Maranic, Guillard, Baker, Einav, and Marks (Granular Matter 2021) introduced an X-ray 'granular thermometer': fast radiography of dense tracer particles embedded in an opaque granular medium yields a tracer-resolved granular temperature (mean-square velocity fluctuation). The paper itself states two unresolved issues that bear on whether such measurements are physically meaningful: (i) reliable inference of the bulk granular temperature from tracers denser than the bulk; and (ii) the fundamental definition and physical nature of granular temperature in dense flows, where kinetic and elastic-potential fluctuations contribute comparably and free-flight and enduring-contact fluctuation modes coexist, calling for a classification and measurement of all fluctuation contributions (velocity, density, elastic potential).
Background
Granular materials — large assemblies of macroscopic, dissipative particles — are often described by a kinetic-theory analogy in which the 'granular temperature' plays the role of thermodynamic temperature: is defined from the mean-square fluctuation of particle velocities about the mean flow (with the particle mass absorbed into the definition), and it governs fluctuation-driven transport, species segregation, and rheology in dilute and moderately dense regimes. In dense flows the situation is unsettled: particles interact through long-lived contacts and deform elastically where they touch, so kinetic (velocity) fluctuations, elastic potential energy stored at contacts, and density (configurational) fluctuations can all contribute comparably, and the motion alternates between brief free-flight segments and collective fluctuation of the load-bearing contact network. The distinction matters practically, because dense-flow constitutive frameworks — nonlocal rheologies (flow laws in which the local strain rate depends on neighboring flow through a fluctuation-related length) and kinetic-stress segregation theories — invoke as a state variable, so an ill-defined or unmeasurable undermines their experimental testability.
Maranic, Guillard, Baker, Einav, and Marks (2021) developed an X-ray-radiography tracer thermometry: tracer particles denser than the surrounding medium are embedded in an opaque granular system such as a vibrofluidised bed (a bed of grains fluidized by vertical vibration), imaged with high-speed X-rays, and their velocity fluctuations yield a tracer-resolved granular temperature. The paper quantified the technique's bias, validated it against discrete element method (DEM) simulations — numerical simulations that resolve every particle and inter-particle contact — and applied it experimentally. Its Supplementary Information (Appendix 6, 'Measuring bulk temperature') applies a DEM-derived correction under simple shear while noting that the intruder–bulk temperature relationship remains unknown and the subject of active research. Two families of adjacent results frame what remains open. First, granular mixtures do not equipartition: positron-based experiments showed two distinct species-resolved granular temperatures coexisting in binary vibrofluidized beds (Wildman and Parker 2002), and segregation theories (Fan and Hill 2011; Hill and Fan 2016) show temperature contrast between species driving their relative motion — so a temperature measured on a tracer species cannot simply be identified with the bulk's. Second, on the definitional side, DEM studies introduced a 'generalized' granular temperature including configurational and contact contributions (Sun et al. 2015, simulation only), acoustic energy was used as an experimental proxy in a shear flow (Taylor and Brodsky 2017), velocity-fluctuation temperature was shown to control the local rheology of vibrated dense flows (Irmer, Brodsky, and Clark 2025), and Edwards-type compactivity — a formal temperature conjugate to volume for jammed configurations, applied by Krechetnikov and Zelnikov (2025) to quasistatic piles — describes static ensembles rather than dense flows. PEPT (positron emission particle tracking), which reconstructs tracer trajectories from positron-annihilation photons and can track tracers with properties identical to the bulk, offers a potential cross-calibration route; its methodology has advanced (reconstruction algorithms, uncertainty quantification) without any cross-calibration against tracer thermometry. None of these results delivers either a calibrated route from dense-tracer measurements to the bulk temperature, or an agreed, operationally measurable definition of in dense flows.
Problem Statement
The source paper (Maranic et al. 2021, 'A granular thermometer') states two unresolved issues that bear on whether X-ray tracer thermometry yields a physically meaningful granular temperature in dense flows:
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Bulk inference from dense tracers. Establish a reliable way to infer the bulk granular temperature from measurements made on tracer particles denser than the bulk material.
