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\n  \n 2026\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Resource Analysis for Quantum Simulation of Spatially Varying Transport-Reaction Equations.\n \n \n \n \n\n\n \n Jasperson, B.; Garikipati, K.; and Srivastava, S.\n\n\n \n\n\n\n August 2026.\n \n\n\n\n
\n\n\n\n \n \n \"ResourcePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{jasperson_resource_2026,\n\ttitle = {Resource {Analysis} for {Quantum} {Simulation} of {Spatially} {Varying} {Transport}-{Reaction} {Equations}},\n\turl = {https://arxiv.org/abs/2608.15827v1},\n\tabstract = {Spatially varying coefficients are the primary source of circuit complexity in quantum simulation of linear advection-diffusion-reaction equations. This work presents a resource analysis of single-step quantum propagators constructed using sparse FABLE block encodings, Quantum Singular Value Transformation, and linear-combination-of-unitaries. We derive theoretical estimates for qubit count, gate complexity, and circuit depth in terms of the spatial discretization, sparsity, and polynomial degree, and compare these predictions with synthesized quantum circuits for one- and two-dimensional variable-coefficient problems. The resulting analysis quantifies the cost of encoding realistic transport operators and provides practical resource estimates for near-term implementations.},\n\tlanguage = {en},\n\turldate = {2026-10-28},\n\tjournal = {arXiv.org},\n\tauthor = {Jasperson, Benjamin and Garikipati, Krishna and Srivastava, Siddhartha},\n\tmonth = aug,\n\tyear = {2026},\n}\n\n\n\n
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\n Spatially varying coefficients are the primary source of circuit complexity in quantum simulation of linear advection-diffusion-reaction equations. This work presents a resource analysis of single-step quantum propagators constructed using sparse FABLE block encodings, Quantum Singular Value Transformation, and linear-combination-of-unitaries. We derive theoretical estimates for qubit count, gate complexity, and circuit depth in terms of the spatial discretization, sparsity, and polynomial degree, and compare these predictions with synthesized quantum circuits for one- and two-dimensional variable-coefficient problems. The resulting analysis quantifies the cost of encoding realistic transport operators and provides practical resource estimates for near-term implementations.\n
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\n \n\n \n \n \n \n \n \n AI University: An LLM-Powered Learning Assistant for Engineering—A Finite Element Method Case Study.\n \n \n \n \n\n\n \n Shojaei, M. F.; Gulati, R.; Jasperson, B. A.; Wang, S.; Cimolato, S.; Vardhan, M.; Cao, D.; Neiswanger, W.; and Garikipati, K.\n\n\n \n\n\n\n Transactions on Machine Learning Research. 2026.\n \n\n\n\n
\n\n\n\n \n \n \"AIPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{shojaei_ai_2026,\n\ttitle = {{AI} {University}: {An} {LLM}-{Powered} {Learning} {Assistant} for {Engineering}—{A} {Finite} {Element} {Method} {Case} {Study}},\n\tissn = {2835-8856},\n\turl = {https://openreview.net/forum?id=NU36BROGDV},\n\tjournal = {Transactions on Machine Learning Research},\n\tauthor = {Shojaei, Mostafa Faghih and Gulati, Rahul and Jasperson, Benjamin A. and Wang, Shangshang and Cimolato, Simone and Vardhan, Manas and Cao, Dangli and Neiswanger, Willie and Garikipati, Krishna},\n\tyear = {2026},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers.\n \n \n \n \n\n\n \n Robertson, A. E.; Lenau, A. T.; Shimanek, J. D.; Jasperson, B. A.; Oommen, V.; Damm, D. L.; Garikipati, K.; and Dingreville, R.\n\n\n \n\n\n\n September 2026.\n arXiv:2609.30198 [cs.LG]\n\n\n\n
