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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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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection

Abstract A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

Wong, May↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part I: Scheme Description

A novel framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Furthermore, using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is demonstrated that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations.

58 GEOSCIENCES↗
DOE OSTI2021

A Triple-Moment Representation of Ice in the Predicted Particle Properties (P3) Microphysics Scheme

In the original Predicted Particle Properties (P3) bulk microphysics scheme, all ice-phase hydrometeors are represented by one or more “free” ice categories, where the physical properties evolve smoothly through changes to the four prognostic variables (per category), and with a two-moment representation of the particle size distribution. As such, the spectral dispersion cannot evolve independently, which thus results in limitations in representation of ice—in particular, hail—due to necessary constraints in the scheme to prevent excessive gravitational size sorting. To overcome this, P3 has been modified to include a three-moment representation of the size distribution of each ice category through the addition of a fifth prognostic variable, the sixth moment of the size distribution. The details of the three-moment ice parameterization in P3 are provided. The behavior of the modified scheme, with the single-ice-category configuration, is illustrated through simulations in a simple 1D kinematic model framework as well as with near large-eddy-resolving (250-m grid spacing) 3D simulations of a hail-producing supercell. It is shown that the three-moment ice configuration controls size sorting in a physically based way and leads to an improved capacity to simulate large, heavily rimed ice (hail), including mean and maximum sizes and reflectivity, and thus an overall improvement in the representation of ice-phase particles in the P3 scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Improving Boundary-layer Turbulence and Cloud Processes in CAM with a Higher-order Turbulence Closure Scheme and ASR Measurement

In this study, a simplified intermediately prognostic higher-order turbulence closure (IPHOC) is implemented in the Community Atmosphere Model, version 5 (CAM5), to provide a consistent treatment of subgrid-scale cloud processes, except for deep convection. The planetary boundary layer (PBL) height is prognosticated to better resolve the discontinuity of temperature and moisture above the PBL top. Singlecolumn model tests show that fluxes of liquid water potential temperature and total water, cloud fraction, and liquid water content are improved with this approach. The simplified IPHOC package replaces the boundary layer dry and moist turbulence parameterizations, the shallow convection parameterization, and the liquid-phase part of the cloud macrophysics parameterization in CAM5. CAM5-IPHOC improves the simulation of the low-level clouds off the west coasts of continents and the storm track region in the Southern Hemisphere (SH). The transition from stratocumulus to cumulus clouds is more gradual. There are also improvements on the cloud radiative forcing, especially shortwave, in the subsidence regime. The improvements in the relationships among low cloud amount, surface relative humidity, lower tropospheric stability, and PBL depth are seen in some stratocumulus regions. CAM5-IPHOC, however, produces weaker precipitation at the South Pacific convergence zone than CAM5 because of less energy flux into the SH atmosphere. The more downward surface shortwave radiative cooling and the less top-of-theatmosphere longwave cloud radiative heating in the SH relative to the Northern Hemisphere explains the anomalous cooling and the lesser energy flux into the SH, which is related to the underestimate of extratropical middle/high clouds in the SH.

58 GEOSCIENCES↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods’ ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

DCYTB is a predictor of outcome in breast cancer that functions via iron-independent mechanisms

