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Journal Articles

Route-Based Energy Analysis of Hybrid Rail Consists

Nitisha Ahuja, Jay D. Martin, Michael A. Yukish, Gary M. Stump, Lorri A. Bennett, Bryan W. Schlake, Joel R. Anstrom, Christopher D. Rahn
Journal: Journal of Dynamic Systems, Measurement, and Control
Publisher: ASME
Article Type: Research-Article
J. Dyn. Sys., Meas., Control. January 2024, 146(1): 011103.
Paper No: DS-23-1105
https://doi.org/10.1115/1.4064091
Published Online: December 11, 2023
View Article titled, Route-Based Energy Analysis of Hybrid Rail Consists
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Topics: Batteries, Locomotives, Fuels, Rails
Proceedings Papers

Route Based Fuel Savings of Freight Train Hybridization

Nitisha Ahuja, Jay D. Martin, Michael A. Yukish, Gary M. Stump, Lorri A. Bennett, Bryan W. Schlake, Joel R. Anstrom, Christopher D. Rahn
Proc. ASME. IDETC-CIE2023, Volume 1: 25th International Conference on Advanced Vehicle Technologies (AVT), V001T01A005, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-114683
https://doi.org/10.1115/DETC2023-114683
View Paper titled, Route Based Fuel Savings of Freight Train Hybridization
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Topics: Batteries, Fuels, Locomotives, Trains, Diesel, Fuel consumption, Diesel engines, Fuel efficiency, Rails, Computation
Proceedings Papers

A Real-Time Artificial Intelligence Process Manager for Engineering Design

Joshua T. Gyory, Nicolás F. Soria Zurita, Jay D. Martin, Corey M. Balon, Christopher McComb, Kenneth Kotovsky, Jonathan Cagan
Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A018, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-88609
https://doi.org/10.1115/DETC2022-88609
View Paper titled, A Real-Time Artificial Intelligence Process Manager for Engineering Design
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Topics: Artificial intelligence, Engineering design, Teams, Design, Performance, Path planning
Proceedings Papers

On Using Kriging Models for Complex Design

Jay D. Martin
Proc. ASME. IDETC-CIE2011, Volume 5: 37th Design Automation Conference, Parts A and B, 763-773, August 28–31, 2011
Publisher: American Society of Mechanical Engineers
Paper No: DETC2011-48579
https://doi.org/10.1115/DETC2011-48579
View Paper titled, On Using Kriging Models for Complex Design
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Topics: Design, Engineering disciplines, Collaboration, Complex systems, Dimensions, Interpolation, Teams
Proceedings Papers

Improving the Performance of Visual Steering Commands for Multi-Dimensional Trade Space Exploration

Christopher D. Congdon, Daniel E. Carlsen, Timothy W. Simpson, Jay D. Martin
Proc. ASME. IDETC-CIE2008, Volume 1: 34th Design Automation Conference, Parts A and B, 511-520, August 3–6, 2008
Publisher: American Society of Mechanical Engineers
Paper No: DETC2008-49684
https://doi.org/10.1115/DETC2008-49684
View Paper titled, Improving the Performance of Visual Steering Commands for Multi-Dimensional Trade Space Exploration
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Topics: Algorithms, Decision making, Design, Pareto optimization, Sampling methods
Journal Articles

Computational Improvements to Estimating Kriging Metamodel Parameters

Jay D. Martin
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Technical Briefs
J. Mech. Des. August 2009, 131(8): 084501.
https://doi.org/10.1115/1.3151807
Published Online: July 9, 2009
View Article titled, Computational Improvements to Estimating Kriging Metamodel Parameters
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Topics: Algorithms, Gaussian distribution, Maximum likelihood estimation, Optimization, Optimization algorithms, Design, Dimensions, Response surface methodology, Computers, Statistics
Proceedings Papers

Using Maximum Likelihood Estimation to Estimate Kriging Model Parameters

Jay D. Martin
Proc. ASME. IDETC-CIE2007, Volume 6: 33rd Design Automation Conference, Parts A and B, 663-673, September 4–7, 2007
Publisher: American Society of Mechanical Engineers
Paper No: DETC2007-34662
https://doi.org/10.1115/DETC2007-34662
View Paper titled, Using Maximum Likelihood Estimation to Estimate Kriging Model Parameters
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Topics: Maximum likelihood estimation, Algorithms, Optimization, Simulation, Statistics
Proceedings Papers

On the Use of Kriging Models to Approximate Deterministic Computer Models

Jay D. Martin, Timothy W. Simpson
Proc. ASME. IDETC-CIE2004, Volume 1: 30th Design Automation Conference, 481-492, September 28–October 2, 2004
Publisher: American Society of Mechanical Engineers
Paper No: DETC2004-57300
https://doi.org/10.1115/DETC2004-57300
View Paper titled, On the Use of Kriging Models to Approximate Deterministic Computer Models
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Topics: Computers, Maximum likelihood estimation, Algorithms, Approximation, Design, Errors, Optimization, Parameter estimation
Proceedings Papers

A Study on the Use of Kriging Models to Approximate Deterministic Computer Models

Jay D. Martin, Timothy W. Simpson
Proc. ASME. IDETC-CIE2003, Volume 2: 29th Design Automation Conference, Parts A and B, 567-576, September 2–6, 2003
Publisher: American Society of Mechanical Engineers
Paper No: DETC2003/DAC-48762
https://doi.org/10.1115/DETC2003/DAC-48762
View Paper titled, A Study on the Use of Kriging Models to Approximate Deterministic Computer Models
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Topics: Computers
Proceedings Papers

A Methodology to Manage Uncertainty During System-Level Conceptual Design

Jay D. Martin, Timothy W. Simpson
Proc. ASME. IDETC-CIE2005, Volume 2: 31st Design Automation Conference, Parts A and B, 1183-1193, September 24–28, 2005
Publisher: American Society of Mechanical Engineers
Paper No: DETC2005-84984
https://doi.org/10.1115/DETC2005-84984
View Paper titled, A Methodology to Manage Uncertainty During System-Level Conceptual Design
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Topics: Conceptual design, Uncertainty, Design, Decision making, Approximation, Computers, Satellites, Simulation
Journal Articles

A Methodology to Manage System-level Uncertainty During Conceptual Design

Jay D. Martin, Timothy W. Simpson
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. July 2006, 128(4): 959–968.
https://doi.org/10.1115/1.2204975
Published Online: January 11, 2006
View Article titled, A Methodology to Manage System-level Uncertainty During Conceptual Design
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Topics: Design, Uncertainty, Simulation