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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Test and Validate Distributed Coaxial Cable Sensors for in situ Condition Monitoring of Coal-Fired Boiler Tubes

This project aims to test, validate, and advance the technology readiness level (from TRL5 to TRL7) of a novel low-cost distributed stainless-steel/ceramic coaxial cable sensing (SSC-CCS) technology for in situ monitoring of the boiler tube temperature in existing coal-fired power plants. The novel SSC-CCS sensing technology and associated condition-based monitoring (CBM) software to be demonstrated in this project will lead to an improved understanding of the boiler tube failure mechanisms and a prognostic system to improve the overall performance, reliability, and flexibility of the nation’s coal-fired power plant fleet. A boiler tube monitoring system with distributed coaxial cable temperature sensors and a sensor acquisition system was constructed. The high-temperature coaxial cable sensor with a length of 1.3m was made by using a quartz tube (1mm inner diameter (ID) and 6mm outer diameter (OD)) to concentrically separate a 304 stainless-steel (SS) rod (1mm OD) and SS tube (7.94mm OD and 6.16mm ID). The sensor acquisition system includes a vector network analyzer (VNA), a radio frequency (RF) power amplifier, multiple switches and a USB hub. The distributed stainless-steel quartz coaxial cable sensor (SSQ-CCS) had a linear response to temperature with a resolution uncertainty of σ = 0.77℃. To withstand the harsh conditions of 3,300 steam pressures and 800℃ high temperatures, the sensor was shielded by a protective tube made of the same material as the boiler tube. The protection tube had an OD of 1.5 inches and a thickness of 0.25 inches. In the laboratory tests, the sensor showed good sensitivity and fast response. The drift was bounded between +0.33% and -0.67% during a test at 600℃ for 350 hours, indicating good stability of the sensor. A field test was conducted where four sensors were welded on four superheat tubes (SH-Ts) at a coal-fired power station over 400 days. Conventional thermocouples were welded to the superheater tubes alongside the coaxial cable sensors for the purpose of comparison. Two sensors were capable of distributed sensing, with three multiplexed sensing sections. The other two sensors were single section. During the 400-day test period, the power plant experienced startups and shutdowns. At the steady state operations, the temperature of the boiler tube is about 600℃ (1112°F). The sensors recorded the entire coal-firing processes (start-up, steady state, and shut-down) and the glitch event. A GSM modem and a Watchdog were added to the system to ensure reliable data recording. The GSM modem sent daily messages to plant managers and Clemson team to inform the status of the sensor system. If the system was not normally working, the Watchdog would reboot the system automatically. The new coaxial cable based distributed sensing technology has been proven to be successful in both laboratory and field tests. A comprehensive four-stage multi-physics computational framework has been developed to assist the design, optimization, installation, and operation of SSQ-CCS. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. A conditional-based monitoring system is implemented as well. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Integration of sensors through additive manufacturing leading to increased efficiencies of gas turbines for power generation and propulsion

To realize the full capability of additively manufactured components in complex energy systems, it is imperative to minimize early component failures during development phases and during operation. Traditional field feedback timelines and offline inspection protocols significantly reduce the design-manufacturing iteration times. To address this specific question, the project developed and demonstrated a method for the integration of sensors into complex components through additive manufacturing. The team used gas turbine engines as a platform, which meets the need of both power generation and propulsion and offer opportunities for cost reductions and efficiency increases. The innovation of this intelligent integration of sensors into complex components uniquely customized to address questions of integrity and durability for additively manufactured components. With real-time sensing data from additively manufactured components, turbine manufacturers will realize higher efficiencies, reduced component failures, and a 30-50% acceleration in product deployment of high efficiency gas turbine components due to a faster reduction in component risk assessment under actual operating conditions. This is a transformative shift towards a data-driven design and qualification of additively manufactured gas turbine components. To directly integrate sensors into additively manufactured components with all the complexities of actual hardware, powder bed fusion (direct metal laser sintering) and laser metal deposition technologies was developed. Validation took take place in two university laboratories both of which contain actual engine hardware and closely simulate a gas turbine prior to demonstrating the technology in a turbine development test. Indeed, two major technologies from this research cold impact turbine systems in the near future: (1) higher efficiency materials and designs enabled by additive manufacturing with 50% faster design to manufacturing cycle time, to enable faster time-to-market targets; and (2) integration of sensors into additively manufactured components enabling broad health and condition based prognostics for faster component and engine risk reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2010

