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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Automated System of Diagnostic Monitoring at Bureya HPP Hydraulic Engineering Installations: a New Level of Safety

This article presents the design, hardware, and software solutions developed and placed in service for the automated system of diagnostic monitoring (ASDM) for hydraulic engineering installations at the Bureya HPP, and assuring a reliable process for monitoring hydraulic engineering installations. Project implementation represents a timely solution of problems addressed by the hydraulic engineering installation diagnostics section.

42 ENGINEERING↗
DOE OSTI2019

Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. This study was designed to investigate the potential of Ribosomal Protein L22-like1 (RPL22L1) and Ribosomal Protein S21 (RPS21) as diagnostic and prognostic biomarkers for PCa. First, RPL22L1 and RPS21 were screened as the key molecular of PCa by bioinformatics analysis. Subsequently, the prostate tissue samples were stained for antibodies against RPL22L1 and RPS21. The unbiased signal quantification was performed by ImageJ software, and the results showed that the expression of RPL22L1 and RPS21 exhibited significant differences between the PCa tissues and the normal prostate tissues. Receiver-operating characteristics (ROC) curves were prepared, and then the area under the curve (AUC) values of RPL22L1 and RPS21 were calculated as 0.798 and 0.768, and the likelihood ratio (LR) values of RPL22L1 and RPS21 were calculated as 2.86 and 2.53. These data implied that the over-expression of RPL22L1 and RPS21 is associated with the presence of PCa. The further analysis suggested that the expression of RPL22L1 and RPS21 were significantly higher in high Gleason grade than they were in low Gleason grade. In addition, in vitro studies were undertaken to evaluate the roles of RPL22L1 and RPS21 in PCa. The results revealed that these genes promote PCa cell proliferation, migration and invasion, and inhibit PCa cell apoptosis. Taken together, these data showed that RPL22L1 and RPS21 exhibited higher expression in human prostate cancer tissue, and involved in PCa cell proliferation and invasion. This research provided a novel insight into diagnostic and prognostic biomarkers for PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Diagnostic value of stress thallium-201/rest technetium-99m-sestamibi sequential dual isotope high-speed myocardial perfusion imaging for the detection of haemodynamically relevant coronary artery stenosis

Background: The aim of this study was to determine the diagnostic accuracy of stress thallium-201/rest technetium-99m-sestamibi sequential dual-isotope high-speed myocardial perfusion imaging (DI-HS-MPI) against invasively determined fractional flow reserve (FFR). Methods: Fifty-four consecutive patients prospectively underwent DI-HS-MPI before invasive coronary angiography. Perfusion was scored visually by summed stress score on a patient and coronary territory basis. Significant coronary artery disease (CAD) was defined by the presence of ≥ 90% stenosis/occlusion or fractional flow reserve ≤ 0.80 for coronary stenosis ≥ 50%. Results: FFR was measured in 69 of 162 coronary vessels, with 1.28 ± 0.56 vessels assessed/patient. Sensitivity, specificity, and diagnostic accuracy of MPI for the detection of significant CAD were 92.8%, 69.2%, and 81.4%, on a patient basis, and 83.7%, 90.4%, and 88.8% by coronary territory. Conclusions: DI-HS-MPI accurately detects functionally significant CAD as defined by using FFR.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Multiple simultaneous faults’ impacts on air-conditioner behavior and performance of a charge diagnostic method

Although there have been several studies that have focused on the effects of faults on the performance and on the characteristic features that could be used to detect faults, almost none of the research has studied the effects of multiple simultaneous faults. Existing fault detection and diagnosis approaches that have been developed based on single faults may struggle with simultaneous faults. Here, this paper is part of a series that present a methodology and comprehensive measurement data from a battery of laboratory tests on an air-conditioner with combinations of common faults (refrigerant charge, evaporator airflow, non-condensable gas, and liquid line restrictions) imposed, to show how the indicator variables and overall performance are impacted. The system has a microtube condenser and a fixed-orifice (FXO) expansion device, which are found to affect the features’ fault sensitivity. This experiment is the first to test these combined faults in an FXO-equipped system, so comparisons are made to a previously studied thermostatic expansion valve-equipped system. Finally, a promising charge diagnostic technology is assessed, to see how it is impacted by other faults. The faults reduced capacity by up to 42%, and efficiency by up to 39%. The charge diagnostic performed well, even with multiple faults present.