\n\n\n\n \n \n \"BeyondPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@misc{robertson_beyond_2026,\n\ttitle = {Beyond {Compression}: {Training} {Latent} {Representations} for {Stable} {Long}-{Horizon} {Rollout} in {Neural} {Surrogate} {Solvers}},\n\tshorttitle = {Beyond {Compression}},\n\turl = {http://arxiv.org/abs/2609.30198},\n\tdoi = {10.48550/arXiv.2609.30198},\n\tabstract = {Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasting. We systematically evaluate training-level interventions that align latent representations with long-horizon rollout: Koopman operator learning and Hamming noise injection during autoencoder training to improve compression, together with noise injection and multi-step rollout fine-tuning to improve dynamics. Interventions that improve long-horizon rollout stability often degrade conventional training metrics, including reconstruction and one-step prediction accuracy. Collectively, these interventions reduce long-rollout error by approximately 40{\\textbackslash}\\% and match or exceed the accuracy of full-resolution models on two physics benchmarks, while requiring 2 orders of magnitude fewer floating point operations and half the GPU memory. Applied to mesoscale crystal-plasticity simulations of high-cycle fatigue, the resulting surrogate achieves stable extrapolation over horizons orders of magnitude beyond those observed during training. More broadly, these results show that neural compression should be designed not merely to reduce dimensionality, but to restructure the solution space for stable dynamical evolution, a key requirement for reliable, efficient neural surrogates in scientific applications.},\n\turldate = {2026-10-24},\n\tpublisher = {arXiv},\n\tauthor = {Robertson, Andreas E. and Lenau, Ashley T. and Shimanek, John D. and Jasperson, Benjamin A. and Oommen, Vivek and Damm, David L. and Garikipati, Krishna and Dingreville, Remi},\n\tmonth = sep,\n\tyear = {2026},\n\tnote = {arXiv:2609.30198 [cs.LG]},\n\tkeywords = {Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Condensed Matter - Materials Science},\n}\n\n\n\n\n\n\n\n\n\n\n\n
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\n Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasting. We systematically evaluate training-level interventions that align latent representations with long-horizon rollout: Koopman operator learning and Hamming noise injection during autoencoder training to improve compression, together with noise injection and multi-step rollout fine-tuning to improve dynamics. Interventions that improve long-horizon rollout stability often degrade conventional training metrics, including reconstruction and one-step prediction accuracy. Collectively, these interventions reduce long-rollout error by approximately 40\\% and match or exceed the accuracy of full-resolution models on two physics benchmarks, while requiring 2 orders of magnitude fewer floating point operations and half the GPU memory. Applied to mesoscale crystal-plasticity simulations of high-cycle fatigue, the resulting surrogate achieves stable extrapolation over horizons orders of magnitude beyond those observed during training. More broadly, these results show that neural compression should be designed not merely to reduce dimensionality, but to restructure the solution space for stable dynamical evolution, a key requirement for reliable, efficient neural surrogates in scientific applications.\n
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\n \n\n \n \n \n \n \n \n Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors.\n \n \n \n \n\n\n \n Boyce, B. L.; Wood, M. A.; Garikipati, K.; Robertson, A. E.; Larson, J.; Srivastava, I.; Debusschere, B.; Desai, S.; Iyer, P.; Robbe, P.; Cherukara, M.; Munson, T.; Du, M.; Mohanty, T.; Gardner, D. J.; Capolungo, L.; Jasperson, B. A.; Tan, J.; and Dingreville, R.\n\n\n \n\n\n\n July 2026.\n arXiv:2607.27163 [cond-mat.mtrl-sci]\n\n\n\n
\n\n\n\n \n \n \"MaterialsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@misc{boyceMaterialsBehaviorMechanism2026,\n\ttitle = {Materials {Behavior} as {Mechanism} {Ensembles}: {A} {Probabilistic} {Framework} for {Emergent} {Behaviors}},\n\tshorttitle = {Materials {Behavior} as {Mechanism} {Ensembles}},\n\turl = {http://arxiv.org/abs/2607.27163},\n\tdoi = {10.48550/arXiv.2607.27163},\n\tabstract = {Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechanisms across scales. This is especially evident in fatigue of metals, where crack growth is typically modeled as monotonic and irreversible process, despite evidence that local microstructure, loading history, and competing unit processes can shift the balance among propagation, arrest, and self-healing. Here we present a probabilistic framework that describes materials behavior as an ensemble of