Intelligent Component Monitoring for Nuclear Power Plants

Reliability and economy are two major concerns for a nuclear power generation system. Next generation nuclear power reactors are being developed to be more reliable and economic. An effective and efficient surveillance system can generously contribute toward this goal. Recent progress in computer systems and computational tools has made it necessary and possible to upgrade current surveillance/monitoring strategy for better performance. For example, intelligent computing techniques can be applied to develop algorithm that help people better understand the information collected from sensors and thus reduce human error to a new low level. Incidents incurred from human error in nuclear industry are not rare and have been proven costly. The goal of this project is to develop and test an intelligent prognostics methodology for predicting aging effects impacting long-term performance of nuclear components and systems. The approach is particularly suitable for predicting the performance of nuclear reactor systems which have low failure probabilities (e.g., less than 10-6 year-). Such components and systems are often perceived as peripheral to the reactor and are left somewhat unattended. That is, even when inspected, if they are not perceived to be causing some immediate problem, they may not be paid due attention. Attention to such systems normally involves long term monitoring and possibly reasoning with multiple features and evidence, requirements that are not best suited for humans.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Parkinson’s disease biomarkers: perspective from the NINDS Parkinson’s Disease Biomarkers Program

Biomarkers for Parkinson’s disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson’s Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset.

Gwinn, Katrina↗
DOE OSTI2015

The effect of bi-terminal PEGylation of an integrin α v β 6 -targeted 18 F peptide on pharmacokinetics and tumor uptake

Radiotracers based on the peptide A20FMDV2 selectively target the cell surface receptor integrin α v β 6 . This integrin has been identified as a prognostic indicator correlating with the severity of disease for several challenging malignancies. In previous studies of A20FMDV2 peptides labeled with 4- 18 F-fluorobenzoic acid ( 18 F-FBA), we have shown that the introduction of poly(ethylene glycol) (PEG) improves pharmacokinetics, including increased uptake in α v β 6 -expressing tumors. The present study evaluated the effect of site-specific C-terminal or dual (N- and C-terminal) PEGylation, yielding 18 F-FBA-A20FMDV2-PEG 28 (4) and 18 F-FBA-PEG 28 -A20FMDV2-PEG 28 (5), on α v β 6 -targeted tumor uptake and pharmacokinetics. The results are compared with 18 F-FBA–labeled A20FMDV2 radiotracers (1–3) bearing either no PEG or different PEG units at the N terminus. The radiotracers were prepared and radiolabeled on solid phase. Using 3 cell lines, DX3puroβ6 (α v β 6 +), DX3puro (α v β 6 –), and BxPC-3 (α v β 6 +), we evaluated the radiotracers in vitro (serum stability; cell binding and internalization) and in vivo in mouse models bearing paired DX3puroβ6–DX3puro and, for 5, BxPC-3 xenografts. Here, the size and location of the PEG units significantly affected α v β 6 targeting and pharmacokinetics. Although the C-terminally PEGylated 4 showed some improvements over the un-PEGylated 18 F-FBA-A20FMDV2 (1), it was the bi-terminally PEGylated 5 that displayed the more favorable combination of high α v β 6 affinity, selectivity, and pharmacokinetic profile. In vitro, 5 bound to α v β 6 -expressing DX3puroβ6 and BxPC-3 cells with 60.5% ± 3.3% and 48.8% ± 8.3%, respectively, with a significant fraction of internalization (37.2% ± 4.0% and 37.6% ± 4.1% of total radioactivity, respectively). By comparison, in the DX3puro control 5 showed only 3.0% ± 0.5% binding and 0.9% ± 0.2% internalization. In vivo, 5 maintained high, α v β 6 -directed binding in the paired DX3puroβ6–DX3puro model (1 h: DX3puroβ6, 2.3 ± 0.2 percentage injected dose per gram [%ID/g]; DX3puroβ6/DX3puro ratio, 6.5:1; 4 h: 10.7:1). In the pancreatic BxPC-3 model, uptake was 4.7 ± 0.9 %ID/g (1 h) despite small tumor sizes (20–80 mg). In conclusion, the bi-PEGylated radiotracer 5 showed a greatly improved pharmacokinetic profile, beyond what was predicted from individual N- or C-terminal PEGylation. It appears that the 2 PEG units acted synergistically to result in an improved metabolic profile including high α v β 6 + tumor uptake and retention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SPRUCE Representing Northern Peatland Microtopography and Hydrology within the Community Land Model: Modeling Archive