Background: Duodenal cytochrome b (DCYTB) is a ferrireductase that functions together with divalent metal transporter 1 (DMT1) to mediate dietary iron reduction and uptake in the duodenum. DCYTB is also a member of a 16-gene iron regulatory gene signature (IRGS) that predicts metastasis-free survival in breast cancer patients. To better understand the relationship between DCYTB and breast cancer, we explored in detail the prognostic significance and molecular function of DCYTB in breast cancer. Methods: The prognostic significance of DCYTB expression was evaluated using publicly available microarray data. Signaling Pathway Impact Analysis (SPIA) of microarray data was used to identify potential novel functions of DCYTB. The role of DCYTB was assessed using immunohistochemistry and measurements of iron uptake, iron metabolism, and FAK signaling. Results: High DCYTB expression was associated with prolonged survival in two large independent cohorts, together totaling 1610 patients (cohort #1, p = 1.6e-11, n = 741; cohort #2, p = 1.2e-05, n = 869; log-rank test) as well as in the Gene expression-based Outcome for Breast cancer Online (GOBO) cohort (p < 1.0e-05, n = 1379). High DCYTB expression was also associated with increased survival in homogeneously treated groups of patients who received either tamoxifen or chemotherapy. Immunohistochemistry revealed that DCYTB is localized on the plasma membrane of breast epithelial cells, and that expression is dramatically reduced in high-grade tumors. Surprisingly, neither overexpression nor knockdown of DCYTB affected levels of ferritin H, transferrin receptor, labile iron or total cellular iron in breast cancer cells. Because SPIA pathway analysis of patient microarray data revealed an association between DCYTB and the focal adhesion pathway, we examined the influence of DCYTB on FAK activation in breast cancer cells. These experiments reveal that DCYTB reduces adhesion and activation of focal adhesion kinase (FAK) and its adapter protein paxillin. Conclusions: DCYTB is an important predictor of outcome and is associated with response to therapy in breast cancer patients. DCYTB does not affect intracellular iron in breast cancer cells. Instead, DCYTB may retard cancer progression by reducing activation of FAK, a kinase that plays a central role in tumor cell adhesion and metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Completion Lymph Node Dissection or Radiation Therapy for Sentinel Node Metastasis in Merkel Cell Carcinoma

Background: For sentinel lymph node (SLN) metastasis from Merkel cell carcinoma (MCC), the benefit of completion lymph node dissection (CLND) versus radiation therapy (RT) is unclear. This study compares outcomes for patients with SLN metastasis undergoing CLND or RT. We also evaluated positive non-SLNs as a prognostic factor. Methods: Using a prospective database, we identified MCC patients with SLN metastasis who underwent CLND or RT. At our institution, CLND was recommended for patients with acceptable perioperative risk, while therapeutic RT was offered to those with high perioperative risk. Primary outcomes were MCC-specific survival (MCCSS), disease-free survival (DFS), nodal recurrence-free survival (NRFS), and distant recurrence-free survival (DRFS). Results: From 2006 to 2017, 163 patients underwent CLND (n = 137) or RT (n = 26). Median follow-up was 1.9 years. CLND had no significant differences for MCCSS (5-year survival 71% vs. 64%, p = 1.0), DFS (52% vs. 61%, p = 0.8), NRFS (76% vs. 91%, p = 0.3), or DRFS (65% vs. 75%, p = 0.3) compared with RT. Patients with positive non-SLNs (n = 44) had significantly worse MCCSS (5-year survival 39% vs. 87%, p < 0.001), DFS (35% vs. 60%, p = 0.005), and DRFS (54% vs. 71%, p = 0.03) compared with negative non-SLNs (n = 93). Multivariate analysis showed positive non-SLNs were independently associated with MCCSS, DFS, and DRFS. Conclusions: CLND and RT may have similar outcomes for MCC patients with SLN metastasis when treatment aligns with our institutional practices. For patients undergoing CLND, positive non-SLNs is an important prognostic factor associated with poor survival and distant recurrence. This high-risk group should be considered for adjuvant systemic therapy trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Proposal of a Nomogram for Predicting Survival in Patients with Siewert Type II Adenocarcinoma of the Esophagogastric Junction After Preoperative Radiation

