Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature map”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36

The trans-regulatory landscape of gene networks in plants

The transcriptional effector domains of transcription factors play a key role in controlling gene expression; however, their functional nature is poorly understood, hampering our ability to explore this fundamental dimension of gene regulatory networks. To map the trans-regulatory landscape in a complex eukaryote, we systematically characterized the putative transcriptional effector domains of over 400 Arabidopsis thaliana transcription factors for their capacity to modulate transcription. We demonstrate that transcriptional effector activity can be integrated into gene regulatory networks capable of elucidating the functional dynamics underlying gene expression patterns. We further show how our characterized domains can enhance genome engineering efforts and reveal how plant transcriptional activators share regulatory features conserved across distantly related eukaryotes. Our results provide a framework to systematically characterize the regulatory role of transcription factors at a genome-scale in order to understand the transcriptional wiring of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗

Pushing the limits: Resolving paleoseawater signatures in nanoscale fluid inclusions by atom probe tomography

New insight into the geochemistry of ancient environments can be gained through structural and chemical analyses of nanometer-scale features within minerals. Here, we present recent developments using atom probe tomography (APT) enabling direct visualization of nanoscale fluid inclusions trapped within pyrite (FeS 2 ) and thereby chemical characterization of remnant seawater. Pyrite framboids (spherical clusters of nanocrystals) were sampled from the Middle Devonian Leicester Pyrite Member (New York). Scanning transmission electron microscopy shows low density regions distributed within the pyrite consistent with nanoscale pores (<4 nm in size). APT 3D visualization and compositional mapping reveals that the nanopores are filled with water. The inclusions appear to preserve the elemental signature of the water column in which the framboids formed, specifically seawater components including Na, K, Mg, and Ca. Mg/Ca ratios within the pyrite were generally measured to be within 0.6±0.2 – consistent with calcite-dominated seawater conditions existing in the Middle Devonian. Furthermore, this study demonstrates the potential for a novel approach to reconstruct paleoenvironmental conditions from coupled elemental and structural analyses of nanoscale fluid inclusions.

54 ENVIRONMENTAL SCIENCES↗

Hybrid Power Plants: Status of Operating and Proposed Plants, 2023 Edition [Slides]

Improving battery technology and the growth of variable renewable generation are driving a surge of interest in “hybrid” power plants that combine, for example, wind or solar generating capacity with co-located batteries. While most of the current interest involves pairing photovoltaic (PV) plants with batteries, other types of hybrid or co-located plants with wide-ranging configurations have been part of the U.S. electricity mix for decades. This annually updated briefing tracks and maps existing hybrid or co-located plants across the United States while also synthesizing data from power purchase agreements (PPAs) and generation interconnection queues to shed light on near- and long-term development pipelines. The scope includes “co-located hybrids” that pair two or more resources (e.g., multiple types of generation and/or generation with storage) that are operated largely independently behind a single point of interconnection, and “full hybrids” that also feature coordinated operations of the co-located resources. The focus is on plants with one megawatt (MW) or more of capacity; smaller (often behind-the-meter) projects are also increasingly common, but are not included in this data synthesis. Key findings from the latest briefing include: -At the end of 2022, there were 374 hybrid plants (>1 MW) operating across the United States (+25% compared to the end of 2021), totaling nearly 41 GW of generating capacity (+15%) and 5.4 GW/15.2 GWh of energy storage (+69%/+88%). PV+storage plants are by far the most common, dominating in terms of plant number (213), storage capacity (4.0 GW/12.5 GWh), storage:generator capacity ratio (49%), and storage duration (3.1 hours). But there are nearly twenty other hybrid plant configurations as well, including several different fossil hybrid categories (each dominated by the fossil component) as well as wind+storage, wind+PV, wind+PV+storage, geothermal+PV, and others. -Last year was another strong year for PV+storage hybrids in particular: 59 of the 62 hybrids added in 2022 were PV+storage. As of the end of 2022, there was roughly as much storage capacity operating within PV+storage hybrid plants as in standalone storage plants (~4 GW each). In storage energy terms, however, PV+storage edged out standalone storage by ~2 GWh (12.5 GWh vs. 10.4 GWh, respectively). -Interconnection queue data show continued strong developer interest in hybridization. At the close of 2022, there were 51% more hybrid plants—representing 59% more generating capacity—in interconnection queues across the United States than there were at the end of 2021. Solar dominates these proposed plants as well: at the close of 2022, there were 457 GW of solar capacity proposed as a hybrid (representing ~48% of all solar capacity in the queues), most typically pairing PV with battery storage. At the same time, there were 24 GW of wind capacity proposed as a hybrid (representing ~8% of all wind capacity in the queues), again most-often pairing wind with storage. Meanwhile, more than half of all storage in the queues is estimated to be part of a hybrid plant. While many of the plants proposed in the queues will not ultimately reach commercial operations, the depth of interest in hybrid plants—especially PV+storage—is notable, particularly in certain regions. For example, in CAISO, 97% of all solar capacity and 45% of all wind capacity in the queues is proposed as a hybrid. -The report also surveys power purchase agreement (PPA) price data from a sample of operating and proposed PV+storage plants. Though PV+storage PPA prices have fallen over time, “levelized storage adders” have recently increased somewhat to ~$\$ $7000/MW-month, ~$\$ $60/MWh-stored (assuming one full cycle per day), or ~$\$ $15/MWh-PV. Some of the recent price increase could simply reflect a trend towards higher battery:PV capacity ratios over time, which increases costs, all else being equal. The well-publicized impact of inflationary and supply chain pressures on battery prices is no doubt a contributor as well.

