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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗
DOE OSTI2024

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) [and subsequently titled] Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022) (Final Report)

This is the Final Technical Report for the DOE Measurement Innovation grant DE-SC0017998 titled Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) and subsequently titled Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022). The grant was funded by the Office of Fusion Energy Sciences (FES) for the period 1 September 2017 – 31 May 2022. Work performed through the grant has resulted in diagnostic innovations and new measurement capabilities that could improve understanding of magnetic equilibrium, edge localized modes in tokamaks, and bootstrap and Pfirsch-Schlüter currents in stellarators. We have performed simulations of a novel non-hydrogen beam-based diagnostic that would determine a local value (in the plasma) of the poloidal flux function by measuring the toroidal angle of the beam particles' velocity at a detector external to the plasma. The approach is similar to that of a Heavy Ion Beam Probe (HIBP), but would be able to operate with a smaller, more economical detection systems and, in doing so, may enable substantial extension of scenarios in which beam-based diagnostics are deployed. Key results include: Identification of the neutral and singly charged atoms that are most appropriate for use in the beam based diagnostic and the ionization cross-sections that are important for studying the plasma edge of a large device; Development of a promising geometry for operation in which the beam is injected horizontally near the torus midplane; Implementation of simulations using banana orbit trajectories of secondary beam ions to access plasma regions that are commonly in-accessible to an HIBP diagnostic, including sample volumes near the X-point of a diverted tokamak; Assessment of the ability of the diagnostic concept (originally developed under the assumption of, and reliant on, symmetry) to work in stellarators by performing simulations with both quasi-symmetric fields and non-quasi-symmetric fields; Measurement of noise levels with a prototype detector on a stellarator and exploration of techniques to minimize effects of noise on a diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

14 SOLAR ENERGY↗
DOE OSTI2018

Serum biomarkers for diagnosis and prediction of type 1 diabetes

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic ß-cells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including glucose, glycated molecules, c-peptide, and autoantibodies have been well established to diagnose T1D from other subtypes of diabetes. However, these molecules often mark a late stage of the disease when ~90% of the pancreatic insulin-producing ß-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive ‘-omics’ technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D and mainly focus on the novel biomarkers that are identified as markers of ß-cell destruction and screened with the use of state-of-the-art ‘-omics’ technologies.

Yi, Lian↗
DOE OSTI2024

Mitigation and Diagnosis of Pin-Hole Formation in Polymer Electrolyte Membrane Fuel Cells

In this work, we implement a calendering technique to flatten stray fibers within the gas diffusion media, thereby mitigating pin-hole formation in the hot-pressed MEAs. We have investigated the influence of calendering on the long-term durability for several types of gas diffusion electrodes (GDEs) using a combined chemical and mechanical accelerated stress test (AST). The calendered MEAs demonstrate an average AST lifetime improvement of 77% relative to the as-fabricated MEAs.

AMR↗
DOE OSTI2019

Diagnosis of Factors Impacting Yield in Multilayer Devices for Superconducting Electronics

The ability to localize defects in order to understand failure mechanisms in complex superconducting electronics circuits, while operating at low temperature, does not yet exist. Here, this work applies thermally-induced voltage alteration (TIVA), to a biased superconducting electronics (SCE) circuit at ambient temperature. TIVA is a commonly used, laser-based failure analysis technique developed for silicon-based microelectronics. The non-operational circuit consisted of an arithmetic logic unit (ALU) in a high-frequency test bed designed at HYPRES and fabricated by MIT Lincoln Laboratory using their SFQ5ee process. Localized TIVA signals were correlated with reflected light images at the surface, and these sites were further investigated by scanning electron microscopy imaging of focused ion-beam cross-sections. The areas investigated, where prominent TIVA signals were observed, showed seams in the Nb wiring layers at contacts to Josephson junctions or inductors and/or disrupted junction morphologies. Finally, these results suggest that the TIVA technique can be used at ambient temperature to diagnose fabrication defects that may cause low temperature circuit failure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Acute non-traumatic abdominal pain by quadrant: relative yield of CT and clinical evaluation for diagnosis in 1000 patients

