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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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To determine the prognostic significance of c-MET expression and develop a predictor of distant failure in patients with resectable pancreatic cancer treated with chemoradiation.
To identify prognostic factors, define evolving patterns of care, and the effect of targeted therapies in a larger contemporary cohort of renal cell carcinoma (RCC) patients with new brain metastases (BM).
To study the prognostic value of gross tumor volume (GTV) shrinkage and its dosimetric implication in a large cohort of patients with cervical cancer receiving definitive chemoradiotherapy plus image guided adaptive brachytherapy.
We aimed to develop an accurate prognostic model to identify suitable candidates for definitive radiation therapy (DRT) in addition to palliative chemotherapy (PCT) among patients with de novo metastatic nasopharyngeal carcinoma (mNPC).
Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. Current parameterizations are designed by humans based on physical understanding, observations, and process modeling. As a result, they are numerically efficient and interpretable, but potentially oversimplified. However, the advent of global high-resolution simulations and observations enables a more robust approach based on machine learning. In this letter, a neural network-based parameterization is trained using a near-global aqua-planet simulation with a 4-km resolution (NG-Aqua). The neural network predicts the apparent sources of heat and moisture averaged onto (160 km)2 grid boxes. A numerically stable scheme is obtained by minimizing the prediction error over multiple time steps rather than single one. In prognostic single-column model tests, this scheme matches both the fluctuations and equilibrium of NG-Aqua simulation better than the Community Atmosphere Model does.
Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.
Grasslands are one of the most widely distributed and abundant vegetation types globally, and land surface models struggle to accurately simulate grassland carbon dioxide, energy, and water fluxes. Here we hypothesize that this is due to land surface models having difficulties in reproducing grassland phenology, in particular in response to the seasonal and interannual variability of precipitation. Using leaf area index (LAI), net primary productivity, and flux data at 55 sites spanning climate zones, the aim of this study is to evaluate a novel prognostic phenology model (Simple Biosphere Model, SiB4) while simultaneously illustrating grassland relationships across precipitation gradients. Evaluating from 2000 to 2014, SiB4 predicts daily LAI, carbon, and energy fluxes with root-mean-square errors < 15% and individual biases <10%; however, not including management likely reduces its performance. Grassland mean annual LAI increases linearly with mean annual precipitation, with both SiB4 and the Moderate Resolution Imaging Spectroradiometer (MODIS) showing a 0.13 increase in LAI per 100-mm increase in precipitation. Both gross primary production and ecosystem respiration increase with growing season length by ~8.5 g C m -2 per day, with SiB4 and Fluxnet estimates within 18%. Despite differences in mean annual precipitation and growing season length, all grassland sites shift to seasonal carbon sinks one month prior to peak uptake. During a U.S. drought, MODIS and SiB4 had nearly identical LAI responses, and the LAI change due to drought was less than the LAI change across the precipitation gradient, indicating that grassland drought response is not as strong as the overlying climate response.
Advanced small modular reactors (aSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. aSMRs are conceived for applications in remote locations and for diverse missions that include providing process or district heating, water desalination, and hydrogen production. Several challenges exist with respect to cost-effective operations and maintenance (O&M) of aSMRs, including the impacts of aggressive operating environments and modularity, and limiting these costs and staffing needs will be essential to ensuring the economic feasibility of aSMR deployment. In this regard, prognostic health management (PHM) systems have the potential to play a vital role in supporting the deployment of aSMR systems. This paper identifies requirements and technical gaps associated with implementation of PHM systems for passive aSMR components.
Purpose: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. Methods: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer. Data from 70 patients were used for training/discovery purposes, while those of remaining 40 patients were used for independent validation. We extracted 191 radiomic features, including statistical, histogram, morphological, and texture features. Cox proportional hazard regression model, coupled with the least absolute shrinkage and selection operator (LASSO), was used to predict overall survival based on the radiomic features. Results: The optimal prognostic model included three image features from the Law’s feature and wavelet texture. In the discovery cohort, this model achieved a concordance index or CI=0.67, and it separated the low-risk from high-risk groups in predicting overall survival (hazard ratio=2.72, log-rank p=0.007). In the independent validation cohort, this radiomic signature achieved a CI=0.62, and significantly stratified the low-risk and high-risk groups in terms of overall survival (hazard ratio=2.20, log-rank p=0.042). Conclusion: We identified CT imaging characteristics associated with overall survival in early stage lung cancer. If prospectively validated, this could potentially help identify high-risk patients who might benefit from adjuvant systemic therapy.