Background: Preoperative radiotherapy tends to be more frequently used for patients with adenocarcinoma of the esophagogastric junction (AEG); however, the prognostic values of postoperative pathologic characteristics in these patients remain unclear. This study aimed to examine the outcomes in Siewert type II AEG patients receiving preoperative radiotherapy to identify the predictive factors for overall survival (OS). Methods and Results: A total of 1818 AEG patients undergoing preoperative radiotherapy were reviewed. Univariate analyses showed that age, sex, histology, tumor grade, positive lymph node (PLN), lymph node ratio, and log odds of positive lymph nodes (LODDS) were significantly correlated with OS; however, only age, grade, PLN, and LODDS were identified as independent risk factors in a multivariate regression model. Subsequently, patients were randomly grouped into training and validation cohorts (1:1 ratio), and the beta coefficients of these variables in the training set were used to generate the nomogram. The composite nomogram showed improved prognostic accuracy in the training, validation, and entire cohorts compared with that of TNM stage alone. Conclusions: In conclusion, our proposed nomogram represents a promising tool for estimating OS in Siewert type II AEG patients after preoperative radiotherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Tumor Volume Is Better Than Diameter for Predicting the Prognosis of Patients with Early-Stage Non-small Cell Lung Cancer

Background: This study aimed to investigate whether tumor volume (TV) is better than diameter for predicting the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) after complete resection. Methods: This study retrospectively reviewed the clinicopathologic characteristics of 274 patients with early-stage NSCLC who had received pretreatment computed tomography (CT) scans and complete resection. TV was semi-automatically measured from CT scans using an imaging software program. The optimal cutoff of TV was determined by X-tile software. Disease-free survival (DFS) and overall survival (OS) were assessed by the Kaplan–Meier method. The prognostic significance of TV and other variables was assessed by Cox proportional hazards regression analysis. Results: Using 3.046 cm{sup 3} and 8.078 cm{sup 3} as optimal cutoff values of TV, the patients were separated into three groups. A larger TV was significantly associated with poor DFS and OS in the multivariable analysis. Kaplan–Meier curves of DFS and OS showed significant differences on the basis of TV among patients with stage 1a disease, greatest tumor diameter (GTD) of 2 cm or smaller, and GTD of 2–3 cm, respectively. Using two TV cutoff points, three categories of TV were created. In 54 cases (19.7%), patients migrated from the GTD categories of 2 cm or smaller, 2–3 cm, and larger than 3 cm into the TV categories of 3.046 cm{sup 3} or smaller, 3.046–8.078 cm{sup 3}, and larger than 8.078 cm{sup 3}. Conclusion: TV is an independent prognostic factor of DFS and OS for early-stage NSCLC. The findings show that TV is better than GTD for predicting the prognosis of patients with early-stage NSCLC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Predicting the Value of Adjuvant Therapy in Esophageal Squamous Cell Carcinoma by Combining the Total Number of Examined Lymph Nodes with the Positive Lymph Node Ratio

Background: The value of adjuvant therapy for esophageal squamous cell carcinoma (ESCC) has been controversial, at least partially due to the lack of efficient criteria for selecting suitable patients. This study aimed to explore the existence of parameters related to lymph node (LN) status that can predict the value of adjuvant therapy in ESCC. Methods: The study included 298 patients with ESCC who had undergone radical esophagectomy with lymphadenectomy. Adjuvant therapy was defined as reception of adjuvant chemotherapy, radiotherapy, or chemoradiotherapy. For the study, LN ratio (LNR), total number of resected LNs (TLNs), and pN stage were selected for Cox regression analyses, including their correlations and prognostic values for survival. Log-rank tests were used to compare the survival rates of the patients with and without adjuvant therapy stratified by pN stage, TLNs, LNR, or their combinations. Results: The independent prognostic factors for survival were TLNs, LNR, and pN stage. Whereas pN stage was significantly related to TLNs and LNR, TLNs were not correlated with LNR. The survival rates between the patients with and those without adjuvant therapy stratified by pN stage, TLNs, or LNR did not differ significantly. We used the median values of TLNs and LNR to group the patients into four groups. The patients in the group with fewer TLNs and higher LNR who had undergone adjuvant therapy showed a significantly better survival than those without adjuvant therapy (p = 0.030). Conclusions: In contrast to TLNs, LNR, and pN stage as single factors, the combination of TLNs and LNR can predict the value of adjuvant therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Surgery or Radiotherapy of the Primary Tumor in T1–2 Head and Neck Squamous Cell Carcinoma with Resectable N3 Nodes: A Multicenter GETTEC Study