constituent mechanisms whose activation, interaction, and evolution determine emergent outcomes. The framework connects mechanism activation, state evolution, and macroscopic observables in a probabilistic way. In the case of fatigue crack propagation, it reframes damage tolerance as an inference problem over mechanism competition and provides a basis for integrating multiscale simulation, multimodal characterization, and machine learning. The same logic extends to other physical and chemical systems suggesting a portable framework for any system in which emergent behavior reflects mechanism competition under changing conditions. The broader ambition of this perspective review is a shift from correlating structure and performance after the fact to identifying, in advance, the conditions that make desired emergent behavior probable.},\n\turldate = {2026-07-30},\n\tpublisher = {arXiv},\n\tauthor = {Boyce, Brad L. and Wood, Mitchell A. and Garikipati, Krishna and Robertson, Andreas E. and Larson, Jeffrey and Srivastava, Ishan and Debusschere, Bert and Desai, Saaketh and Iyer, Prasad and Robbe, Pieterjan and Cherukara, Mathew and Munson, Todd and Du, Ming and Mohanty, Trupti and Gardner, David J. and Capolungo, Laurent and Jasperson, Benjamin A. and Tan, Jingye and Dingreville, Rémi},\n\tmonth = jul,\n\tyear = {2026},\n\tnote = {arXiv:2607.27163 [cond-mat.mtrl-sci]},\n\tkeywords = {Condensed Matter - Materials Science},\n}\n\n\n\n
\n
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\n Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechanisms across scales. This is especially evident in fatigue of metals, where crack growth is typically modeled as monotonic and irreversible process, despite evidence that local microstructure, loading history, and competing unit processes can shift the balance among propagation, arrest, and self-healing. Here we present a probabilistic framework that describes materials behavior as an ensemble of constituent mechanisms whose activation, interaction, and evolution determine emergent outcomes. The framework connects mechanism activation, state evolution, and macroscopic observables in a probabilistic way. In the case of fatigue crack propagation, it reframes damage tolerance as an inference problem over mechanism competition and provides a basis for integrating multiscale simulation, multimodal characterization, and machine learning. The same logic extends to other physical and chemical systems suggesting a portable framework for any system in which emergent behavior reflects mechanism competition under changing conditions. The broader ambition of this perspective review is a shift from correlating structure and performance after the fact to identifying, in advance, the conditions that make desired emergent behavior probable.\n
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\n  \n 2025\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends.\n \n \n \n \n\n\n \n Jasperson, B. A.; Nikiforov, I.; Samanta, A.; Runnels, B.; Johnson, H. T.; and Tadmor, E. B.\n\n\n \n\n\n\n Acta Materialia, 286: 120722. March 2025.\n \n\n\n\n
\n\n\n\n \n \n \"FundamentalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_fundamental_2025,\n\ttitle = {Fundamental {Microscopic} {Properties} as {Predictors} of {Large}-{Scale} {Quantities} of {Interest}: {Validation} through {Grain} {Boundary} {Energy} {Trends}},\n\tvolume = {286},\n\tissn = {13596454},\n\tshorttitle = {Fundamental {Microscopic} {Properties} as {Predictors} of {Large}-{Scale} {Quantities} of {Interest}},\n\turl = {https://www.sciencedirect.com/science/article/abs/pii/S1359645425000151},\n\tdoi = {10.1016/j.actamat.2025.120722},\n\turldate = {2025-01-27},\n\tjournal = {Acta Materialia},\n\tauthor = {Jasperson, Benjamin A. and Nikiforov, Ilia and Samanta, Amit and Runnels, Brandon and Johnson, Harley T. and Tadmor, Ellad B.},\n\tmonth = mar,\n\tyear = {2025},\n\tpages = {120722},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Cross-Scale Covariance for Material Property Prediction.\n \n \n \n \n\n\n \n Jasperson, B. A.; Nikiforov, I.; Samanta, A.; Zhou, F.; Tadmor, E. B.; Lordi, V.; and Bulatov, V. V.\n\n\n \n\n\n\n npj Computational Materials, 11(1): 1. January 2025.\n \n\n\n\n