Background: Neoadjuvant therapy (NAT) before radical excision has become the preferred initial option for locally advanced digestive cancers such as esophageal cancer (EC), esophagogastric junction adenocarcinoma (EGJAC), gastric adenocarcinoma (GAC), rectal cancer (RC), and pancreatic cancer (PC). Although some patients reportedly achieve a pathologic complete response (pCR) after neoadjuvant therapy, the published data are inconsistent regarding whether pCR yields a survival benefit. The current meta-analysis was performed to assess the potential prognostic value of pCR after preoperative therapy for patients with digestive cancers. Methods: An extensive electronic search in PubMed, Web of Science, and the Cochrane Library was performed for relevant articles, from which data relative to independent correlations of pCR with overall survival (OS) and disease-free survival (DFS) were extracted for analysis. A random-effects model was used to calculate the pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs). Results: The study identified 6780 patients who met the inclusion and exclusion criteria. The results showed that pCR was significantly correlated with better OS (HR, 0.50; 95% CI, 0.43–0.58; P < 0.001) and DFS (HR, 0.49; 95% CI, 0.40–0.60; P < 0.001) for the digestive cancer patients who achieved pCR than for those who did not achieve pCR. Subgroup analysis showed that the correlation of pCR with OS was significant in EC (HR, 0.57; 95% CI, 0.47–0.69; P < 0.001), EGJAC/GAC (HR, 0.38; 95% CI, 0.17–0.86; P = 0.02), RC (HR, 0.48; 95% CI, 0.28–0.81; P = 0.006), and PC (HR, 0.41; 95% CI, 0.17–0.97; P = 0.04). In addition, the survival benefit for pCR patients was of similar magnitude, irrespective of the type of study, type of NAT, or ethnicity. Conclusions: A pCR is correlated with favorable survival outcomes compared with a non-pCR for digestive cancer patients after NAT.
The objective of this work is to develop methodologies that will accurately estimate state-of-health (SOH) and remaining useful life (RUL) of electrochemical energy storage devices using both offline and online (i.e., in-situ) techniques through: • A statistically robust offline battery calendar life estimator tool based on both testing and simulation, and • Novel onboard sensor technology for improved online battery diagnostics and prognostics.
Operations and maintenance costs for offshore wind plants are expected to be significantly higher than the current costs for onshore plants. One way in which these costs may be able to be reduced is through the use of a structural health and prognostic management system as part of a condition based maintenance paradigm with smart load management. To facilitate the creation of such a system a multiscale modeling approach has been developed to identify how the underlying physics of the system are affected by the presence of damage and how these changes manifest themselves in the operational response of a full turbine. The developed methodology was used to investigate the effects of a candidate blade damage feature, a trailing edge disbond, on a 5-MW offshore wind turbine and the measurements that demonstrated the highest sensitivity to the damage were the local pitching moments around the disbond. The multiscale method demonstrated that these changes were caused by a local decrease in the blade's torsional stiffness due to the disbond, which also resulted in changes in the blade's local strain field. Full turbine simulations were also used to demonstrate that derating the turbine power by as little as 5% could extend the fatigue life of a blade by as much as a factor of 3. The integration of the health monitoring information, conceptual repair cost versus damage size information, and this load management methodology provides an initial roadmap for reducing operations and maintenance costs for offshore wind farms while increasing turbine availability and overall profit.
Through the New Mexico Small Business Assistance Program, Sandia National Laboratories worked with Sentient Business Systems, Inc. to develop and test a novel photovoltaic (PV) arc-fault detection system. The system operates by pairing translucent polymeric fiber optic sensors with electrical circuitry so that any external abrasion to the system or internal heating causes the fiber optic connection to fail or detectably degrade. A periodic pulse of light is sent through the optical path using a transmitter-receiver pair. If the receiver does not detect the pulse, an alarm is sounded and the PV system can be de-energized. This technology has the unique ability to prognostically determine impending failures to the electrical system in two ways: (a) the optical connection is severed prior to physical abrasion or cutting of PV DC electrical conductors, and (b) the polymeric fiber optic cable melts via Joule heating before an arc-fault is established through corrosion. Three arc-faults were created in different configurations found in PV systems with the integrated fiber optic system to determine the feasibility of the technology. In each case, the fiber optic cable was broken and the system annunciated the fault.
