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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 757 records · Page 42

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗

DOE OSTI2021

Layered zinc hydroxide as an adsorbent for phosphate removal and recovery from wastewater

At present, phosphate removal and recovery from wastewater is gaining wide attention due to the dual issues of eutrophication, caused by the increased production of algae, and universal phosphorus scarcity. In this study, a layered zinc hydroxide (LZH) was synthesized by a simple precipitation method and characterized via various techniques. Experiments investigating the effect of contact time, pH, LZH dose, initial phosphate concentration, and co-existing ions on phosphate adsorption were conducted. LZH exhibited a high phosphate adsorption capacity (135.4 mg g -1 ) at a neutral pH. More than 50% of phosphate was removed within the first 60 s of contact time at an initial phosphate concentration of 5 mg L -1 . Phosphate removal using the as-prepared LZH adsorbent was also tested in real treated sewage effluent reducing the residual phosphate amount to levels inhibiting to the growth of algae. Furthermore, phosphate desorption from LZH was investigated using acetic acid and sodium hydroxide regenerants which showed to be very effective for phosphate recovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Performance of the Pair Spectrometer in Hall D at Jefferson Lab

This article describes the performance of the pair spectrometer installed in experimental Hall D at Jefferson Lab and its successfully operation in multiple experiments with the GlueX detector. The primary purpose of the pair spectrometer is the precise determination of the flux of beam photons incident on the GlueX target, a critical input for physics analyses such as absolute cross-section measurements. The photon energy spectrum is determined by reconstructing electron–positron pairs produced in a thin converter inserted into the photon beam. The spectrometer is integrated into the GlueX trigger system, whose electronics enable continuous real-time monitoring of the photon flux and the recording of e+e- pair candidates for offline analysis. In addition, the pair spectrometer provides a versatile test facility for evaluating calorimeter prototypes using leptons with well-defined energies produced via electromagnetic pair production.

Somov, Alexander [Jefferson Lab]↗

DOE OSTI2021

LncRNA HOTTIP from synovial fibroblast-derived exosomes: A novel molecular target for rheumatoid arthritis through the miR-1908–5p/STAT3 axis

Rheumatoid arthritis (RA) is a chronic inflammation mediated by autoimmune responses. HOTTIP, a long noncoding RNA (lncRNA), participates in cell proliferation and invasion. However, the correlation between HOTTIP and RA remains unclear. Therefore, this study aimed to clarify how HOTTIP works in RA and to investigate its role in the development of RA. Flow cytometry was used to analyze cell cycle progression. Binding between HOTTIP, signal transducer and activator of transcription 3 (STAT3) and miR-1908–5p was demonstrated by dual-luciferase assays. Quantitative real-time polymerase chain reaction (qRT–PCR) was used to measure the expression of T cell differentiation-related proteins. We found that HOTTIP was upregulated in rheumatoid arthritis synovial fibroblasts (RASFs). HOTTIP directly bound to miR-1908–5p and negatively modulated miR-1908–5p expression while positively regulating STAT3. The effects of HOTTIP overexpression on regulating the balance of the Th17/Treg cell ratio were partly reversed by miR-1908–5p overexpression. In addition, in vivo experiments demonstrated that overexpression of HOTTIP aggravated inflammation in RA mice, which was demonstrated by hematoxylin and eosin (HE) staining and the increased expression levels of CD4{sup +} interleukin (IL)-17{sup +}, forkhead Box P3 (FOXP3) and retinoid-related orphan receptor gamma-t (RORγt). In summary, our study suggests that HOTTIP plays a damaging role in RA by promoting inflammation, which may be related to the regulation of miR-1908–5p expression and the STAT3 signaling pathway. These results suggest that the regulation of HOTTIP may be a promising therapeutic strategy for RA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

X‑ray Coherent Diffractive Imaging of Large Helium Nanodroplets Doped with Small Molecules

We report the first X-ray coherent diffractive imaging experiment on molecule-doped helium nanodroplets. It complements previous work, where we reported single-shot X-ray coherent diffractive imaging studies of Xe dopant clusters formed in 4He and 3He droplets. These noble gas clusters were used to visualize the impact of rotational excitation of the droplets on the spatial distribution of atomic dopants within the droplets, and to study the differences and connections between quantum and classical droplet rotational motion. Here, we expand our studies to the molecular dopants CF4, CHF3, CH3CN, and SF6, imaged with 1.5 keV photons. We find multiple Bragg spots in the diffraction patterns of molecule-doped droplets with radii of approximately 600 nm, which provide evidence that molecules form elongated clusters with preferential alignment along the angular momentum axis of the 4He droplets, in agreement with our previous results on the aggregation of Xe clusters on quantum vortices. Real-space reconstructions of molecular dopant cluster density profiles are obtained for droplets with smaller radii of approximately 300 nm. The diffuse images suggest the formation of low-density, potentially porous, molecular clusters upon aggregation at T = 0.4 K in 4He droplets. In the normal fluid 3He droplets, molecules aggregate into loose clusters on the droplets' equator, similar to previous observations for Xe atoms. Time-of-flight mass spectra reveal that the doped helium nanodroplet moieties fragment extensively into constituent atomic ions, producing only a small fraction of molecular fragment ions. The findings are discussed in the context of previously proposed schemes to use He droplets as potential tamper materials for ultrafast X-ray imaging experiments.