Background: The prognosis of advanced nodal (N3) squamous cell carcinoma of the head and neck (HNSCC) is poor. We investigated whether surgery or radiotherapy of early (T1–2) primary stage HSNCC is preferable to limit the overall morbidity after upfront neck dissection (uND) for N3 disease. Methods: This retrospective multicentric Groupe d’Étude des Tumeurs de la Tête Et du Cou study included patients undergoing uND and surgery or radiotherapy of their primary. Prognostic factors were evaluated using propensity score matching to account for biases in performing surgery depending on primary site and stage. Results: Of 189 T1–2, N3 HNSCC patients, 70 (37.0%) underwent uND: 42 with surgery of their primary and 28 with radiotherapy only. Radiotherapy alone was more frequent in patients with hypopharyngeal primaries. All local (N = 3) and regional (N = 10) relapses (included 2 locoregional relapses) occurred within the first 2 years. There were 16 distant metastatic failures. Five-year locoregional relapse and survival incidences were 15.7% and 66.5% and were similar regardless of the treatment of the primary. The overall morbidity rate was 65.2% and was similar after weighting by the inverse propensity score (p = 0.148). The only prognostic factor for morbidity was the radicality of the uND. Prolonged parenteral feeding was not more frequent in patients only irradiated to their primary (p = 0.118). Prolonged tracheostomy was more frequent after surgery of the primary. Conclusions: In patients with T1–2, N3 HNSCC undergoing uND, radiotherapy and surgery of the primary yield similar oncological outcomes. Morbidity was related to the extent of neck dissection.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

A Novel N Staging System for Predicting Survival in Patients with Medullary Thyroid Cancer

Introduction: Despite the crucially prognostic value of lymph node metastasis (LNM) in patients with medullary thyroid cancer (MTC), only the LNM compartment alone was reflected in the 8th edition of the American Joint Committee on Cancer (AJCC) system. Objective: This study aimed to incorporate the metastatic lymph node number and metastatic lymph node ratio to generate a more accurate and appropriate N staging system for patients with MTC based on recursive partitioning analysis. Design, Setting, and Patients: Two cohorts were included in the analysis, including 1374 MTC patients from the Surveillance, Epidemiology, and End Results database as the derivation cohort, and 164 patients from Fudan University Shanghai Cancer Center as the validation cohort. The predictive performance of the alternative proposed N staging system was compared with that of the 8th AJCC system by using the Harrell concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). Results: In the derivation cohort, the C-index and the AUC at 10 years were 0.778 and 0.789, respectively, for the novel N staging system, and 0.749 and 0.741, respectively, for the 8th AJCC N staging system. Similar trends were also observed in the validation cohort. The proposed N staging system had a better prognostic performance. Conclusion: With some improvements, the novel N staging system for MTC suggested from this research may be assessed for potential adoption in the next edition of the AJCC N staging system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Prostate extracellular vesicles in patient plasma as a liquid biopsy platform for prostate cancer using nanoscale flow cytometry