25 ENERGY STORAGE↗

Hybrid Power Plants: Status of Operating and Proposed Plants, 2024 Edition [Slides]

Improving battery technology and the growth of variable renewable generation are driving a surge of interest in “hybrid” power plants that combine, for example, wind or solar generating capacity with co-located batteries. While most of the current interest involves pairing photovoltaic (PV) plants with batteries, other types of hybrid or co-located plants with wide-ranging configurations have been part of the U.S. electricity mix for decades. This annually updated briefing tracks and maps existing hybrid or co-located plants across the United States while also synthesizing data from power purchase agreements (PPAs) and generation interconnection queues to shed light on near- and long-term development pipelines. The scope includes “co-located hybrids” that pair two or more resources (e.g., multiple types of generation and/or generation with storage) that are operated largely independently behind a single point of interconnection, and “full hybrids” that also feature coordinated operations of the co-located resources. The focus is on plants with one megawatt (MW) or more of capacity; smaller (often behind-the-meter) projects are also increasingly common, but are not included in this data synthesis.

14 SOLAR ENERGY↗

A Particle-in-Cell Method for Plasmas with a Generalized Momentum Formulation, Part II: Enforcing the Lorenz Gauge Condition

In a previous paper Christlieb et al. (A particle-in-cell method for plasmas with a generalized momentum formulation, part I: Model formulation, 2024), we developed a new particle-in-cell (PIC) method for the relativistic Vlasov–Maxwell system in which the electromagnetic fields and the equations of motion for the particles were cast in terms of scalar and vector potentials through a Hamiltonian formulation. This new method evolved the potentials under the Lorenz gauge using integral equation methods. New methods to construct spatial derivatives of the potentials that converge at the same rates as the fields were also presented. The new particle method was compared against standard explicit discretizations, including the well-known FDTD-PIC method, for a range of applications involving sheaths and particle beams. Here, this paper extends this new class of methods by focusing on the enforcement the Lorenz gauge condition in both exact and approximate forms using co-located meshes. A time-consistency property of the proposed field solver for the vector potential form of Maxwell’s equations is established, which is shown to preserve the equivalence between the semi-discrete Lorenz gauge condition and the analogous semi-discrete continuity equation. Using this property, we present three methods to enforce a semi-discrete gauge condition. The first method introduces an update for the continuity equation that is consistent with the discretization of the Lorenz gauge condition. Both the finite difference and spectral implementations satisfy this discrete gauge condition to machine precision. The second approach we propose enforces a semi-discrete continuity equation using the boundary integral solution to the field equations. The potential benefit of this approach is that it eliminates spatial derivatives that appear on the particle data, namely the current density, which is often calculated by linear combinations of low-order spline basis functions. This method is ideally suited to boundary integral equation methods that invert multi-dimensional operators without dimensional splitting techniques and will be the subject of future work. The third approach introduces a gauge correcting method that makes direct use of the gauge condition to modify the scalar potential and uses local maps for both the charge and current densities. This results in a gauge error, as the maps do not enforce the continuity equation. The vector potential coming from the current density is taken to be exact, and using the Lorenz gauge, we compute a correction to the scalar potential that makes the two potentials satisfy the gauge condition. This method also enforces the gauge condition to machine precision. We demonstrate two of the proposed methods in the context of periodic domains. Problems defined on bounded domains, including those with complex geometric features remain an ongoing effort. However, this work shows that it is possible to design computationally efficient methods that can effectively enforce the Lorenz gauge condition in a non-staggered PIC formulation.

