Search NASA⌕ Search

SEARCH · Search NASA

Results for “Real time analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Operando Electrochemical Strain Generation in α-NaFeO 2 Composite Cathodes during Cycling of Na-Ion Batteries

The transition metal oxide (TMO) cathodes in Na-ion batteries suffer from low-capacity retention. Chemo-mechanical instabilities lead to the deterioration of the electrochemical performance of TMO cathodes in Li-ion batteries. However, there is not much known about the chemo-mechanical instabilities in the TMO cathodes for Na-ion batteries. Understanding the governing forces behind the interplay between the electrochemical performance and mechanical stability in TMO cathodes is critical for the development of Na-ion batteries. Here, we synchronize the digital image correlation (DIC) technique with electrochemical analysis to capture the real-time deformation behavior of the α-NaFeO 2 cathodes during cycling. When the charge cutoff voltage is 3.6 V, the cathode experiences reversible deformations (except for the first cycle). There is negative strain (shrinkage) generation during Na extraction and positive strain (expansion) generation during the subsequent Na insertion. A detailed analysis of the potential-dependent strain rate evolution points out complicated phase transformations and nonequilibrium conditions in the α-NaFeO 2 cathodes during cycling. When the charge cutoff voltage was increased to 4.2 V, there was a rapid capacity loss and large plastic deformations in the α-NaFeO 2 cathodes. We provide an in-depth discussion about the possible mechanisms behind the chemo-mechanical instabilities in the α-NaFeO 2 . In conclusion, the correlation is critical to develop material-based strategies to mitigate instability mechanisms in TMO cathodes for Na-ion batteries.

Wable, Minal [University of Maryland Baltimore Cou↗
DOE OSTI2023

Operator-theoretic Resilience Calculations (ORC)

This project developed operational metrics to characterize the resilience of a dynamic network in real-time from time-series data by proposing: a) Resilience ratio b) Recovery energy. Resilience analysis considers the controllability and observability and provides a real-time under- standing on resilience without creating/simulating them. The report focuses on the application of resilience metrics on the HYPERSIM data and insights obtained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The Role of Titania Surface Coating by Atomic Layer Deposition in Improving Osteogenic Differentiation and Hard Tissue Formation of Dental Pulp Stem Cells

Recent studies have focused on modifying implant surface properties to improve bone regeneration and osseointegration. Herein, the role of titania surface coating in enabling dental pulp stem cells (DPSCs) differentiation and hard tissue formation is studied. Atomic layer deposition is applied at lower temperature to coat a nanolayer of titania onto hard and soft polybutadiene substrates without altering their moduli, to probe the effects of surface chemistry and substrate modulus. In the absence of titania, DPSCs prefer to attach and proliferate on the hard substrate, where particle-like biomineralization is present after 28 days in culture, whereas no biomineralization is observed on the soft substrate. Applying titania surface coating results in the improvement of adhesion and proliferation, regardless of substrate modulus, whereas cell and substrate moduli remain the same as control without titania coating. Furthermore, templated biomineralization is observed given the bone-like tissue composition by Raman analysis, in consistent with real time-polymerase chain reaction (RT-PCR) results showing upregulation of osteopontin (OPN) and bone sialoprotein (BSP), representing osteogenic differentiation. Furthermore, the results indicate that titania surface coating improved the ability to nucleate banded collagen fibers which in turn templated mineral deposits, and further induced the osteogenesis of DPSCs, regardless of its substrate modulus.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹ instrument, verb, target › combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. Here, the challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. Here, a total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multiplexable intrinsic Fabry-Pérot interferometers inscribed by femtosecond laser for vibration measurement in high temperature environments