42 ENGINEERING↗
DOE OSTI2019

Thermal Loads and Cooling Design for ITER in-Port Low Field Side Reflectometer Diagnostic System

The current design for the ITER Low Field Side Reflectometer (LFSR) diagnostic system contains six circular waveguides that function as both launch and receive antennas. The front end of LFSR system, interfacing with the plasma facing DFW and its supporting diagnostic shielding Modules (DSM), is integrated into Equatorial Port Plug 11(EPP#11). In-vessel LFSR configuration is mainly divided into two sections, i.e. Front Antenna Unit (FAU) and in-vessel Transmission Line (TL) components. During plasma normal operation, the 14 MeV neutrons from D-T fusion reactions penetrate into the port plug and LFSR components. Neutronics code ATTILA is used for assessing the volumetric heating of in-vessel components. The maximum normal heat load due to radiation and the charge exchange from plasma is 0.35 MW/m 2 , which has to be applied on those surfaces affected by a viewing factor consistent with the aperture surface position and orientation. The front antenna unit (FAU) needs the active cooling to handle the radiant and nuclear heating from the plasma. Due to the specific viewing aperture layout at the front-end of in-vessel LFSR, the cooling channel design is challenging. The thermal-hydraulic iterations are performed to achieve the desirable temperature distribution of the water cooled front antenna units. The progressive ratcheting effects of the FAU subjected to cyclic thermal loading are examined by the preliminary non-linear elastic-plastic analysis.

42 ENGINEERING↗
DOE OSTI2019

The JET upgraded toroidal Alfvén Eigenmode Diagnostic System

The Alfvén Eigenmode Active Diagnostic system (AEAD) has undergone a major upgrade and redesign to provide a state-of-the-art excitation and real-time detection system for JET. The new system consists of individual 4 kW amplifiers allowing for increased current, separate excitation and real time control of relative phasing between antenna currents. The amplifiers have a frequency range of 10–1000 kHz, divided into various frequency bands by external matching filters. Due to the varying transmission line impedance throughout the frequency range, the amplifiers were designed with a very high resilience to reflected power. The existing amplifier control electronics have been replaced with a digital control system incorporating a National Instruments platform and Field Programmable Gate Array (FPGA) modules for frequency, gain and phase control with a frequency and phase resolution of less than 1 kHz and 1 degree respectively. Complementing the digital control system is the Protection and Control System, which utilizes Field Programmable Analog Arrays (FPAAs) and an array of electronic devices to monitor and control the AEAD. New capabilities such as independent antenna current/phase control, allow for improved excitation control, better definition of antenna spectrum combined with enhanced system reliability. This paper will review the new AEAD system, its unique capabilities and improvements over the previous diagnostic system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optical design and synthetic analysis of the electron cyclotron emission imaging diagnostic of HL-2M tokamak

The electron cyclotron emission imaging (ECEI) diagnostic is a powerful tool to study the MHD and turbulent transport in magnetically confined fusion plasmas. In this work, the optical system including the local oscillator (LO) coupling and radio frequency (RF) receiving optics, has been designed and analyzed for the HL-2M ECEI diagnostic. The LO optics can illuminate the antenna array and drive the mixer diode to work efficiently, with more than 36% of the beam intensity for the channels which probe the plasma edge relative to those of channels which probe the plasma core. The RF optics aims at guiding the plasma emission signal to the antenna array. To meet different physical requirements, three types of field of view have been achieved for the plasma imaging, with zoom factors of around 1, 1.5 and 2, respectively. The focal surfaces are almost flat, with a maximum off-mapping (defined as the radial distance between the beam waists of the lower-/uppermost antennas and electron cyclotron emission layer) less than 2.5 cm, which can match the electron cyclotron emission layer quite well and suppress the image distortion in the plasma edge region. Furthermore, the impact of possible vibrations and installation error on the focal plane has been evaluated. In addition, the predefined MHD and turbulence perturbations are well reproduced by combining the optical simulation results and synthetic ECEI modeling, which further verifies the good performance of the RF optics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A temperature profile diagnostic for radiation waves on OMEGA-60

