Engineering topics

Zhang, Meng

Publications and source records attributed to Zhang, Meng.

5 records

DOE OSTI2026

Exposing and Reducing Biases of Simulating Mixed-Phase Clouds in the Convection-Permitting E3SM Atmosphere Model: Lessons From an Arctic Cold-Air Outbreak

Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener–Bergeron–Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Geosciences↗

DOE OSTI2025

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Bogenschutz, Peter A. [Lawrence Livermore National↗

DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-richRhodotorula glutiniscells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

Science & Technology - Other Topics↗

NASA NTRS1999

Experimental Studies of the Interaction Between a Parallel Shear Flow and a Directionally-Solidifying Front

It has long been recognized that flow in the melt can have a profound influence on the dynamics of a solidifying interface and hence the quality of the solid material. In particular, flow affects the heat and mass transfer, and causes spatial and temporal variations in the flow and melt composition. This results in a crystal with nonuniform physical properties. Flow can be generated by buoyancy, expansion or contraction upon phase change, and thermo-soluto capillary effects. In general, these flows can not be avoided and can have an adverse effect on the stability of the crystal structures. This motivates crystal growth experiments in a microgravity environment, where buoyancy-driven convection is significantly suppressed. However, transient accelerations (g-jitter) caused by the acceleration of the spacecraft can affect the melt, while convection generated from the effects other than buoyancy remain important. Rather than bemoan the presence of convection as a source of interfacial instability, Hurle in the 1960s suggested that flow in the melt, either forced or natural convection, might be used to stabilize the interface. Delves considered the imposition of both a parabolic velocity profile and a Blasius boundary layer flow over the interface. He concluded that fast stirring could stabilize the interface to perturbations whose wave vector is in the direction of the fluid velocity. Forth and Wheeler considered the effect of the asymptotic suction boundary layer profile. They showed that the effect of the shear flow was to generate travelling waves parallel to the flow with a speed proportional to the Reynolds number. There have been few quantitative, experimental works reporting on the coupling effect of fluid flow and morphological instabilities. Huang studied plane Couette flow over cells and dendrites. It was found that this flow could greatly enhance the planar stability and even induce the cell-planar transition. A rotating impeller was buried inside the sample cell, driven by an outside rotating magnet, in order to generate the flow. However, it appears that this was not a well-controlled flow and may also have been unsteady. In the present experimental study, we want to study how a forced parallel shear flow in a Hele-Shaw cell interacts with the directionally solidifying crystal interface. The comparison of experimental data show that the parallel shear flow in a Hele-Shaw cell has a strong stabilizing effect on the planar interface by damping the existing initial perturbations. The flow also shows a stabilizing effect on the cellular interface by slightly reducing the exponential growth rate of cells. The left-right symmetry of cells is broken by the flow with cells tilting toward the incoming flow direction. The tilting angle increases with the velocity ratio. The experimental results are explained through the parallel flow effect on lateral solute transport. The phenomenon of cells tilting against the flow is consistent with the numerical result of Dantzig and Chao.

Zhang, Meng↗