97 MATHEMATICS AND COMPUTING↗

Utah FORGE: Well 16A(78)-32 Core Analysis Results

This dataset features petrographic, X-Ray Diffraction (XRD), and Scanning Electron Microscopy Analyses of core samples from well 16A(78)-32, which serves as the injection well for the Utah FORGE Enhanced Geothermal System (EGS) project. In addition to an overview of the analysis, the following items have been included here: images of 21 entire thin sections in plane-polarized and cross-polarized light from 16 depths, scanning electron microscope back scatter electron photomosaic maps of 21 thin sections from 16 depths, bulk XRD data from 16 depths, clay-sized XRD patterns from 16 depths, an optical petrography report, and scanning electron microscopy and energy dispersive spectroscopy analyses of eight selected samples.

15 GEOTHERMAL ENERGY↗

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗

Magnetic Field Mapping of a 2.5 T Fixed-Field HTS Gantry Magnet for Proton Therapy

We present results from testing a high-temperature superconducting (HTS) magnet prototype for proton therapy. This magnet is specifically designed for a novel rotating gantry capable of delivering the entire proton beam energy range (70225 MeV) while maintaining a fixed magnetic field in the superconducting magnets. The gantry layout simplifies the magnet design, enabling the use of straight, flat racetrack Bi-2223 (DI-BSCCO) coil technology and operation at higher temperatures (1015 K). The magnet has a non-linear field distribution for bending and focusing the proton beams. To validate this feature, we developed a system for measuring the magnetic field distribution in the magnet aperture. We present the design of this hall probe array and experimental results from two different magnet tests at 4.2 K in a liquid helium bath. These results are compared with the simulated field distribution and discussed in the context of the required field quality for the application.

Mosat, M↗

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗

Dispersion Leverage Coronagraph: a nulling coronagraph for use on primary objective grating telescopes

Here, we present the Dispersion Leverage Coronagraph (DLC), a variation of the Achromatic Interfero Coronagraph (AIC) that is designed for optical systems featuring large, dispersive primary objective gratings. DLC was originally designed for the Diffractive Interfero Coronagraph Exoplanet Resolver (DICER), a notional 20 m class infrared space telescope utilizing the enhanced one-dimensional angular resolution of large diffraction gratings to discover and characterize near-Earth exoplanets. Here we develop the theoretical foundation for DLC, and apply it to DICER as an example use case. We derive important properties of the DLC system, including focal plane transmission maps, stellar leakage, residual optical path difference tolerance, and pointing error/jitter considerations. Ultimately, we found that DLC effectively nulls an on-axis target across the entire spectrum in the focal plane, allowing for 2D/λ diffraction-limited imaging. It requires asymmetrical fine-guidance tolerances on pointing error/jitter. We work through a benchmark DICER design, explaining the need for a second disperser to reduce background from Zodiacal light, and showing that it could plausibly find and characterize ∼4 nearby, habitable exoplanets around Sun-like stars in a 7-year mission; about 30% of the habitable exoplanets within 8 pc were found in our simulation. The DLC may be useful for any application requiring extremely high-resolution, close companion spectroscopy.

AIC↗

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li 2 , Li 3 , and Li 4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.

36 MATERIALS SCIENCE↗

A High-Efficient Solution for Electro-Hydraulic Actuators with Energy Regeneration Capability

This paper proposes a cost-effective, robustly practical solution for a high-efficient electro-hydraulic actuator (EHA) for linear drives. The solution addresses the challenges of high costs and limited efficiency that limit the application of EHA for the electrification of many off-road vehicles. Based on a variable-speed electric motor and a low-cost technology for a fixed-displacement pump, the proposed solution is suitable for a compact implementation and it is capable of energy regeneration under overrunning load conditions. Two different architectures—an open circuit and a closed circuit one—are considered to determine the most practical and efficient configuration as well as the control features. Both architectures minimize throttling losses associated with the conventional regulation of hydraulic actuators. Actuator velocities below the minimum and beyond the maximum allowable pump flow rate are achieved in both architectures using a proportional valve that bypasses the pump flow. The paper describes the design methodology as well as the simulation and experimental activities performed to verify the control strategies as well as energy efficiency. Considering an EHA system of about 20 kW, a complete efficiency map of the system is determined for all the possible four quadrants of operation thus including both resistive and overrunning loads, where energy recuperation is implemented. Simulation and experimental results show that the energy efficiency of the hydraulic systems can reach 84.7% in resistive phases. In assistive phases, the hydraulic system can recover up to 81.8% of the actuator energy. Here, the comparison between open-circuit and closed-circuit structures shows the advantages of the former in terms of energy efficiency. The proposed solution is suitable for electrified hydraulic actuation systems, in both industrial and mobile applications. With the advantages of the low cost of the hydraulic unit and high energy efficiency, it is particularly suitable for mobile applications such as construction machines, that are recently experiencing a significant electrification trend.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Molecular insight into the anion effect and free volume effect of CO 2 solubility in multivalent ionic liquids

