Daniel Arnold: h-index, Total Citations, and Citation Map
Daniel Arnold's h-index is 21 (36 i10-index, 1,879+ total citations across 5+ publications) according to Google Scholar as of May 2026. Daniel Arnold is affiliated with Lawrence Livermore National Laboratory.
Daniel Arnold is a researcher affiliated with Lawrence Livermore National Laboratory, specializing in Controls, power systems, optimization. Their work has been cited 1,879 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Daniel Arnold's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 21
- i10-Index
- 36
- Total Citations
- 1,879
- Citing Countries
- 19
As of May 2026.
Daniel Arnold has an h-index of 21 and 1,879 total citations across 5 publications, with research cited by institutions in 19 countries.
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Global Impact Map
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Top Cited Works
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Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity
2007285
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed a source-seeking control strategy for non-holonomic unicycles that operates without position measurement and incorporates forward velocity tuning.
The researcher developed optimal dispatch methods for reactive power to regulate voltage and balance unbalanced distribution systems, a contribution validated by independent scholarly uptake.
The researcher developed a model-free extremum seeking control framework for optimizing VAR resources in distribution systems, establishing a foundational approach for adaptive power system management.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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