\n\n\n\n \n \n \"Cross-ScalePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_cross-scale_2025,\n\ttitle = {Cross-{Scale} {Covariance} for {Material} {Property} {Prediction}},\n\tvolume = {11},\n\tissn = {2057-3960},\n\turl = {https://www.nature.com/articles/s41524-024-01453-w},\n\tdoi = {10.1038/s41524-024-01453-w},\n\tnumber = {1},\n\turldate = {2025-01-14},\n\tjournal = {npj Computational Materials},\n\tauthor = {Jasperson, Benjamin A. and Nikiforov, Ilia and Samanta, Amit and Zhou, Fei and Tadmor, Ellad B. and Lordi, Vincenzo and Bulatov, Vasily V.},\n\tmonth = jan,\n\tyear = {2025},\n\tpages = {1},\n}\n\n\n\n
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\n  \n 2024\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n A Dual Neural Network Approach to Topology Optimization for Thermal-Electromagnetic Device Design.\n \n \n \n \n\n\n \n Jasperson, B. A.; Wood, M. G.; and Johnson, H. T.\n\n\n \n\n\n\n Computer-Aided Design, 168: 103665. March 2024.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_dual_2024,\n\ttitle = {A {Dual} {Neural} {Network} {Approach} to {Topology} {Optimization} for {Thermal}-{Electromagnetic} {Device} {Design}},\n\tvolume = {168},\n\tissn = {00104485},\n\turl = {https://www.sciencedirect.com/science/article/abs/pii/S0010448523001975},\n\tdoi = {10.1016/j.cad.2023.103665},\n\turldate = {2025-01-07},\n\tjournal = {Computer-Aided Design},\n\tauthor = {Jasperson, Benjamin A. and Wood, Michael G. and Johnson, Harley T.},\n\tmonth = mar,\n\tyear = {2024},\n\tpages = {103665},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n A Data-Driven Method for Optimization of Classical Interatomic Potentials.\n \n \n \n \n\n\n \n Jasperson, B. A.; and Johnson, H. T.\n\n\n \n\n\n\n MRS Advances, 9(11): 863–869. March 2024.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_data-driven_2024,\n\ttitle = {A {Data}-{Driven} {Method} for {Optimization} of {Classical} {Interatomic} {Potentials}},\n\tvolume = {9},\n\tissn = {2059-8521},\n\turl = {https://link.springer.com/article/10.1557/s43580-024-00802-7},\n\tdoi = {10.1557/s43580-024-00802-7},\n\tnumber = {11},\n\turldate = {2025-01-07},\n\tjournal = {MRS Advances},\n\tauthor = {Jasperson, Benjamin A. and Johnson, Harley T.},\n\tmonth = mar,\n\tyear = {2024},\n\tpages = {863--869},\n}\n
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\n \n\n \n \n \n \n \n \n Optically-Triggered Optical Limiters for Short-Wavelength Infrared Sensor Protection.\n \n \n \n \n\n\n \n Wood, M. G.; McKay, A.; Morin, T. J.; Serkland, D. K.; Luk, T. S.; Wolfley, S. L.; Gastian, L.; Mudrick, J. P.; Jasperson, B.; and Johnson, H. T.\n\n\n \n\n\n\n In 2021 Conference on Lasers and Electro-Optics (CLEO), pages 1–2, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"Optically-TriggeredPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wood_optically-triggered_2021,\n\ttitle = {Optically-{Triggered} {Optical} {Limiters} for {Short}-{Wavelength} {Infrared} {Sensor} {Protection}},\n\turl = {https://doi.org/10.1364/CLEO_SI.2021.STh1E.3},\n\tbooktitle = {2021 {Conference} on {Lasers} and {Electro}-{Optics} ({CLEO})},\n\tauthor = {Wood, Michael G. and McKay, Alec and Morin, Theodore J. and Serkland, Darwin K. and Luk, Ting S. and Wolfley, Steve L. and Gastian, Loren and Mudrick, John P. and Jasperson, Ben and Johnson, Harley T.},\n\tyear = {2021},\n\tpages = {1--2},\n}\n\n\n\n
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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Devices and Methods for Delivering a Beneficial Agent to a User.\n \n \n \n \n\n\n \n Anderson, P.; Novak, K.; Mclennan, K.; Mackaplow, M.; Song, G.; Dhami, G. S.; Jasperson, B. A.; Smieja, S.; and Svacina, M.\n\n\n \n\n\n\n February 2019.\n \n\n\n\n