Here we provide model code, inputs, outputs and evaluation datasets for a new configuration of the Community Land Model (CLM) for SPRUCE, which includes a fully prognostic water table calculation for SPRUCE. Our structural and process changes to CLM focus on modifications needed to represent the hydrologic cycle of bogs environment with perched water tables, as well as distinct hydrologic dynamics and vegetation communities of the raised hummock and sunken hollow microtopography characteristic of SPRUCE and other peatland bogs. The modified model was parameterized and independently evaluated against observations from an ombrotrophic raised-dome bog in northern Minnesota (S1-Bog), the site for the Spruce and Peatland Responses Under Climatic and Environmental Change experiment (SPRUCE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Cul4A Modulates Invasion and Metastasis of Lung Cancer through Regulation of ANXA10

Cullin 4A (Cul4A) is overexpressed in a number of cancers and has been established as an oncogene. This study aimed to elucidate the role of Cul4A in lung cancer invasion and metastasis. We observed that Cul4A was overexpressed in non-small cell lung cancer (NSCLC) tissues and the overexpression of Cul4A was associated with poor prognosis after surgical resection and it also decreased the expression of the tumor suppressor protein annexin A10 (ANXA10). The knockdown of Cul4A was associated with the upregulation of ANXA10, and the forced expression of Cul4A was associated with the downregulation of ANXA10 in lung cancer cells. Further studies showed that the knockdown of Cul4A inhibited the invasion and metastasis of lung cancer cells, which was reversed by the further knockdown of ANXA10. In addition, the knockdown of Cul4A inhibited lung tumor metastasis in mouse tail vein injection xenograft models. Notably, Cul4A regulated the degradation of ANXA10 through its interaction with ANXA10 and ubiquitination in lung cancer cells. Our findings suggest that Cul4A is a prognostic marker in NSCLC patients, and Cul4A plays important roles in lung cancer invasion and metastasis through the regulation of the ANXA10 tumor suppressor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Distinct Clinical Impact and Biological Function of Angiopoietin and Angiopoietin-like Proteins in Human Breast Cancer

Secreted angiopoietin/angiopoietin-like (ANGPT/ANGPTL) proteins are involved in many biological processes. However, the role of these proteins in human breast cancers (BCs) remains largely unclear. Here, we conducted integrated omics analyses to evaluate the clinical impact of ANGPT/ANGPTL proteins and to elucidate their biological functions. In BCs, we identified rare mutations in ANGPT/ANGPTL genes, frequent gains of ANGPT1, ANGPT4, and ANGPTL1, and frequent losses of ANGPT2, ANGPTL5, and ANGPTL7, but observed that ANGPTL1, 2, and 4 were robustly downregulated in multiple datasets. The expression levels of ANGPTL1, 5, and 8 were positively correlated with overall survival (OS), while the expression levels of ANGPTL4 were negatively correlated with OS. Additionally, the expression levels of ANGPTL1 and 7 were positively correlated with distant metastasis-free survival (DMFS), while the expression levels of ANGPT2 and ANGPTL4 were negatively correlated with DMFS. The prognostic impacts of ANGPT/ANGPTL genes depended on the molecular subtypes and on clinical factors. We discovered that various ANGPT/ANGPTL genes were co-expressed with various genes involved in different pathways. Finally, with the exception of ANGPTL3, the remaining genes showed significant correlations with cancer-associated fibroblasts, endothelial cells, and microenvironment score, whereas only ANGPTL6 was significantly correlated with immune score. Our findings provide strong evidence for the distinct clinical impact and biological function of ANGPT/ANGPTL proteins, but the question of whether some of them could be potential therapeutic targets still needs further investigation in BCs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
DOE OSTI2011

Delineating Chromosomal Breakpoints in Radiation-Induced Papillary Thyroid Cancer