Feinberg, AlexandraJ↗

DOE OSTI2023

The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human ‘expert’ classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 percent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than ~25 or Einstein radii less than ~1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier’s experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding

How data is encoded in a knowledge graph directly influences what can be done with that data. A common problem with many encodings is that they have difficulty representing ambiguity and evolution inherent in many real-world data sets. The data encoding represented in this paper (called CARAT) is a graph-level description of our attempt to capture data that is contradictory, ambiguous and evolves over time (including deleting information). The data encoding relies on tracking assertions about subjects rather than directly tracking states. This encoding decision resolves many issues our team had experienced using other data encodings but produces a a larger graph. This is a preliminary presentation of our experience with CARAT.

Cottam, Joseph A. [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2020

Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data

Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.

Ragan, Eric↗

DOE OSTI2025

Laser calibration system and lost muons correction in the g-2 experiment

The Muon g-2 experiment at Fermilab has the main goal to measure the muon anomalous magnetic moment $a_{µ}$ = ($g$ − 2)/2 to a precision of 0.14 parts per million (ppm), which means 4 times improvement in precision with respect to the final result from BNL: $a_{µ}$ (expt. BNL) = 11659208.0(6.3) × 10$^{−10}$ (0.54 ppm) In this report I summarize the work done at the $g$-2 experiment during my summer internship at Fermilab. In the first two weeks my first task has been to replace the NIM logic used in the Laser Calibration System with a new FPGA. Then, I was involved in the Lost Muons analysis studying both real data and MonteCarlo simulations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services

Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to evaluate actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Opinion: Lighting Research During and After the Pandemic

For many lighting researchers, the recent restrictions related to COVID-19 have paused most in-person laboratory or field experiments. In certain countries, regulatory institutions have allowed such experiments to resume but issued safety recommendations like pre-checking participants for disease symptoms and excluding those that are especially vulnerable. To avoid potential complications and delays, researchers are likely to resort to other research designs that enable remote research — eliminating the need for the experimenter and subjects to be in the same room. While unanticipated, this shift to remote research methods is an opportunity to revisit and further develop remote experimental techniques, which can have beneficial long-lasting implications on lighting research during and after the pandemic. Perhaps the most common type of remote research is online experiments; which can greatly benefit from improved techniques to allow for greater control or characterization of experimental conditions, like those proposed by Villa and Labayrade . These techniques require validation specific to the type of response collected and context, e.g. space type and attributes. New research techniques continue to emerge and will enable remote collection of objective measures such as gaze direction and high dynamic range images (HDRIs) without specialized equipment. In fact, many of these methods and techniques are commonly used in other disciplines, like vision science and psychology, and can be adapted for lighting research. Not all lighting studies can be conducted online, but in the age of ubiquitous and connected technologies, there is a largely missed opportunity to utilize crowdsourced responses to lighting conditions in real environments where people live and go about their daily lives without the research being limited to a certain time and laboratory locations. For instance, outdoor street luminaires can be rated by passersby using ecological momentary assessments on mobile phones. While this approach does not provide laboratory-level control over experimental conditions, it can provide more contextual responses resulting from ‘normal’ dynamic gaze, behavior, and interactions. Further, it can also reduce bias related to the experimental setting. Further development of remote research methods and their use in lighting research can improve the quality of lighting research. Given that sample sizes in most lighting research are relatively small, remote research can substantially increase sample sizes, reduce research costs, and shorten study duration, all while reducing interaction between experimenter and subjects. Another benefit is reaching a wider audience and including under-represented individuals who typically are not reachable using common recruitment methods. The pandemic has set back our plans for in-lab experiments, but can it inspire us to think of new and creative ways to expand lighting research?