\n\n\n\n \n \n \"DevicesPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@patent{anderson_devices_2019,\n\ttitle = {Devices and {Methods} for {Delivering} a {Beneficial} {Agent} to a {User}},\n\turl = {https://patents.google.com/patent/US10213546B2/en},\n\tnationality = {United States},\n\tassignee = {AbbVie Inc.},\n\tnumber = {10213546},\n\tauthor = {Anderson, Phil and Novak, Kevin and Mclennan, Kevin and Mackaplow, Michael and Song, Guiyong and Dhami, Gurjinder Singh and Jasperson, Benjamin Alan and Smieja, Scott and Svacina, Matthew},\n\tmonth = feb,\n\tyear = {2019},\n}\n\n\n\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Thin Film Heat Flux Sensors Fabricated on Copper Substrates for Thermal Measurements in Microfluidic Environments.\n \n \n \n \n\n\n \n Jasperson, B. A; Schmale, J.; Qu, W.; Pfefferkorn, F. E; and Turner, K. T\n\n\n \n\n\n\n Journal of Micromechanics and Microengineering, 24(12): 125018. December 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ThinPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_thin_2014,\n\ttitle = {Thin {Film} {Heat} {Flux} {Sensors} {Fabricated} on {Copper} {Substrates} for {Thermal} {Measurements} in {Microfluidic} {Environments}},\n\tvolume = {24},\n\tissn = {0960-1317, 1361-6439},\n\turl = {https://iopscience.iop.org/article/10.1088/0960-1317/24/12/125018/meta?casa_token=vXtZgVFPYYMAAAAA:Oe3I4ih3g6LbxFOV0XP4kiRUpqyrmfqL9n6Uhl50yplM4L0yKZJUW9jel59DuPP9cwVYHypLLTSDTnvO6SuY-_7XjgJc},\n\tdoi = {10.1088/0960-1317/24/12/125018},\n\tnumber = {12},\n\turldate = {2022-08-24},\n\tjournal = {Journal of Micromechanics and Microengineering},\n\tauthor = {Jasperson, Benjamin A and Schmale, Joshua and Qu, Weilin and Pfefferkorn, Frank E and Turner, Kevin T},\n\tmonth = dec,\n\tyear = {2014},\n\tpages = {125018},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Comparison of Micro-Pin-Fin and Microchannel Heat Sinks Considering Thermal-Hydraulic Performance and Manufacturability.\n \n \n \n \n\n\n \n Jasperson, B.; Yongho Jeon; Turner, K.; Pfefferkorn, F.; and Weilin Qu\n\n\n \n\n\n\n IEEE Transactions on Components and Packaging Technologies, 33(1): 148–160. March 2010.\n \n\n\n\n
\n\n\n\n \n \n \"ComparisonPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jasperson_comparison_2010,\n\ttitle = {Comparison of {Micro}-{Pin}-{Fin} and {Microchannel} {Heat} {Sinks} {Considering} {Thermal}-{Hydraulic} {Performance} and {Manufacturability}},\n\tvolume = {33},\n\tissn = {1521-3331, 1557-9972},\n\turl = {https://ieeexplore.ieee.org/abstract/document/5286856},\n\tdoi = {10.1109/TCAPT.2009.2023980},\n\tnumber = {1},\n\turldate = {2022-08-24},\n\tjournal = {IEEE Transactions on Components and Packaging Technologies},\n\tauthor = {Jasperson, B.A. and {Yongho Jeon} and Turner, K.T. and Pfefferkorn, F.E. and {Weilin Qu}},\n\tmonth = mar,\n\tyear = {2010},\n\tpages = {148--160},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n A Thin-Film Heat Flux Sensor Fabricated on Copper for Heat Transfer Measurements in Parallel Channel Heat Sinks.\n \n \n \n \n\n\n \n Jasperson, B. A.; Pfefferkorn, F. E.; Qu, W.; and Turner, K. T.\n\n\n \n\n\n\n In International Conference on MicroManufacturing (ICOMM), Madison, WI, May 2010. 5th International Conference on MicroManufacturing (ICOMM 2010)\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{jasperson_thin-film_2010,\n\taddress = {Madison, WI},\n\ttitle = {A {Thin}-{Film} {Heat} {Flux} {Sensor} {Fabricated} on {Copper} for {Heat} {Transfer} {Measurements} in {Parallel} {Channel} {Heat} {Sinks}},\n\turl = {https://minds.wisconsin.edu/handle/1793/65527},\n\tabstract = {A combination of lithography-based microfabrication and micro end milling is used to manufacture thin-film resistance temperature detector (RTD) heat flux sensors on bulk copper substrates. The fabrication process uses photoresist patterning, metal deposition, and lift-off to build the sensor and micro-end milling to segment the sensors. Micro end milling tests were performed to establish determine the optimum conditions for sensor removal which minimized delamination and burr formation. It was determined that starting on the backside (opposite the sensor) of the copper wafer and machining through to the thin film layers resulted in the least amount of burr formation.},\n\tbooktitle = {International {Conference} on {MicroManufacturing} ({ICOMM})},\n\tpublisher = {5th International Conference on MicroManufacturing (ICOMM 2010)},\n\tauthor = {Jasperson, Benjamin A. and Pfefferkorn, Frank E. and Qu, Weilin and Turner, Kevin T.},\n\tmonth = may,\n\tyear = {2010},\n}\n\n\n\n