For many years, experimental and theoretical studies have investigated the solubility of CO 2 in a variety of ionic liquids (ILs), but the overarching absorption mechanism is still unclear. Currently, two different factors are believed to dominate the absorption performance: (a) the fractional free volume (FFV) accessible for absorption; and (b) the nature of the CO 2 interactions with the anion species. The FFV is often more influential than the specific choice of the anion, but neither mechanism provides a complete picture. Herein, we have attempted to decouple these mechanisms in order to provide a more definitive molecular-level perspective of CO 2 absorption in IL solvents. We simulate a series of nine different multivalent ILs comprised of imidazolium cations and sulfonate/sulfonimide anions tethered to benzene rings, along with a comprehensive analysis of the CO 2 absorption and underlying molecular-level features. We find that the CO 2 solubility has a very strong, linear correlation with respect to FFV, but only when comparisons are constrained to a common anion species. The choice of anion results in a fundamental remapping of the correlation between CO 2 solubility and FFV. Overall, the free volume effect dominates in the ILs with smaller FFV values, while the choice of anion becomes more important in the systems with larger FFVs. Our proposed mechanistic map is intended to provide a more consistent framework for guiding further IL design for gas absorption applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Procedure for locating oil and gas wells in the Appalachian Basin

Locating undocumented (or poorly documented) oil and gas wells for environmental assessment is often difficult. Remnant features that confirm the presence of a well (intact casing/wellhead, well bore, etc.) are typically less than a meter in size and often are obscured from direct observation on the ground or from the air (by dense vegetation, for example). To efficiently find such features, it is useful to first systematically compile publicly available digital data at progressively smaller scales prior to embarking on field campaigns. Further, the information presented here describes the procedure developed and used by the U.S. Department of Energy's National Energy Technology Laboratory to locate potential oil and gas well sites for follow-up field verification and characterization. Digital data are first compiled from national and state resources such as well location/production databases, historical topographic maps, historical aerial photographs, and LiDAR data. Although each data set is likely to be incomplete or inaccurate to some extent, combining the data resources using geographic information system technology can generate potential well site targets with a higher degree of confidence, which improves the efficiency of fieldwork activities. This workflow was developed in the Appalachian Basin region, and although certain aspects may be unique, the general process would be applicable to locating undocumented wells in other regions.

54 ENVIRONMENTAL SCIENCES↗

Utah FORGE 5-2419: Temperature Testing of Proppants for EGS and Stimulation of Electromagnetic Fracture Mapping Using Electrically-Conducive Proppants - 2024 Annual Workshop Presentation

This is a presentation on the Temperature Testing of Proppants for EGS and Stimulation of Electromagnetic Fracture Mapping Using Electrically-Conducive Proppants by Stevens Institute of Technology, presented by Cheng Chen. This video slide presentation discusses well controlled laboratory experiments testing the high-temperature performance of electrically-conductive and non-electrically-conductive proppants in maintaining fractures hydraulic and electrical conductivities to understand and quantify the thermal-hydrologic-mechanical-chemical mechanisms that regulate the observed behaviors. This will be tested using proppant/fracture imaging via borehole electromagnetic measurements. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗

Deep learning models for interpretation of point of care ultrasound in military working dogs

Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs. Methods: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions. Results: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation. Discussion: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.

59 BASIC BIOLOGICAL SCIENCES↗

Tracking precipitation features and associated large-scale environments over southeastern Texas

Abstract. Deep convection initiated under different large-scale environmental conditions exhibits different precipitation features and interacts with local meteorology and surface properties in distinct ways. Here, we analyze the characteristics and spatiotemporal patterns of different types of convective systems over southeastern Texas using 13 years of high-resolution observations and reanalysis data. We find that mesoscale convective systems (MCSs) contribute significantly to both mean and extreme precipitation in all seasons, while isolated deep convection (IDC) plays a role in intense precipitation during summer and fall. Using self-organizing maps (SOMs), we found that convection can occur under unfavorable conditions without large-scale lifting or moisture convergence. In spring, fall, and winter, front-related large-scale meteorological patterns (LSMPs) characterized by low-level moisture convergence act as primary triggers for convection, while the remaining storms are associated with an anticyclonic pattern and orographic lifting. In summer, IDC events are mainly associated with front-related and anticyclonic LSMPs, while MCSs occur more in front-related LSMPs. We further tracked the life cycle of MCS and IDC events using the Flexible Object Tracker algorithm over southeastern Texas. MCSs frequently initiate west of Houston, traveling eastward for around 8 h to southeastern Texas, while IDC events initiate locally. The average duration of MCSs in southeastern Texas is 6.1 h, approximately 4.1 times the duration of IDC events. Diurnally, the initiation of convection associated with favorable LSMPs peaks at 11:00 UTC, 3 h earlier than that associated with anticyclones.

54 ENVIRONMENTAL SCIENCES↗