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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Analysis of SPND Material Candidates for Real-Time Neutron Spectrum Monitoring Poster

The future of the nuclear industry will be shaped by advanced reactor designs requiring innovative instrumentation for safe operation. A key area of innovation is the ability to accurately and reliably monitor the neutron energy spectrum during operations, which would also enhance the experimental capabilities of existing research reactors. To address this need, our research focuses on the potential use of Self-Powered Neutron Detectors (SPNDs) for real-time neutron spectrum monitoring. We investigated various candidate materials for SPNDs by evaluating their energy-dependent reaction rates. Promising materials were further modeled using MCNP to determine the free electron production per neutron across the neutron energy spectrum, approximating their energy-dependent signal sensitivity. Our findings identified Samarium (Sm) and Gadolinium (Gd) as top candidates for thermal neutrons, Hafnium (Hf) and Thulium (Tm) for epithermal neutrons, and Tantalum (Ta) for fast neutrons. These materials will undergo future experimental validation to confirm the modeled responses and assess their feasibility for real-time neutron spectrum measurements.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2025

Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro- Waveform Measurements

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. Here, the outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.

High-fidelity synchro-waveform measurements↗
DOE OSTI2026

Metalloproteins in an Era of Modern Crystallography and Why the Beamline Matters

The Structural Molecular Biology (SMB) macromolecular crystallography (MC) group at the Stanford Synchrotron Radiation Lightsource (SSRL) have developed state-of-the-art capabilities tailored for metalloenzyme structural analysis. Metalloproteins sit at the center of biology’s most audacious chemistry. From multi-electron redox catalysis to radical rearrangements and light-driven transformations, metal sites give proteins access to reaction landscapes that would otherwise be inaccessible under ambient conditions. Yet their study presents a fundamental paradox for MC studies: the very X-rays we use to reveal atomic structure can alter the electronic states we seek to understand. As the field moves beyond static snapshots toward mechanistic insight, success increasingly depends on our ability to maintain metal centers in their native state throughout the experiment. The SSRL SMB-MC beamlines integrate a suite of capabilities specifically designed to address these challenges. By combining in situ spectroscopic verification, intelligent dose management, controlled reaction initiation, optimized anomalous diffraction, and real-time crystallographic diffraction analysis, these tools enable researchers to interrogate metalloproteins with unprecedented rigor. This article explores how these complementary approaches are reshaping our ability to capture metalloprotein chemistry, and what this means for mechanistic studies at synchrotron beamlines.

Maggiolo, Ailiena O. [SLAC National Accelerator La↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2021

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

Abstract Multimodal in-situ experiments are the wave of the future, as this approach will permit multispectral data collection and analysis during real-time nanoscale observation. In contrast, the evolution of technique development in the electron microscopy field has generally trended toward specialization and subsequent bifurcation into more and more niche instruments, creating a challenge for reintegration and backward compatibility for in-situ experiments on state-of-the-art microscopes. We do not believe this to be a requirement in the field; therefore, we propose an adaptive instrument that is designed to allow nearly simultaneous collection of data from aberration-corrected transmission electron microscopy (TEM), probe-corrected scanning transmission electron microscopy, ultrafast TEM, and dynamic TEM with a flexible in-situ testing chamber, where the entire instrument can be modified as future technologies are developed. The value would be to obtain a holistic understanding of the underlying physics and chemistry of the process-structure–property relationships in materials exposed to controlled extreme environments. Such a tool would permit the ability to explore, in-situ, the active reaction mechanisms in a controlled manner emulating those of real-world applications with nanometer and nanosecond resolution. If such a powerful tool is developed, it has the potential to revolutionize our materials understanding of nanoscale mechanisms and transients. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2021

Few-layer tin sulfide (SnS): Controlled synthesis, thickness dependent vibrational properties, and ferroelectricity

Group IV monochalcogenides, anisotropic van der Waals crystals (and black phosphorus analogues), are attracting increasing interest due to a number of exceptional properties including anisotropic optoelectronics and multiferroicity predicted and in part realized in the ultrathin limit. Due to their enhanced chemical reactivity, both exfoliation and synthesis of monolayer and few-layer crystals pose challenges not found in other 2D/layered materials, such as the transition metal dichalcogenides. In this work, we show that SnS synthesis on SnS 2 van der Waals substrates can address these challenges and consistently produces few-layer flakes, a capability that is explained via analysis of real-time microscopy of the growth process. Raman spectroscopy combined with efficient computations of the Raman-active modes across an extended thickness range enables a comprehensive understanding of the evolution of the vibrational properties of SnS with number of layers. Lateral piezoresponse force microscopy provides unprecedented insight into the stacking-dependent polarization and ferroelectric domain structures in large few-layer SnS flakes. The combined results establish a basis for further fundamental studies and applications of SnS and other group IV monochalcogenides in the few-layer regime.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distinct Gas-Particle Partitioning and Viscosity Characteristics of Secondary Organic Aerosols Derived from α-Pinene versus Ocimene