Recurrent translocations are well known hallmarks of many human solid tumors and hematological disorders, where patient- and breakpoint-specific information may facilitate prognostication and individualized therapy. In thyroid carcinomas, the proto-oncogenes RET and NTRK1 are often found to be activated through chromosomal rearrangements. However, many sporadic tumors and papillary thyroid carcinomas (PTCs) arising in patients with a history of exposure to elevated levels of ionizing irradiation do not carry these known abnormalities. We developed a rapid scheme to screen tumor cell metaphase spreads and identify candidate genes of tumorigenesis and neoplastic progression for subsequent functional studies. Using a series of overnight fluorescence in situ hybridization (FISH) experiments with pools comprised of bacterial artificial chromosome (BAC) clones, it now becomes possible to rapidly refine breakpoint maps and, within one week, progress from the low resolution Spectral Karyotyping (SKY) maps or Giemsa-banding (G-banding) karyotypes to fully integrated, high resolution physical maps including a list of candiate genes in the critical regions.

molecular cytogenetics↗
DOE OSTI2023

Coccidioidomycosis Osteoarticular Dissemination

Valley fever or coccidioidomycosis is a pulmonary infection caused by species of Coccidioides fungi that are endemic to California and Arizona. Skeletal coccidioidomycosis accounts for about half of disseminated infections, with the vertebral spine being the preferred site of dissemination. Most cases of skeletal coccidioidomycosis progress to bone destruction or spread to adjacent structures such as joints, tendons, and other soft tissues, causing significant pain and restricting mobility. Manifestations of such cases are usually nonspecific, making diagnosis very challenging, especially in non-endemic areas. The lack of basic knowledge and research data on the mechanisms defining susceptibility to extrapulmonary infection, especially when it involves bones and joints, prompted us to survey available clinical and animal data to establish specific research questions that remain to be investigated. In this review, we explore published literature reviews, case reports, and case series on the dissemination of coccidioidomycosis to bones and/or joints. We highlight key differential features with other conditions and opportunities for mechanistic and basic research studies that can help develop novel diagnostic, prognostic, and treatment strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Preclinical Validation of the Heparin-Reactive Peptide p5+14 as a Molecular Imaging Agent for Visceral Amyloidosis

Amyloid is a complex pathologic matrix comprised principally of para-crystalline protein fibrils and heparan sulfate proteoglycans. Systemic amyloidoses are rare (~3500 new cases per year in the US); thus, routine diagnosis is often challenging, and effective treatment options are limited, resulting in high morbidity and mortality rates. Glycosaminoglycans contribute inextricably to the formation of amyloid fibrils and foster the deposition of amyloid in tissues. Those present in amyloid deposits are biochemically and electrochemically distinct from glycosaminoglycans found in the plasma membrane and extracellular matrices of healthy tissues due to the presence of a high degree of heparin-like hypersulfation. We have exploited this unique property and evaluated heparin-reactive peptides, such as p5+14. Herein we show efficacious detection of murine systemic amyloid in vivo by using molecular imaging, and the specific targeting of the peptide to major forms of human amyloid in tissue sections. Furthermore, we have demonstrated that the peptide also binds synthetic amyloid fibrils that lack glycosaminoglycans implying that the dense anionic motif present on heparin is mimicked by the amyloid protein fibril itself. These biochemical and functional data support the translation of radiolabeled peptide p5+14 for the clinical imaging of amyloid in patients; thus, providing a novel technique for prognostication, patient stratification, and monitoring response to therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the spallation neutron source (SNS) facility. HVCM experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive metrics of precision and recall reaching up to 99\% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Radaideh, Majdi↗
DOE OSTI2023

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Yucesan, Yigit↗
DOE OSTI2015

The role of ice nuclei recycling in the maintenance of cloud ice in Arctic mixed-phase stratocumulus

This study investigates the maintenance of cloud ice production in Arctic mixed-phase stratocumulus in large eddy simulations that include a prognostic ice nuclei (IN) formulation and a diurnal cycle. Balances derived from a mixed-layer model and phase analyses are used to provide insight into buffering mechanisms that maintain ice in these cloud systems. We find that, for the case under investigation, IN recycling through subcloud sublimation considerably prolongs ice production over a multi-day integration. This effective source of IN to the cloud dominates over mixing sources from above or below the cloud-driven mixed layer. Competing feedbacks between dynamical mixing and recycling are found to slow the rate of ice lost from the mixed layer when a diurnal cycle is simulated. The results of this study have important implications for maintaining phase partitioning of cloud ice and liquid that determine the radiative forcing of Arctic mixed-phase clouds.

54 ENVIRONMENTAL SCIENCES↗