Objective: To determine the relative diagnostic yield of contrast-enhanced CT in adults presenting with symptoms referable to a specific abdominal quadrant. Methods: Electronic health records review systematically identified patients meeting the following inclusion criteria: adults (≥ 18 years) undergoing IV contrast-enhanced abdominopelvic CT for acute non-traumatic symptoms referable to a specific abdominal quadrant (RLQ/LLQ/LUQ/RUQ). The CT-based diagnosis and any clinical diagnosis in the absence of CT diagnosis were recorded. The final cohort of 1000 subjects (mean age, 48.1 years; 647F/353M) consisted of consecutive sub-cohorts of 250 patients for each abdominal quadrant. Positive oral contrast was utilized in 91.6% (916/1000) of cases. Results: A positive CT diagnosis was provided in 47.3% (473/1000) of all patients, and was highest for LLQ (58.8%) and RLQ (58.0%) symptoms, including diverticulitis and appendicitis in 23.6% and 24.8% cases, respectively. CT positivity was lower for the LUQ (34.4%) and RUQ (38.0%) (p < 0.0001), with no single diagnosis representing > 5% of cases. However, all quadrants provided valuable triage of 218 hospital admissions (21.8%), 83.0% were CT positive, whereas 62.7% of 782 discharged patients were CT negative. Only 7.0% of CT-negative patients were admitted. A clinical-only diagnosis was provided in 9.3% of the total cohort (93/1000), representing 17.6% of the CT-negative cohort (93/527). Conclusion: The rate of positive CT diagnosis is considerably higher for the lower abdominal quadrants, predominately due to appendicitis and diverticulitis. However, CT results (positive vs. negative) for all four quadrants strongly correlated with hospital admission versus discharge. Clinical-only diagnosis represented < 10% of all cases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Effect of radiation exposure on survival after first solid cancer diagnosis in A-bomb survivors

Comparison of the estimated effect of atomic bomb radiation exposure on solid cancer incidence and solid cancer mortality in the RERF Life Span Study (LSS) reveals a difference in the magnitude and shape of the excess relative risk dose response. A possible contributing factor to this difference is pre-diagnosis radiation effect on post-diagnosis survival. Pre-diagnosis radiation exposure theoretically could influence post-diagnosis survival by affecting the genetic makeup and possibly aggressiveness of cancer, or by compromising tolerance for aggressive treatment for cancer. We analyze the radiation effect on post-diagnosis survival in 20,463 LSS subjects diagnosed with first-primary solid cancer between 1958 and 2009 with particular attention to whether death was caused by the first-primary cancer, other cancer, or non-cancer diseases. From multivariable Cox regression analysis of cause-specific survival, the excess hazard at 1 Gy (EH 1Gy ) for death from the first primary cancer was not significantly different from zero – p = 0.23, EH 1Gy = 0.038 (95 % CI: –0.023, 0.104). Death from other cancer and death from non-cancer diseases both were significantly associated with radiation dose: other cancer EH 1Gy = 0.38 (95 % CI: 0.24, 0.53); non-cancer EH 1Gy = 0.24 (95 % CI: 0.13, 0.36), both p < 0.001. There is no detectable large effect of pre-diagnosis radiation exposure on post-diagnosis death from the first primary cancer in A-bomb survivors. A direct effect of pre-diagnosis radiation exposure on cancer prognosis is ruled out as an explanation for the difference in incidence and mortality dose response in A-bomb survivors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Fault diagnosis and severity estimation in nuclear power plants - 196