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Definition of granular temperature in dense flows. Establish the fundamental definition and physical nature of granular temperature in dense flows, where kinetic and elastic-potential fluctuations contribute comparably and the system exhibits both free-flight and enduring-contact-network fluctuation modes. This calls for a classification and measurement of all fluctuation contributions — velocity, density, and elastic potential — sufficient to define a physically measurable granular temperature.
The common objective uniting the two issues is a well-defined, measurable granular temperature for dense flows realized through the tracer-thermometry program. The two issues are jointly required — resolving either one alone does not complete the task: the bulk-inference route must yield a bulk temperature checkable against an independent determination of that bulk temperature, and the definitional work must classify and measure the three named contributions — velocity, density, and elastic potential — as the complete set to be accounted for, making each an observable extractable from experimental data so that together they define a physically measurable granular temperature.
References
- Maranic, Guillard, Baker, Einav, and Marks, 'A granular thermometer', Granular Matter 23 (2), 19 (2021). DOI: 10.1007/s10035-020-01081-3, https://doi.org/10.1007/s10035-020-01081-3
- Wildman and Parker, 'Coexistence of Two Granular Temperatures in Binary Vibrofluidized Beds', Physical Review Letters 88, 064301 (2002). DOI: 10.1103/PhysRevLett.88.064301, https://doi.org/10.1103/PhysRevLett.88.064301
- Fan and Hill, 'Theory for shear-induced segregation of dense granular mixtures', New Journal of Physics 13, 095009 (2011). DOI: 10.1088/1367-2630/13/9/095009, https://doi.org/10.1088/1367-2630/13/9/095009
- Hill and Fan, 'Granular Temperature and Segregation in Dense Sheared Particulate Mixtures', KONA Powder and Particle Journal 33, 150-168 (2016). DOI: 10.14356/kona.2016022, https://doi.org/10.14356/kona.2016022
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- Krechetnikov and Zelnikov, 'Statistical mechanics of a two-dimensional quasistatic granular pile: Thermodynamics and constitutive relations', Physical Review E 111, 055408 (2025). DOI: 10.1103/PhysRevE.111.055408, https://doi.org/10.1103/PhysRevE.111.055408
- Benjamin M. Alessio, Matthew R. Edwards, and Ching-Yao Lai, 'Dense granular rheology from fluctuations', arXiv:2601.01907 (2026). https://arxiv.org/abs/2601.01907
- Irmer, Brodsky, and Clark, 'Granular Temperature Controls Local Rheology of Vibrated Granular Flows', Physical Review Letters 134, 048202 (2025). DOI: 10.1103/PhysRevLett.134.048202, https://doi.org/10.1103/PhysRevLett.134.048202
- Taylor and Brodsky, 'Granular temperature measured experimentally in a shear flow by acoustic energy', Physical Review E 96, 032913 (2017). DOI: 10.1103/PhysRevE.96.032913, https://doi.org/10.1103/PhysRevE.96.032913
- Sun, Xu, Lu, Liu, Zhang, Tang, and Zhang, 'Simulated configurational temperature of particles and a model of constitutive relations of rapid-intermediate-dense granular flow based on generalized granular temperature', International Journal of Multiphase Flow 77, 1-18 (2015). DOI: 10.1016/j.ijmultiphaseflow.2015.07.008, https://doi.org/10.1016/j.ijmultiphaseflow.2015.07.008
- Escobar, Baker, Guillard, Faug, and Einav, 'Using x-ray rheography to estimate the 3D field of inertial numbers in flowing granular media', EPJ Web of Conferences 340, 10022 (2025). DOI: 10.1051/epjconf/202534010022, https://doi.org/10.1051/epjconf/202534010022
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- Sam Manger, Antoine Renaud, and Jacques Vanneste, 'An expectation-maximization algorithm for positron emission particle tracking', arXiv:2104.07457 (2021). https://arxiv.org/abs/2104.07457
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