Predicting and matching radiation wave propagation with computational models has proven difficult. Information provided by experiments studying radiation flow has been limited when only radiation breakout is measured. We have developed the COAX (co-axial) diagnostic platform to provide spatial temperature profiles of a radiation wave through low density foams as a more detailed constraint for simulations. COAX uses a standard, laser-driven OMEGA-60 halfraum to drive radiation down a titanium-laden silicon oxide foam. Point-projection X-ray absorption spectroscopy perpendicular to the radiation flow measures the spatial profile of titanium ionization. The spectroscopic measurement utilizes a broadband capsule backlighter. Imaging and streak spectroscopy are used to characterize the size and spectrum of this source. Radiography provides an additional constraint by capturing the developing shock as the radiation flow becomes subsonic. The DANTE diagnostic is used to measure the halfraum temperature. Here, we provide a spectroscopic analysis of COAX data to determine temperature, and we describe experimental sources of uncertainty. The temperature is obtained by comparison to multi-temperature synthetic spectra post-processed from radiation-hydrodynamics simulations. Quantitative comparison between data and synthetic spectra generated from temperature profiles at relevant simulation times enable determination of a peak temperature of 114 ± 8 eV at 265 ± 22.4 μm from the halfraum. This represents an improvement over the temperature uncertainties of previous radiation flow experiments. Further refinements to the spectroscopic analysis could achieve ±4 eV. The combination between space-resolved spectroscopy and radiography enables us to determine the distance from the halfraum of both the radiation front and the shock front at the time of measurement. For the example shown in this paper the radiation front position is 600–630 μm at 3.43 ± 0.16 ns and the shock front position is 633 μm at 3.3 ± 0.24 ns.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Benchmarking optimization methods for materials research: Gradient descent and Bayesian optimization for lithium-ion battery aging diagnostics

Accurate and efficient parameter estimation is essential for battery diagnostics and aging analysis. Here, in this study, we compare two optimization-based approaches—gradient descent and Bayesian optimization—for extracting parameters from differential voltage analysis in lithium-ion batteries. While these techniques are widely used, their relative strengths and limitations for this application are not well understood. The study evaluates the trade-offs between these methods in terms of result quality, computational cost, and reliability within this specific application. The diagnostic results from our battery data suggest adopting gradient descent as an initial method for rapid and efficient analysis, while employing more stable optimization techniques, such as Bayesian optimization, as a verification step to mitigate potential instability. Comparing the two methods provides information on algorithmic choice, while inspiring further discussions on selecting appropriate techniques for specific research tasks.

Zhao, Ziqing [Boston Univ., MA (United States)] (O↗
DOE OSTI2018

Diagnostic and Prognostic Biomarkers in ovarian cancer and the potential roles of cancer stem cells – An updated review

Highlights: • Current updated potential prognostic and diagnostic biomarkers in ovarian cancer. • Potential importance of cancer stem cells in tumorigenesis and metastasis. • Signature biomarkers improve the prediction of survival in cancer patients. Ovarian carcinomas relate to highest death rate in gynecologic malignancies as absence of symptoms shield the disease in the early stage. Current evidences have been devoted to discovering early effective screening mechanism prior to the onset of clinical symptoms. Therefore, biomarkers are the crucial tools that are capable of predicting progression, risk stratification and overall therapeutic benefit to fight against this deadly disease. Although recent studies have revealed serum protein markers, CA-125, HE4, mesothelin etc. have higher sensitivity and specificity at the early stages of the cancer; the critical questions arise regarding the applicability and reproducibility of genomic profiling across different patient groups. Hence, our hypothesis is that the panels of signature biomarkers will be much more effective to improve the diagnosis and prediction of patient survival outcome with high sensitivity and specificity. Ovarian cancer is heterogeneous in nature and contain a sub-population of stem cell-like characteristics that has the ability to grow as anchorage-independent manner and subsequently is able to metastasize. Highly tumorigenic and chemotherapy-resistant cancer stem cells (CSCs)-specific biomarkers therefore reflects the interesting possibilities to be targeted to minimize the high frequency of relapse and resistance to drugs. Several putative ovarian CSC markers such as CD24, CD44, CD133, SSEA have already been proposed in recent studies, yet, a large panel of updated biomarkers have high clinical relevance to define the prospective isolation of viable circulating CSCs. Therefore, this review highlights current evidence based updated ovarian cancer specific prognostic and diagnostic biomarkers and potential importance of CSCs in context of tumorigenicity and metastatic activity for fundamental biological and clinical implications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement

The localized failures of gears introduce cyclic-transient impulses in the measured gearbox vibration signals. These impulses are usually identified from the sidebands around gear-mesh harmonics through the spectral analysis of cyclo-stationary signals. However, in practice, several high-powered applications of gearboxes like wind turbines are intrinsically characterized by nonstationary processes that blur the measured vibration spectra of a gearbox and deteriorate the efficacy of spectral diagnostic methods. Although order-tracking techniques have been proposed to improve the performance of spectral diagnosis for nonstationary signals measured in such applications, the required hardware for the measurement of rotational speed of these machines is often unavailable in industrial settings. Moreover, existing tacho-less order-tracking approaches are usually limited by the high time-frequency resolution requirement, which is a prerequisite for the precise estimation of the instantaneous frequency. To address such issues, a novel fault-signature enhancement algorithm is proposed that can alleviate the spectral smearing without the need of rotational speed measurement. This proposed tacho-less diagnostic technique resamples the measured acceleration signal of the gearbox based on the optimal warping path evaluated from the fast dynamic time-warping algorithm, which aligns a filtered shaft rotational harmonic signal with respect to a reference signal assuming a constant shaft rotational speed estimated from the approximation of operational speed. The effectiveness of this method is validated using both simulated signals from a fixed-axis gear pair under nonstationary conditions and experimental measurements from a 750-kW planetary wind turbine gearbox on a dynamometer test rig. Lastly, the results demonstrate that the proposed algorithm can identify fault information from typical gearbox vibration measurements carried out in a resource-constrained industrial environment.

