 1.8× 255 0.9× 554 4.7× 70 1.4k
Yaoran Chen China 13 358 0.4× 212 0.5× 274 0.6× 155 0.5× 50 0.4× 39 708

Countries citing papers authored by Christopher J. Bay

Since Specialization
Citations

This map shows the geographic impact of Christopher J. Bay's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Christopher J. Bay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher J. Bay more than expected).

Fields of papers citing papers by Christopher J. Bay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher J. Bay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Christopher J. Bay. The network helps show where Christopher J. Bay may publish in the future.

Co-authorship network of co-authors of Christopher J. Bay

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher J. Bay. A scholar is included among the top collaborators of Christopher J. Bay based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christopher J. Bay. Christopher J. Bay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bay, Christopher J., et al.. (2024). FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization. Wind Energy. 27(12). 1563–1580. 2 indexed citations
2.
Roberts, Owen, et al.. (2023). Opportunities for green hydrogen production with land-based wind in the United States. Energy Conversion and Management. 296. 117595–117595. 14 indexed citations
3.
Bay, Christopher J., Paul Fleming, Bart Doekemeijer, et al.. (2023). Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model. Wind energy science. 8(3). 401–419. 21 indexed citations
4.
Thomas, Jared, Erik Quaeghebeur, John Jasa, et al.. (2023). A comparison of eight optimization methods applied to a wind farm layout optimization problem. Wind energy science. 8(5). 865–891. 20 indexed citations
5.
Stanley, Andrew P. J., Christopher J. Bay, & Paul Fleming. (2023). Enabling control co-design of the next generation of wind power plants. Wind energy science. 8(8). 1341–1350. 10 indexed citations
6.
Pawar, Suraj, Ashesh Sharma, Ganesh Vijayakumar, et al.. (2022). Towards multi-fidelity deep learning of wind turbine wakes. Renewable Energy. 200. 867–879. 16 indexed citations
7.
Stanley, Andrew P. J., Jennifer King, Christopher J. Bay, & Andrew Ning. (2022). A model to calculate fatigue damage caused by partial waking during wind farm optimization. Wind energy science. 7(1). 433–454. 7 indexed citations
8.
Stanley, Andrew P. J., Christopher J. Bay, Rafael Mudafort, & Paul Fleming. (2022). Fast yaw optimization for wind plant wake steering using Boolean yaw angles. Wind energy science. 7(2). 741–757. 10 indexed citations
9.
Stanley, Andrew P. J., Owen Roberts, Jennifer King, & Christopher J. Bay. (2021). Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant. Wind energy science. 6(5). 1143–1167. 15 indexed citations
10.
Martínez‐Tossas, Luis A., Jennifer King, Eliot Quon, et al.. (2021). The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows. Wind energy science. 6(2). 555–570. 35 indexed citations
11.
Hamilton, Nicholas, Christopher J. Bay, Paul Fleming, Jennifer King, & Luis A. Martínez‐Tossas. (2020). Comparison of modular analytical wake models to the Lillgrund wind plant. Journal of Renewable and Sustainable Energy. 12(5). 25 indexed citations
12.
Stanley, Andrew P. J., Jennifer King, Christopher J. Bay, & Andrew Ning. (2020). A Model to Calculate Fatigue Damage Caused by Partial Wakingduring Wind Farm Optimization. 1 indexed citations
13.
King, Jennifer, Caroline Draxl, Rafael Mudafort, et al.. (2020). Design and analysis of a spatially heterogeneous wake. 6 indexed citations
14.
Fleming, Paul, Jennifer King, Eric Simley, et al.. (2020). Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2. Wind energy science. 5(3). 945–958. 84 indexed citations
15.
Bay, Christopher J., Jennifer Annoni, Luis A. Martínez‐Tossas, Lucy Y. Pao, & Kathryn Johnson. (2019). Flow Control Leveraging Downwind Rotors for Improved Wind Power Plant Operation. 2843–2848. 19 indexed citations
16.
Fleming, Paul, Jennifer King, Katherine Dykes, et al.. (2019). Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 7 indexed citations
17.
Annoni, Jennifer, Christopher J. Bay, Kathryn Johnson, et al.. (2018). A Framework for Autonomous Wind Farms: Wind Direction Consensus. 4 indexed citations
18.
19.
Bay, Christopher J., et al.. (2016). Autonomous lighting assessments in buildings: part 1 – robotic navigation and mapping. Advances in Building Energy Research. 11(2). 260–281. 1 indexed citations
20.
Bay, Christopher J., et al.. (2016). Autonomous lighting assessments in buildings: part 2 – light identification and energy analysis. Advances in Building Energy Research. 11(2). 227–244. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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