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\n A combination of lithography-based microfabrication and micro end milling is used to manufacture thin-film resistance temperature detector (RTD) heat flux sensors on bulk copper substrates. The fabrication process uses photoresist patterning, metal deposition, and lift-off to build the sensor and micro-end milling to segment the sensors. Micro end milling tests were performed to establish determine the optimum conditions for sensor removal which minimized delamination and burr formation. It was determined that starting on the backside (opposite the sensor) of the copper wafer and machining through to the thin film layers resulted in the least amount of burr formation.\n
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\n  \n 2008\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Experimental Study of Adiabatic Water Liquid-Vapor Two-Phase Pressure Drop Across an Array of Staggered Micro-Pin-Fins.\n \n \n \n \n\n\n \n Konishi, C. A.; Qu, W.; Jasperson, B.; Pfefferkorn, F. E.; and Turner, K. T.\n\n\n \n\n\n\n In Volume 10: Heat Transfer, Fluid Flows, and Thermal Systems, Parts A, B, and C, pages 1597–1605, Boston, Massachusetts, USA, January 2008. ASMEDC\n \n\n\n\n
\n\n\n\n \n \n \"ExperimentalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{konishi_experimental_2008,\n\taddress = {Boston, Massachusetts, USA},\n\ttitle = {Experimental {Study} of {Adiabatic} {Water} {Liquid}-{Vapor} {Two}-{Phase} {Pressure} {Drop} {Across} an {Array} of {Staggered} {Micro}-{Pin}-{Fins}},\n\tisbn = {978-0-7918-4871-5},\n\turl = {https://asmedigitalcollection.asme.org/IMECE/proceedings/IMECE2008/48715/1597/332343?casa_token=SqlnR-qum_cAAAAA:vnmTHCPygqYX4UWf3yCOvpQ2NhmFPUz1OdKry7PMW2neRt42RtMwLzeiPy43UwT39lpiICwO&guestAccessKey=},\n\tdoi = {10.1115/IMECE2008-69051},\n\tabstract = {This study concerns pressure drop of adiabatic water liquid-vapor two-phase flow across an array of 1950 staggered square micro-pin-fins having a 200× 200 micron cross-section by a 670 micron height. The ratios of longitudinal pitch and transverse pitch to pin-fin equivalent diameter are equal to 2. An inline immersion heater upstream of the micro-pin-fin test module was employed to produce liquid-vapor two-phase mixture, which flowed across the micro-pin-fin array. The test module was well insulated to maintain an adiabatic condition. Four maximum mass velocities of 184, 235, 337, and 391 kg/m2s, and a range of vapor qualities for each maximum mass velocity were tested. Measured pressure drop increases drastically with increasing vapor quality. Nine existing two-phase pressure drop models and correlations were assessed. The Lockhart-Martinelli correlation for laminar liquid-laminar vapor combination in conjunction with a single-phase friction factor correlation proposed for the present micro-pin-fin array provided the best agreement with the data.},\n\turldate = {2022-08-24},\n\tbooktitle = {Volume 10: {Heat} {Transfer}, {Fluid} {Flows}, and {Thermal} {Systems}, {Parts} {A}, {B}, and {C}},\n\tpublisher = {ASMEDC},\n\tauthor = {Konishi, Christopher A. and Qu, Weilin and Jasperson, Ben and Pfefferkorn, Frank E. and Turner, Kevin T.},\n\tmonth = jan,\n\tyear = {2008},\n\tpages = {1597--1605},\n}\n\n\n\n
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\n This study concerns pressure drop of adiabatic water liquid-vapor two-phase flow across an array of 1950 staggered square micro-pin-fins having a 200× 200 micron cross-section by a 670 micron height. The ratios of longitudinal pitch and transverse pitch to pin-fin equivalent diameter are equal to 2. An inline immersion heater upstream of the micro-pin-fin test module was employed to produce liquid-vapor two-phase mixture, which flowed across the micro-pin-fin array. The test module was well insulated to maintain an adiabatic condition. Four maximum mass velocities of 184, 235, 337, and 391 kg/m2s, and a range of vapor qualities for each maximum mass velocity were tested. Measured pressure drop increases drastically with increasing vapor quality. Nine existing two-phase pressure drop models and correlations were assessed. The Lockhart-Martinelli correlation for laminar liquid-laminar vapor combination in conjunction with a single-phase friction factor correlation proposed for the present micro-pin-fin array provided the best agreement with the data.\n
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