Background: Extracellular vesicles released by prostate cancer present in seminal fluid, urine, and blood may represent a non-invasive means to identify and prioritize patients with intermediate risk and high risk of prostate cancer. We hypothesize that enumeration of circulating prostate microparticles (PMPs), a type of extracellular vesicle (EV), can identify patients with Gleason Score≥4+4 prostate cancer (PCa) in a manner independent of PSA. Patients and Methods: Plasmas from healthy volunteers, benign prostatic hyperplasia patients, and PCa patients with various Gleason score patterns were analyzed for PMPs. We used nanoscale flow cytometry to enumerate PMPs which were defined as submicron events (100-1000nm) immunoreactive to anti-PSMA mAb when compared to isotype control labeled samples. Levels of PMPs (counts/µL of plasma) were also compared to CellSearch CTC Subclasses in various PCa metastatic disease subtypes (treatment naïve, castration resistant prostate cancer) and in serially collected plasma sets from patients undergoing radical prostatectomy. Results: PMP levels in plasma as enumerated by nanoscale flow cytometry are effective in distinguishing PCa patients with Gleason Score≥8 disease, a highrisk prognostic factor, from patients with Gleason Score≤7 PCa, which carries an intermediate risk of PCa recurrence. PMP levels were independent of PSA and significantly decreased after surgical resection of the prostate, demonstrating its prognostic potential for clinical follow-up. CTC subclasses did not decrease after prostatectomy and were not effective in distinguishing localized PCa patients from metastatic PCa patients. Conclusions: PMP enumeration was able to identify patients with Gleason Score ≥8 PCa but not patients with Gleason Score 4+3 PCa, but offers greater confidence than CTC counts in identifying patients with metastatic prostate cancer. CTC Subclass analysis was also not effective for post-prostatectomy follow up and for distinguishing metastatic PCa and localized PCa patients. Nanoscale flow cytometry of PMPs presents an emerging biomarker platform for various stages of prostate cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Advanced Instrumentation and Control Methods for Small and Medium Reactors with IRIS Demonstration

Development and deployment of small-scale nuclear power reactors and their maintenance, monitoring, and control are part of the mission under the Small Modular Reactor (SMR) program. The objectives of this NERI-consortium research project are to investigate, develop, and validate advanced methods for sensing, controlling, monitoring, diagnosis, and prognosis of these reactors, and to demonstrate the methods with application to one of the proposed integral pressurized water reactors (IPWR). For this project, the IPWR design by Westinghouse, the International Reactor Secure and Innovative (IRIS), has been used to demonstrate the techniques developed under this project. The research focuses on three topical areas with the following objectives. Objective 1 - Develop and apply simulation capabilities and sensitivity/uncertainty analysis methods to address sensor deployment analysis and small grid stability issues. Objective 2 - Develop and test an autonomous and fault-tolerant control architecture and apply to the IRIS system and an experimental flow control loop, with extensions to multiple reactor modules, nuclear desalination, and optimal sensor placement strategy. Objective 3 - Develop and test an integrated monitoring, diagnosis, and prognosis system for SMRs using the IRIS as a test platform, and integrate process and equipment monitoring (PEM) and process and equipment prognostics (PEP) toolboxes. The research tasks are focused on meeting the unique needs of reactors that may be deployed to remote locations or to developing countries with limited support infrastructure. These applications will require smaller, robust reactor designs with advanced technologies for sensors, instrumentation, and control. An excellent overview of SMRs is described in an article by Ingersoll (2009). The article refers to these as deliberately small reactors. Most of these have modular characteristics, with multiple units deployed at the same plant site. Additionally, the topics focus on meeting two of the eight needs outlined in the recently published 'Technology Roadmap on Instrumentation, Control, and Human-Machine Interface (ICHMI) to Support DOE Advanced Nuclear Energy Programs' which was created 'to provide a systematic path forward for the integration of new ICHMI technologies in both near-term and future nuclear power plants and the reinvigoration of the U.S. nuclear ICHMI community and capabilities.' The research consortium is led by The University of Tennessee (UT) and is focused on three interrelated topics: Topic 1 (simulator development and measurement sensitivity analysis) is led by Dr. Mike Doster with Dr. Paul Turinsky of North Carolina State University (NCSU). Topic 2 (multivariate autonomous control of modular reactors) is led by Dr. Belle Upadhyaya of the University of Tennessee (UT) and Dr. Robert Edwards of Penn State University (PSU). Topic 3 (monitoring, diagnostics, and prognostics system development) is led by Dr. Wes Hines of UT. Additionally, South Carolina State University (SCSU, Dr. Ken Lewis) participated in this research through summer interns, visiting faculty, and on-campus research projects identified throughout the grant period. Lastly, Westinghouse Science and Technology Center (Dr. Mario Carelli) was a no-cost collaborator and provided design information related to the IRIS demonstration platform and defining needs that may be common to other SMR designs. The results of this research are reported in a six-volume Final Report (including the Executive Summary, Volume 1). Volumes 2 through 6 of the report describe in detail the research and development under the topical areas. This volume serves to introduce the overall NERI-C project and to summarize the key results. Section 2 provides a summary of the significant contributions of this project. A list of all the publications under this project is also given in Section 2. Section 3 provides a brief summary of each of the five volumes (2-6) of the report. The contributions of SCSU are described in Section 4, including a summary of undergraduate research experience. The project management organizational chart is provided as Figure 1. Appendices A, B, and C contain the reports on the summer research performed at the University of Tennessee by undergraduate students from South Carolina State University.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Data Collection Plan to Populate the Light Water Reactor Sustainability Failure Mode Degradation Library