Secondary organic aerosols (SOA) have complex, multicomponent composition that controls particle viscosity and gas-particle partitioning, key factors to their atmospheric evolution. This study investigates the chemical composition, volatility and viscosity of SOA formed by ozonolysis of cyclic α-pinene (PSOA) and acyclic ocimene (OSOA) monoterpenes. Using Temperature-Programmed Desorption combined with Direct Analysis in Real-Time ionization and High-Resolution Mass Spectrometry, we determined the molecular composition and saturation mass concentration of individual SOA constituents. These data enabled gas-particle partitioning and viscosity estimates under varied atmospheric conditions. PSOA, composed of higher molecular weight and less oxidized species, shows higher condensability and viscosity under high total organic mass (tOM) loadings. Here, in contrast, OSOA, consisting of more oxidized, lower molecular weight species, exhibits greater sensitivity to tOM, with viscosity increasing significantly upon dilution. Poke-flow experiments support this trend, indicating that OSOA undergoes more dynamic compositional and phase changes during atmospheric aging. These observations reveal distinct dynamic trends in the atmospheric transformations and reactivity of SOA from cyclic and acyclic monoterpenes, with the latter showing greater compositional changes during aging that alter viscosity and diffusion. This highlights the importance of incorporating such dynamic transformations into atmospheric models to improve predictions of SOA atmospheric loadings, lifetimes, and impacts.

cyclic and acyclic monoterpenes↗
DOE OSTI2023

Chemical characterization of microplastic particles formed in airborne waste discharged from sewer pipe repairs

Microplastic particles are of increasing environmental concern due to the widespread uncontrolled degradation of various commercial products made of plastic and their associated waste disposal. Recently, common technology used to repair sewer pipes was reported as one of the emission sources of airborne microplastics in urban areas. This research presents results of the multi-modal comprehensive chemical characterization of the microplastic particles related to waste discharged in the pipe repair process and compares particle composition with the components of uncured resin and cured plastic composite used in the process. Analysis of these materials employs complementary use of surface-enhanced Raman spectroscopy, scanning transmission X-ray spectro-microscopy, single particle mass spectrometry, and direct analysis in real-time high-resolution mass spectrometry. Further, it is shown that the composition of the relatively large (100 μm) microplastic particles resembles components of plastic material used in the process. In contrast, the composition of the smaller (micrometer and sub-micrometer) particles is significantly different, suggesting their formation from unintended polymerization of water-soluble components occurring in drying droplets of the air-discharged waste. In addition, resin material type influences the composition of released microplastic particles. Results are further discussed to guide the detection and advanced characterization of airborne microplastics in future field and laboratory studies pertaining to sewer pipe repair technology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Advanced flip-coil system for magnetic field integral measurements of insertion devices

A novel flip-coil measurement system has been developed for the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory. This paper describes the design, implementation, and commissioning of the new measurement bench, highlighting its key features, including improved mechanical stability, advanced data acquisition, and enhanced reproducibility. The system enables precise characterization of field integrals and multipole components, ensuring the optimal performance of Insertion Devices (IDs) before installation in the NSLS-II storage ring. The flip-coil system incorporates an innovative approach to minimize mechanical and electrical errors, which significantly improves the reproducibility of measurements. In addition, the system features a state-of-the-art data acquisition system that enables real-time monitoring and analysis, further enhancing the efficiency and accuracy of the measurement process. Furthermore, preliminary tests have demonstrated that the new system meets the stringent requirements for magnetic field characterization of advanced insertion devices, making it an essential tool for future ID commissioning and quality assurance at NSLS-II.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗
DOE OSTI2024

STUDIES ON PIPELINE POLYETHYLENES IN HYDROGEN GAS ENVIRONMENTS USING IN-SITU AND EX-SITU CHARACTERIZATION METHODS

Polymeric materials are commonplace in the natural gas infrastructure as distribution pipes, coatings, seals, and gaskets. Under the auspices of the U.S. Department of Energy HyBlend program, one of the means to reduce greenhouse gas emissions is with replacing natural gas, either partially or completely, with hydrogen. This approach makes it imperative that we conduct near-term and long-term materials compatibility research in these relevant environments. Insights into the effects of hydrogen and hydrogen gas blends on polymer integrity can be gained through both ex-situ and in-situ analytical methods. Our work represented here highlights a study of the behavior of pipeline polyethylene (PE) materials, including HDPE (Dow 2490 and GDB50) and MDPE (Ineos and legacy Dupont Aldyl A), when exposed to hydrogen by means of in-situ X-ray scattering and ex-situ Raman spectroscopy techniques. Samples were tested in ex-situ hydrogen and argon gas environments and in-situ hydrogen environments to identify differences due to permeation and solubility of these gases. These methods complemented each other because Raman spectroscopy could capture permanent effects after materials were removed from gaseous environments, and in-situ X-ray scattering analysis collected real-time data to elucidate the impact of the gas environment on polymer microstructure. Data collected revealed that the aforementioned polymers did not show significant changes in crystallinity and microstructure under the exposure conditions tested. Our findings from these studies will help establish real-time effects caused by hydrogen gas transport through pipeline polyethylenes by way of its influence on polymer structure and chemistry, which is directly related to pipeline mechanical strength and longevity of service.

Hydrogen materials compatibility, Natural gas pipe↗
DOE OSTI2024

Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction accuracy from 0.69% to 0.87%. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.

SSTDR, FDR, Reflectometry, Machine Learning, ARENA↗
DOE OSTI2024

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations

This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗
DOE OSTI2022

A Robotics Enabled Eddy Current Testing System for Autonomous Inspection of Heat Exchanger Tubes

The objective of the project is to develop a robotics enabled eddy current testing system (REECTS) in automatic probe deployment, inspection, and data acquisition and analysis. The main functions of the REECTS are to: 1) identify geometry and locations of heat exchange tubes with assistance of an imaging recognition system; 2) precisely control the position and motion speed of ECT probes by an adaptive control system; 3) facilitate data analysis and real-time decision making for autonomous inspection assisted by machine learning algorithms.

20 FOSSIL-FUELED POWER PLANTS↗