Operations and maintenance costs for offshore wind plants are significantly higher than the current costs for land-based (onshore) wind plants. One way to reduce these costs would be to implement a structural health and prognostic management (SHPM) system as part of a condition based maintenance paradigm with smart load management and utilize a state-based cost model to assess the economics associated with use of the SHPM system. To facilitate the development of such a system a multi-scale modeling approach developed in prior work is used to identify how the underlying physics of the system are affected by the presence of damage and faults, and how these changes manifest themselves in the operational response of a full turbine. This methodology was used to investigate two case studies: (1) the effects of rotor imbalance due to pitch error (aerodynamic imbalance) and mass imbalance and (2) disbond of the shear web; both on a 5-MW offshore wind turbine in the present report. Based on simulations of damage in the turbine model, the operational measurements that demonstrated the highest sensitivity to the damage/faults were the blade tip accelerations and local pitching moments for both imbalance and shear web disbond. The initial cost model provided a great deal of insight into the estimated savings in operations and maintenance costs due to the implementation of an effective SHPM system. The integration of the health monitoring information and O&M cost versus damage/fault severity information provides the initial steps to identify processes to reduce operations and maintenance costs for an offshore wind farm while increasing turbine availability, revenue, and overall profit.
This final report is a compilation of research efforts – funded by the US Department of Energy Wind and Water Power Technologies Office over a four-year period from FY11 through FY14. The goals of this research program were to develop and evaluate technical innovations with promise for maximizing revenues and reducing levelized cost of energy (LCOE) for offshore wind plants – more specifically the goals of the Structural Health and Prognostics Management (SHPM) program were to reduce O&M costs and increase energy capture through use of SHPM-based technologies. A technology roadmap was developed at the start of the project to guide the research efforts. This roadmap identified and outlined six major research thrust areas each having five stages of maturity. Research was conducted in each of these thrust areas, as documented throughout this report, although a major focus was on development of damage detection strategies for the most frequent blade damage conditions and damage mitigation and life-extension strategies via changes in turbine operations (smart loads management). The work summarized in this compilation report is the product of the work of many researchers. A summary of the major findings, status of the SHPM Technology Roadmap and recommendations for future work are also provided.
Operations and maintenance costs for offshore wind plants are significantly higher than the current costs for land-based (onshore) wind plants. One way to reduce these costs would be to implement a structural health and prognostic management (SHPM) system as part of a condition based maintenance paradigm with smart load management and utilize a state-based cost model to assess the economics associated with use of the SHPM system. To facilitate the development of such a system a multi-scale modeling and simulation approach developed in prior work is used to identify how the underlying physics of the system are affected by the presence of damage and faults, and how these changes manifest themselves in the operational response of a full turbine. This methodology was used to investigate two case studies: (1) the effects of rotor imbalance due to pitch error (aerodynamic imbalance) and mass imbalance and (2) disbond of the shear web; both on a 5-MW offshore wind turbine in the present report. Sensitivity analyses were carried out for the detection strategies of rotor imbalance and shear web disbond developed in prior work by evaluating the robustness of key measurement parameters in the presence of varying wind speeds, horizontal shear, and turbulence. Detection strategies were refined for these fault mechanisms and probabilities of detection were calculated. For all three fault mechanisms, the probability of detection was 96% or higher for the optimized wind speed ranges of the laminar, 30% horizontal shear, and 60% horizontal shear wind profiles. The revised cost model provided insight into the estimated savings in operations and maintenance costs as they relate to the characteristics of the SHPM system. The integration of the health monitoring information and O&M cost versus damage/fault severity information provides the initial steps to identify processes to reduce operations and maintenance costs for an offshore wind farm while increasing turbine availability, revenue, and overall profit.
This report describes research results to date in support of the integration and demonstration of diagnostics technologies for prototypical advanced reactor passive components (to establish condition indices for monitoring) with model-based prognostics methods. Achieving this objective will necessitate addressing several of the research gaps and technical needs described in previous technical reports in this series.
Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.