Interest in implementing advanced Prognostic Health Management (PHM) systems in commercial nuclear power plants (NPPs) has increased rapidly in recent years, with an overarching goal of implementing of improving the safety, reliability, and economics/profitability of the aging nuclear fleet and extending their service life in the most cost-effective manner. The PHM system utilizes prognostic tools to estimate the remaining useful life (RUL) of a component or system of components based on current and predicted operating conditions. An effective implementation of the PHM system will anticipate and identify unique age-dependent degradation modes to provide early warning of emerging problems. Selection of the components and structures to be monitored is a crucial step for successful PHM implementation in NPPs. A selection framework is recommended for risk significant components (both safety-related and non-safety related) based on the Fussell-Vesely (F-V) Importance Measure and the Risk Achievement Worth (RAW) measure. For the selected components, a failure mode degradation library will be developed consisting of data corresponding to different failure/degradation modes. In lieu of constructing an expensive scaled test facility, several data sources are identified for populating the failure mode degradation library, including various national laboratories, universities, agencies, and industries.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Coupling Mechanistically the Convective Motions and Cloud Macrophysics in a Climate Model (Final Technical Report for CM4 Project)

Global climate models fail to adequately represent shallow cumulus conditions. The activities at the University of Texas at Austin focus on deploying advanced statistical approaches to identify linkages between numerical models and observations using the E3SM-Single Column Model (SCM) and different ARM campaigns focused on shallow cumulus conditions at SGP. The current version of the SCM uses CLUBB to represent shallow cumulus. The parameterization represents a significant evolution from older generation parameterizations insofar as it contains a number of prognostic equations that keep track of the turbulent nature of the environment that are carried forward in time. The parameters associated with this scheme address the rate at which different prognostic quantities that determine vertical turbulent fluxes of heat and moisture decay with time. The large number of parameters gives the CLUBB scheme a lot of flexibility. The expectation of its developers is that it should represent any shallowly convective environment. The concern among developers and other theorists was not whether it could represent any one environment, but whether a single judicious choice of parameters would allow it to predict the turbulent characteristics of a range of environments. Through several exhaustive studies that made use of Bayesian statistical calibration, tens of thousands of SCM experiments, and ensembles of observations of shallow cumulus at SGP that were also modeled using Large Eddy Simulations (LES), we have only achieved partial success in using CLUBB within SCM to capture important aspects of what has been observed and modeled with LES at SGP. Even as we worked to improve observational tests and make a more scientifically focused attempt at modifying subsets of CLUBB parameters, model performance continues to be lacking in some important respects. In one calibration, we were able to achieve a near perfect match to observed properties of liquid water path, cloud radiative effects, and cloud fraction but with clouds that were trapped within a shallow layer and without the highly turbulent fluxes and vertical extent observed with ARM data and modeled with LES (Figure 1). The conclusion we are reaching is that shallow cumulus conditions are much more challenging to represent than was previously thought. This result is also prompting us to consider some aspects of the experiment design that may be limiting the performance of SCM in this case. The work has garnered the attention of Vincent Larson (CLUBB developer at Univ. Wisconsin – Milwaukee) and model developers Steven Klein and Yunyan Zhang (LLNL) with whom this work and will continue.

58 GEOSCIENCES↗