Signed Directed Graph (SDG) is a type of fault diagnosis method with a capability to display the complex relationship between plant parameters and reveal the potential danger and the propagation path when a fault occurs in a nuclear plant. However, the fault diagnosis method based on qualitative SDG model inevitably has a number of disadvantages, and some of these disadvantages include its inability to identify reoccurring faults; ambiguous reasoning that can result in low resolution; performance that relies on the accuracy of the model; slow diagnosis rate for large systems and poor real-time diagnosis, among others. In this paper, we propose an SDG fault diagnosis methodology based on Granular Computing Theory, referred to as Granular Computing Signed Directed Graph (GCSDG). This paper shows how knowledge reduction nature of granular computing based fault diagnosis can simplify the decision table, reduce the allocation of resources, scale-down the complexity of the problem and improve the efficiency of data processing. Then, we estimate the severity of the fault using Back Propagation (BP) neural network approach. The proposed methodology was verified by considering certain faults in the primary loop of a PWR, using PCTRAN. The result shows a considerable improvement in fault diagnosis and the severity estimated serves as a useful information for the operator, providing a firm foundation for further intervention. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Pitfalls in the Diagnosis of Acute Pulmonary Embolism on Computed Tomography: Common Pathologic and Imaging Mimics

Purpose of Review: Acute pulmonary embolism (PE) is the third most-common acute cardiovascular disease in the United States. Nearly 900,000 new diagnoses are made each year, with 250,000 associated hospitalizations and up to 100,000 deaths (Palacio et al. in Semin Roentgenol 50(3):217–225, 2015; Metter et al. in Am J Roentgenol 208(3):489–494, 2017; Jaff et al. in Circulation 123(16):1788–830, 2011). Timely and accurate diagnosis of acute PE is crucial for the prevention of most deaths associated with PE, even more so than optimal medical therapy (Palacio et al. in Semin Roentgenol 50(3):217–225, 2015; Jaff et al. in Circulation 123(16):1788–830, 2011; Raja et al. in Ann Intern Med 163(9):701, 2015). Recent Findings: Computed tomography (CT) has emerged as the primary imaging modality used in the diagnosis of acute PE. Thus, radiologists play a key role in the correct diagnosis of acute PE, and must differentiate this diagnosis from other conditions that mimic the imaging findings of acute PE, as well as from artifacts associated with imaging techniques (Palacio et al. in Semin Roentgenol 50(3):217–225, 2015; Jaff et al. in Circulation 123(16):1788–830, 2011; Raja et al. in Ann Intern Med 163(9):701, 2015). Understanding how these diagnoses present on CT is necessary for improved patient care. Summary: This article will discuss the diagnosis of acute PE with CT, the diagnosis of diseases that may be mistaken for acute PE, and how to identify imaging artifacts that may be mistaken for an acute PE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

CT detection of primary and metastatic ileal carcinoid tumor: rates of missed findings and associated delay in clinical diagnosis

Purpose: To determine the rate of missed CT findings of ileal carcinoid tumor prior to pathologic diagnosis and the resultant diagnostic delay. Methods: Initially, 74 patients with abdominal and pelvic CT prior to pathologically-proven diagnosis of ileal carcinoid were identified. Patients were excluded when the original CT study (n = 6) or report (n = 4) was not available, resulting in a final cohort of 64 patients (mean age, 58.3 years; 29 M/35F); 27 (42%) patients had more than one abdominal CT prior to diagnosis. All available CT studies prior to diagnosis were retrospectively reviewed for the presence of the primary ileal tumor and metastatic disease (mesenteric and hepatic). Results: Primary ileal tumors were prospectively missed on at least one CT scan in 64% (32/50) of patients with retrospectively identifiable disease. CT findings of mesenteric spread were missed at least once in 46% (25/54) of cases where present in retrospect. By the final pre-operative CT, hepatic metastases and bowel wall thickening were present in 55% (35/64) and 52% (33/64) of cases, respectively. In patients with missed ileal and/or mesenteric findings resulting in diagnostic delay, mean delay was 40 months (range 4–98 months). Conclusion: Initial presentation of ileal carcinoid tumor, even with mesenteric involvement, is often missed prospectively at abdominal CT, leading to delay in diagnosis until bowel or mesenteric findings become more obvious, or hepatic metastatic disease manifests. Radiologists should make a concerted effort to evaluate the bowel and mesentery in patients with long-standing vague abdominal symptoms.

62 RADIOLOGY AND NUCLEAR MEDICINE↗