This article reports fabrication technique and demodulation algorithm developments to use multiplexable intrinsic Fabry-Pérot interferometer (IFPI) fiber sensor array for distributed vibration measurements at high temperatures. Using femtosecond laser direct writing scheme, IFPI array were fabricated through laser-induced Type II scattering points in single mode fiber cores. Reflection spectra of IFPI array were demodulated in real-time using modified Bunemen frequency analysis. The demodulation algorithm, which was implemented using a photodetector-array spectrometer, achieves 64-nε dynamic strain resolution at 1-kHz spectral acquisition rate and 2-kHz maximum vibration bandwidth. Performance and stabilities of IFPI sensor array were characterized from room temperatures to 800 °C. The static strain resolution was 0.6 με and the minimum detectable dynamic strain amplitude was 23 nε/√Hz at 800 °C. The multiplex performance was also tested by measuring dynamic strain in time domain through six IFPI sensor array fabricated in one fiber. This paper presents an integrated and low-cost sensing solution to perform distributed vibration measurements in harsh environments.

42 ENGINEERING↗
DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗
DOE OSTI2025

A device for volatile organic compound (VOC) analysis from skin using heated dynamic headspace sampling

Abstract Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Cyber State Awareness For Resilience

Characterization of cyber-physical attacks requires a holistic understanding of cyber and physical behavior in a system. Machine Learning Anomaly Detection provides a compelling solution for continuously identifying suspicious behavior within these complex systems. In our software, we present an approach for holistic characterization of cyber-physical systems based on cyber and physical anomaly correlation. The approach consists of three main components: 1) an architecture for real-time data acquisition, management, and analysis of both cyber and physical data; 2) cyber and physical data driven anomaly detection systems (ADSs), 3) a metric that combines cyber and physical ADSs to provide a holistic characterization of the system..

Rieger, CraigG.↗
DOE OSTI2023

An Online Prototype Toolset for Predicting and Optimizing P&T Performance (FY23 Status Report)

A new web-based toolset is being developed to support ongoing remediation optimization efforts and implementation of an adaptive site management strategy for the 200 West Area Pump-and-Treat (P&T) system at the Hanford Site. This toolset, comprising the well performance index tool and the well optimization pre-screening tool, will offer a user-friendly interface to predict and optimize the P&T well network’s performance at a preliminary level. Efforts in fiscal year (FY) 2023 focused on three main components: updating the existing deep learning model for predicting P&T performance, designing and developing a prototype of a web-based performance index tool, and initiating the conceptual design of the well optimization pre-screening tool. The well performance index tool is based on a pre-trained deep learning model that allows users to select a target contaminant and well screen length, then visualize the predicted performance of potential new wells across the site. The well optimization pre-screening tool includes two separate modules: the pre-computed scenario viewer, which organizes and visualizes offline optimization simulation results, and the quick analysis module, which provides real-time model prediction using user-specified well locations. In FY24, the plan is to add web-based applications to SOCRATES for both the well performance prediction tool and the optimization prescreening tool, with accompanying user and theory guides. These tools are intended to enable an accessible, easily applied, and transparent approach to remedy planning and decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High Speed Data Acquisition for Real Time Feedback in a Light Duty Engine Combustion-Mode Switching Application

The paper describes the integration of a high-speed data acquisition and diagnostics controller used in an advanced engine platform. The controller enables ultra-low emissions and new benchmarks of engine efficiency while running a Gasoline Compression Ignition (GCI) cycle on a 2.2L, 4-cylinder engine. The system enables real-time combustion feedback and vibration analysis in engines. The paper focuses on: (1) the development of an interpolative sampling algorithm for transposition of time acquired data to the crank angle domain using a production crank sensor (60-2 tooth wheel); (2) the control unit, high-speed data acquisition, communication rates between the dedicated data acquisition and base controller to ensure cycle-to-cycle feedback; and, (3) validation exercises using cylinder pressure measurements. Here, the study shows how the algorithm resolves cylinder pressure information over an engine cycle, validating its robustness across acquisition rates of 50 and 200 kHz, with crank angle resolutions of 0.5° and 0.1°. Results show that the 50 kHz, 0.5° hardware yield combustion phasing and torque estimates within 0.1° and 1% torque of those attained with the 200 kHz, 0.1° hardware. Finally, study shows the dedicated controller and communication speeds allow for next-cycle fuel injection correction for speeds above 5000 rpm.