17 WIND ENERGY↗
DOE OSTI2017

Evaluation of 64 Cu-Based Radiopharmaceuticals that Target Aβ Peptide Aggregates as Diagnostic Tools for Alzheimer’s Disease

Positron emission tomography (PET) imaging agents that detect amyloid plaques containing amyloid beta (Aβ) peptide aggregates in the brain of Alzheimer’s disease (AD) patients have been successfully developed and recently approved by the FDA for clinical use. However, the short half-lives of the currently used radionuclides 11 C (20.4 min) and 18 F (109.8 min) may limit the widespread use of these imaging agents. Therefore, we have begun to evaluate novel AD diagnostic agents that can be radiolabeled with 64 Cu, a radionuclide with a half-life of 12.7 h, ideal for PET imaging. Described here in this paper are a series of bifunctional chelators (BFCs), L 1 –L 5 , that were designed to tightly bind 64 Cu and shown to interact with Aβ aggregates both in vitro and in transgenic AD mouse brain sections. Importantly, biodistribution studies show that these compounds exhibit promising brain uptake and rapid clearance in wild-type mice, and initial microPET imaging studies of transgenic AD mice suggest that these compounds could serve as lead compounds for the development of improved diagnostic agents for AD.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

Abstract Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool’s accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE’s user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Conditional guided generative diffusion for particle accelerator beam diagnostics

Abstract Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam’s 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam’s longitudinal phase space at megapixel resolution (1024 × 1024) based on a generative conditional diffusion model. We demonstrate the model’s generative ability on experimental data from the European X-ray FEL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
DOE OSTI2025

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Laser-Accelerated Proton Beams as Diagnostics for Cultural Heritage

This paper introduces the first use of laser-generated proton beams as diagnostic for materials of interest in the domain of Cultural Heritage. Using laser-accelerated protons, as generated by interaction of a high-power short-pulse laser with a solid target, we can produce proton-induced X-ray emission spectroscopies (PIXE). By correctly tuning the proton flux on the sample, we are able to perform the PIXE in a single shot without provoking more damage to the sample than conventional methodologies. We verify this by experimentally irradiating materials of interest in the Cultural Heritage with laser-accelerated protons and measuring the PIXE emission. The morphological and chemical analysis of the sample before and after irradiation are compared in order to assess the damage provoked to the artifact. Monte Carlo simulations confirm that the temperature in the sample stays safely below the melting point. Compared to conventional diagnostic methodologies, laser-driven PIXE has the advantage of being potentially quicker and more efficient.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Classifying the full SDSS-IV MaNGA Survey using optical diagnostic diagrams: Presentation of AGN catalogs in flexible apertures

Accurate active galactic nucleus (AGN) identifications in large galaxy samples are crucial for the assessment of the role of AGN and AGN feedback in the co-evolution of galaxies and their central supermassive black holes. Emission-line flux-ratio diagnostics are commonly used to identify AGN in optical spectra. New large samples of integral field unit observations allow exploration of the role of aperture size in the classification process. In this paper, we present galaxy classifications for all 10010 galaxies observed within the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. We use Baldwin-Philips-Terlevich line flux-ratio diagnostics combined with an Hα equivalent threshold in 60 apertures of varying size for the classification, and provide the corresponding catalogs. MaNGA-selected AGN primarily lie below the main sequence of star-forming galaxies, and reside in massive galaxies with stellar masses of ~10 11 M ⊙ and a median Hα-derived star formation rate of ~1.44M ⊙ yr -1 . We find that the number of “fake” AGN increases significantly beyond selection apertures of >1.0 R eff because of increased contamination from diffuse ionized gas (DIG). A comparison with previous works shows that the treatment of the underlying stellar continuum and flux measurements can significantly impact galaxy classification. Our work provides the community with AGN catalogs and galaxy classifications for the full MaNGA survey.

79 ASTRONOMY AND ASTROPHYSICS↗