Daniel G. Friend

2.9k citations
38 papers · 2.0k indexed · h-index 17

Daniel G. Friend

37 papers receiving 1.9k citations

Peers

Daniel G. Friend
Comparison fields: 5 of 89
  • Fluid Flow and Transfer Processes 637
  • Filtration and Separation 64
  • Biomedical Engineering 1.1k
  • Statistical and Nonlinear Physics 262
  • Computational Mechanics 297
Replace Haruki Sato with:
Haruki Sato Japan
N. B. Vargaftik Russia
Steven G. Penoncello United States
Eckhard Vogel Germany
D. G. Friend United States
R. T. Jacobsen United States
Marcelo Castier Brazil
James F. Ely United States
H. Ezzat Khalifa United States
Reiner Kleinrahm Germany
Daniel G. Friend relative to Haruki Sato Japan Haruki Sato's profile →
Citations per field
00.5×1.5×2.1×
Haruki Sato · 1×
Citations per year

Countries citing papers authored by Daniel G. Friend

Since Specialization
Citations

This map shows the geographic impact of Daniel G. Friend'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 Daniel G. Friend with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel G. Friend more than expected).

Fields of papers citing papers by Daniel G. Friend

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel G. Friend. 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 Daniel G. Friend. The network helps show where Daniel G. Friend may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Daniel G. Friend, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel G. Friend Line = papers co-authored together Daniel G. Friend links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20230
2 20202
3
Early LNG Program and LN2 Cryogenic Flow Measurement Facility at NIST | NIST
20111
4
The International Association for the Properties of Water and Steam
20091
5
The Fifth Industrial Fluid Properties Simulation Challenge | NIST
20071
6 20078
7
Physical and Chemical Data, Section 2
200715
8 200617
9 200515
10 20038
11 200115
12 20016
13 2000421
14
Thermophysical Properties of Helium-4 from 0.8 to 1500 K with Pressures to 2000 MPa | NIST
199815
15 199858
16 19936
17 199211
18 199276
19 1989168
20 198314

About Daniel G. Friend

Daniel G. Friend is a scholar working on Filtration and Separation, Fluid Flow and Transfer Processes and Biomedical Engineering, having authored 38 papers that have together received 2.0k indexed citations. Recurring topics across this work include Phase Equilibria and Thermodynamics (24 papers), Chemical Thermodynamics and Molecular Structure (7 papers), Advanced Thermodynamics and Statistical Mechanics (6 papers), Thermodynamic properties of mixtures (6 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Quantum, superfluid, helium dynamics (3 papers), Scientific Measurement and Uncertainty Evaluation (3 papers) and Chemical and Physical Properties in Aqueous Solutions (3 papers). The work is most often cited by research in Fluid Flow and Transfer Processes (637 citations), Filtration and Separation (64 citations) and Biomedical Engineering (1.1k citations). Daniel G. Friend has collaborated with scholars based in United States, Japan and Egypt. Frequent co-authors include James C. Rainwater, Reiner Tillner‐Roth, Eric W. Lemmon, Steven G. Penoncello, R. T. Jacobsen, James F. Ely, Marcia L. Huber, Arno Laesecke, James D. Olson and D. J. Frurip.

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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