arXiv subjects

Christopher J. Hogan

Publications and source records attributed to Christopher J. Hogan.

4 recordsLinked to original sources

arXiv2026

Post-Collision Thermal Excitation and Survival-Limited Cluster Growth in the Gas Phase

Gas-phase cluster growth by monomer addition is commonly modeled as an isothermal process. We develop a survival-limited framework in which association produces a thermally excited cluster that may dissociate before either cooling through bath-gas collisions or encountering the next monomer. A continuous-energy survival probability is first derived for an individual post-association thermal trajectory and is then marginalized over distributions of excitation energy, equilibrium energy, and monomer-arrival time using a trajectory functional. Molecular-dynamics simulations of water, silver, and gold clusters provide size-dependent caloric relationships and latent heats, while event-based Monte Carlo simulations independently test the survival formulation. Theory and Monte Carlo results agree closely. The ensemble-averaged survival probability exhibits strong and non-monotonic size dependence, with the largest thermal penalties generally occurring for the smallest clusters. Intermediate-size local maxima arise only when complete cluster-energy distributions are retained and result from competition between curvature-enhanced dissociation and the narrowing of the low-energy tail with increasing size. Surviving clusters are consequently drawn preferentially from the colder portion of the pre-collision energy distribution, and mean thermal trajectories can substantially underestimate population survival. To connect single-event survival to cumulative growth, we introduce a thermal forward-rate correction relative to an isothermal reference and incorporate it into a reversible birth--death model. Although the correction at each size may be moderate, its multiplicative accumulation can increase mean first-passage times by many orders of magnitude. The framework provides a general route for identifying post-collision stabilization as a control on gas-phase cluster growth.

physics.chem-ph↗

arXiv2024

Real-time Geoinformation Systems to Improve the Quality, Scalability, and Cost of Internet of Things for Agri-environment Research

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices.

q-bio.QM↗

arXiv2023

Visualization and Characterization of Agricultural Sprays Using Machine Learning based Digital Inline Holography

Accurate characterization of agricultural sprays is crucial to predict in field performance of liquid applied crop protection products. Here we introduce a robust and efficient machine learning (ML) based Digital In-line Holography (DIH) to accurately characterize the droplet field for a wide range of agricultural spray nozzles. Compared to non-ML methods, our method enhances accuracy, generalizability, and processing speed. Our approach employs two neural networks: a modified U-Net to obtain the 3D droplet field from the numerically reconstructed optical field, followed by a VGG16 classifier to reduce false positives from the U-Net prediction. The modified U-Net is trained using holograms generated using a single spray nozzle at three spray locations; center, half-span, and the spray edge to create training data with various number densities and droplet size ranges. VGG16 is trained via the minimum intensity projection of the droplet 3D point spread function. Data augmentation is used to increase the efficiency of classification and make the algorithm generalizable for different measurement settings. The model is validated via NIST traceable glass beads and six agricultural spray nozzles representing various spray characteristics. The results demonstrate a high accuracy rate, with over 90% droplet extraction and less than 5% false positives. Compared to traditional spray measurement techniques, our method offers a significant leap forward in spatial resolution and generalizability. In particular, our method can extract the real cumulative volume distribution of the NIST beads, where the laser diffraction is biased towards droplets moving at slower speeds. Additionally, the ML-based DIH enables the estimation of mass and momentum flux at different locations and the calculation of relative velocities of droplet pairs, which are difficult to obtain via conventional techniques.

physics.flu-dyn↗

arXiv2020

A General Drag Coefficient for Flow over a Sphere

A generalized physics-based expression for the drag coefficient of spherical particles moving in a fluid is derived. The proposed correlation incorporates essential rarefied physics, low-speed hydrodynamics, and shock-wave physics to accurately model the particle-drag force for a wide range of Mach and Knudsen numbers (and therefore Reynolds number) a particle may experience. Owing to the basis of the derivation in physics-based scaling laws, the proposed correlation embeds gas-specific properties and has explicit dependence on the ratio of specific heat capacities at constant pressure and constant volume. The correlation is applicable for arbitrary particle relative velocity, particle diameter, gas pressure, gas temperature, and surface temperature. Compared to existing drag models, the correlation is shown to more accurately reproduce a wide range of experimental data. Finally, the new correlation is applied to simulate dust particles' trajectories in high-speed flow, relevant to a spacecraft entering the Martian atmosphere. The enhanced surface heat flux due to particle impact is found to be sensitive to the particle drag model.

physics.flu-dyn↗