Abboushi, Belal K.↗

DOE OSTI2023

Real-Time Detection and Localization of Line Trip Event Via Relative Phase Angles

Line trip events widely exist in power systems. They can result in power outages and a huge economic loss if not promptly detected and localized. To provide a fast and precise solution, this paper presents a Complete Coverage of Voltage Measurement (CCVM)-based line trip event detection algorithm and a Relative Phase Angle (RPA)-based line trip event localization algorithm. First, frequency and relative phase angle features during a line trip event are calculated. Then, the CCVM-based algorithm is proposed from both frequency and rate of change of frequency estimation algorithm aspects. Additionally, the RPA-based algorithm is presented, and two cases are studied to demonstrate the uniqueness of the proposed algorithm. Various experiments are conducted, where the simulation results demonstrate that the proposed CCVM-based algorithm can detect a line trip event as short as 2.07 ms. In addition, the RPA-based algorithm has 1.26 times higher localization accuracy compared with the frequency magnitude-based and phase angle-based algorithms. In conclusion, the experiment results on the examples from two interconnected power systems in the U.S. verified the performance of the proposed algorithms in wide-area power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Operando FTIR investigation of salt dynamics in Li-ion batteries during fast charging

Li-ion batteries, when charged at fast-charging rates ($>$2C), suffer from reduced lifetimes and can undergo catastrophic failure. During high-rate charging, Li-ions are unable to rapidly transport through high-loading electrodes ($>$4 mAh cm −2 ). This results in unequal charge distributions, potentials, and utilization of the active material, which can lead to Li plating. Li-ion concentration polarization, in which Li-ions deplete in the anode and accumulate in the cathode during charging, precedes Li plating. An operando FTIR-ATR graphite/NMC cell developed in this research captured Li-ion concentration polarization in real-time. During fast charging, decreases in Li-ion concentration ($>$95%) were measured at the back of the anode. This is the first verification of complete Li-ion depletion within the anode at high C-rates. The measurements also showed graphite stage transition. A P2D model was developed for comparison to the operando measurements. The measurements agreed with the model in some cases, but disparities existed at high C-rates and loadings. In the experiment, the Li-ion concentration often failed to recover to 1.2 M until several hours after charging, whereas the model Li-ion concentration rapidly recovered. The contrast between the model and experiment results indicates that further investigation is required to improve understanding of Li-ion concentration dynamics during fast charging.

25 - ENERGY STORAGE↗

DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Ab initio electron dynamics in high electric fields: Accurate prediction of velocity-field curves

Electron dynamics in external electric fields governs the behavior of solid-state electronic devices. First-principles calculations enable precise predictions of charge transport in low electric fields. However, studies of high-field electron dynamics remain elusive due to a lack of accurate and broadly applicable methods. In this work, we develop an efficient approach to solve the real-time Boltzmann transport equation with both the electric field term and ab initio electron-phonon collisions. These simulations provide field-dependent electronic distributions in the time domain, allowing us to investigate both transient and steady-state transport in electric fields ranging from low to high (> 10 kV/cm). The broad capabilities of our approach are shown by computing nonequilibrium electron occupations and velocity-field curves in Si, GaAs, and graphene, obtaining results in quantitative agreement with experiment. Our approach sheds light on microscopic details of transport in high electric fields, including the dominant scattering mechanisms and valley occupation dynamics. Our results demonstrate quantitatively accurate calculations of electron dynamics in low to high electric fields, with broad application to power and micro-electronics, optoelectronics, and sensing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

From Simple Labels to Time-Use Integrations: Supporting the Spectrum of Qualitative Travel Behavior Data

In transportation research, applications of travel behavior data collection are context-specific and require different types of qualitative inputs. These inputs can be viewed as spanning a spectrum of user burden and data quality, from simple trip labels to complex time-use surveys. However, each currently active smartphone-based travel diary platform appears to only support one type of qualitative input, and the effort required for customization is unclear. In this paper, we characterize the spectrum by defining four canonical use cases: (i) trip labels, (ii) trip questionnaire, (iii) counterfactual trips, and (iv) time-use surveys. We then outline a mechanism for supporting configurable user inputs on the same underlying smartphone-based sensing mechanism and demonstrate that it can support all the use cases without any code changes. We further demonstrate that the flexible data model that underpins this mechanism can enable real-time monitoring and analysis. Finally, we evaluate per-user data collection and engagement metrics for large-scale deployments of three canonical use cases, spanning 10 programs, 435 users, and 251,041 trips, and a maximum duration of 800 days. Future efforts may support additional use cases through an expanded configuration and provide greater insight into user engagement. We hope that these insights enable the research community to look at qualitative inputs through a new lens and experiment with novel use cases to fill in the spectrum.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2021

Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids

“Source ID Mix” spoofing emerged as a new type of cyber-attack on Distribution Synchrophasors (DS) where adversaries have the capability to swap the source information of DS without changing the measurement values. Accurate detection of such a highly-deceptive attack is a challenging task especially when the spoofing attack happens on short fragments of DS recorded within a relatively small geographical scale. Herein this letter proposes an effective approach to detect this cyber-attack by realizing the multifractal characteristics of DS measurements. First, the multifractal cross-correlation of DS measured at multiple intra-state locations is revealed. Then the derived correlation is integrated with weighted two-dimensional multifractal surface interpolation to reconstruct quasi high-resolution signals. Finally, informative location-specific signatures are extracted from the high-resolution DS and they are integrated with advanced machine learning techniques for source authentication. Experiments using the real-life DS are performed to verify the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