42 ENGINEERING↗
DOE OSTI2021

Thermal Stimulation and Injectivity Testing at Raft River, ID EGS Site

The Raft River geothermal field is the site of an innovative Department of Energy Enhanced Geothermal System (EGS) project to determine the viability of using combined thermal and hydraulic stimulation techniques to improve energy production. Well RRG-9 is currently undergoing a stimulation program using injectate from the US Geothermal Raft River Power Plant and cold water from a cooling tower make-up water well. The stimulation began on 13 June 2013 with injection from the power plant at a temperature of about 39 °C and a pressure of 275 psig. Next, two positive displacement plunger type pumps were used to increase the injection pressure and flow rate for about one month. The highest rate achieved was 258 gpm at a pressure of 741 psig. During this time, fluid from the cooler water well was injected for about 2 weeks at various pressures. Then, the pumps were removed and plant injection resumed on 25 September. Plant injection will continue until the spring of 2014, when a high pressure hydraulic stimulation will be conducted. A series of seismic monitoring stations deployed around the well are providing data on seismic events occurring at the site. Over the past year, 51 microseismic events have been recorded, all less than Magnitude 1. During injection, several diagnostic tests were conducted to gain a better understanding of the well and reservoir. A step-rate test was performed on 22 August to measure the in-situ stress and aid in modeling in-situ fractures. A tracer was injected into the well on 9 September. No tracer was detected in adjacent production wells after several months. A second borehole televiewer survey was conducted for comparison to pre-stimulation images. Here, a third borehole televiewer survey is planned after the high pressure stimulation. Injection test data is evaluated in real time. A modified Hall plot analysis indicates the effective permeability is increasing. The injectivity index supports the results of the modified Hall plot analysis. As the thermal stimulation has continued, the injectivity index has consistently followed an upward trend from 0.1 gpm/psi to 0.53 gpm/psi.

enhanced geothermal systems↗
DOE OSTI2023

HERMES: A cyber-energy SAAS platform to provide real-time 2d and 3d interactive visualizations [SWR-23-15]

HERMES is a novel visualization SAAS platform for achieving real-time visualization of large-scale environments involving cyber-energy devices. The platform receives data from emulated environments which include physical hardware and network devices that represent a complex IT / OT system. The platform is capable of streaming, filtering, storing, and visualizing all data within the environment and scaling vertically and horizontally. This capability enables high fidelity visual analysis of events to be performed in real time as well as the collection and storage of historical data for forensic analysis.

Van Natta, Joshua↗
DOE OSTI2024

National Security Programs - Cyber: MMAREJBLIGE – Modular Multi Agent Grid Emulation for Joined Breakdowns in Linked Generative Emulations - 23-0644

Modular Multi Agent Grid Emulations for Joined Breakdowns in Linked Generative Emulations (MMAREJBLIGE) introduces an agent-based modeling framework into real-time cyber-physical emulation to achieve a context-aware environment that introduces operator/attacker/external-condition variability to improve emulation fidelity and testing rigor. We detail our agent framework design, internal communication via message passing, and time synchronization, as well as the individual components of the system. We include a brief analysis of several scenarios run on a real-time, hardware-in-the-loop, Industrial Control Systems (ICS) test-bed which include normal operation, physical disruption, disruption with mitigation, and disruption with mitigation during a cyber denial-of-service (DOS) attack.

42 ENGINEERING↗
DOE OSTI2022

BoxScore - A real-time beam-diagnosis program for the CAEN digitizer x730 series

BoxScore is a real-time beam diagnosis and monitoring program for the CAEN x730 series digitizer, developed for the ATLAS in-flight system at Argonne National Laboratory. The CAEN x730 series digitizer, with built-in Digital Pulse Processing for the Pulse-Height-Analysis, digitizes the input signal in real-time and processes it using a trapezoidal filter. BoxScore reads the digitizer’s buffer directly, builds and saves events to local files, plots histograms for particle identification, and outputs the rates of selected isotopes every second. Implementation of BoxScore has shortened the time needed for in-flight beam-tuning and has potential applications for other nuclear physics experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.

EPICS pvAccess↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