Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “real time experiments”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

FOS: Physical sciences↗
DOE OSTI2023

Retractable Sensors for In-Core Use in Material Test Reactors - conf paper

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on friction drive wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 – 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 – 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology’s MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Monitoring strain evolution in water-sand systems using distributed acoustic sensing for geohazard early warning

Rainfall-driven hazards such as landslides, debris flows, and earthen dam failures often arise when water changes the internal strain within sand. This study evaluates the ability of distributed acoustic sensing to monitor these strain changes in real time. We embed a fiber-optic cable in a sand-filled glass cylinder and run controlled dry- and wet-sand experiments to measure how strain develops as water infiltrates, saturates, and drains from the sand. The sensing system detects uneven water movement in dry sand and enables millimeter-scale estimates of infiltration rates, and in wet sand it tracks rising water levels, delayed strain peaks after saturation, and abrupt strain shifts during drainage. These results show that fiber-optic sensing captures subtle strain evolution throughout the full water-sand interaction cycle. The study demonstrates that fiber-optic sensing offers promising potential for real-time and cost-effective monitoring and early warning of rainfall-induced geohazards.

58 GEOSCIENCES↗
DOE OSTI2022

Cold-atom quantum simulator for string and hadron dynamics in non-Abelian lattice gauge theory

We propose an analog quantum simulator for simulating real-time dynamics of (1+1)-dimensional non-Abelian gauge theory well within the existing capacity of ultracold-atom experiments. The scheme calls for the realization of a two-state ultracold fermionic system in a one-dimensional bipartite lattice, and the observation of subsequent tunneling dynamics. Being based on the loop string hadron formalism of SU(2) lattice gauge theory, this simulation technique is completely SU(2) invariant and simulates accurate dynamics of physical phenomena such as string breaking and/or pair production. The scheme is scalable and particularly effective in simulating the theory in the weak-coupling regime, and also a bulk limit of the theory in the strong-coupling regime up to certain approximations. This paper also presents a numerical benchmark comparison of the exact spectrum and real-time dynamics of lattice gauge theory to that of the atomic Hamiltonian with an experimentally realizable range of parameters.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Real-time reconstruction of ground motion during small magnitude earthquakes: A pilot study

This study presents a pilot investigation into a novel method for reconstructing real-time ground motion during small magnitude earthquakes (M < 4.5), removing the need for computationally expensive source characterization and simulation processes to assess ground shaking. Small magnitude earthquakes, which occur frequently and can be modeled as point sources, provide ideal conditions for evaluating real-time reconstruction methods. Utilizing sparse observation data, the method applies the Gappy Auto-Encoder (Gappy AE) algorithm for efficient field data reconstruction. This is the first study to apply the Gappy AE algorithm to earthquake ground motion reconstruction. Numerical experiments conducted with SW4 simulations demonstrate the method’s accuracy and speed across varying seismic scenarios. The reconstruction performance is further validated using real seismic data from the Berkeley area in California, USA, demonstrating the potential for practical application of real-time earthquake data reconstruction using Gappy AE. As a pilot investigation, it lays the groundwork for future applications to larger and more complex seismic events.

58 GEOSCIENCES↗
DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗
DOE OSTI2021

A contextual sensor system for non-intrusive machine status and energy monitoring

Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Initial results from boron powder injection experiments in WEST lower single null L-mode plasmas

Using a recently installed impurity powder dropper (IPD), boron powder (<150 μm) was injected into lower single null (LSN) L-mode discharges in WEST. IPDs possibly enable real-time wall conditioning of the plasma-facing components and may help to facilitate H-mode access in the full-tungsten environment of WEST. The discharges in this experiment featured I p = 0.5 MA, B T = 3.7 T, q 95 = 4.3, t pulse = 12–30 s, n e,0 ~ 4 × 10 19 m –2 , and P LHCD ~ 4.5 MW. Estimates of the deuterium and impurity particle fluxes, derived from a combination of visible spectroscopy measurements and their corresponding S/XB coefficients, showed decreases of ~50% in O + , N + , and C + populations during powder injection and a moderate reduction of these low-Z impurities (~50%) and W (~10%) in the discharges that followed powder injection. Along with the improved wall conditions, WEST discharges with B powder injection observed improved confinement, as the stored energy W MHD , neutron rate, and electron temperature T e increased significantly (10%–25% for W MHD and 60%–200% for the neutron rate) at constant input power. Notably, these increases in confinement scale up with the powder drop rate and are likely due to the suppression of ion temperature gradient (ITG) turbulence from changes in Z eff and/or modifications to the electron density profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Initial Results from Boron Powder Injection Experiments in WEST Lower Single Null L-mode Plasmas

Using a recently installed impurity powder dropper (IPD), boron powder (< 150 μm) was injected into lower single null (LSN) L-mode discharges in WEST. IPDs possibly enable real-time wall conditioning of the plasma-facing components and may help to facilitate H-mode access in the full-tungsten environment of WEST. The discharges in this experiment featured Ip = 0.5 MA, BT = 3.7 T, q95 = 4.3, tpulse = 12–30 s, ne,0 ~ 4×1019 m-2, and PLHCD ~ 4.5 MW. Estimates of the deuterium and impurity particle fluxes, derived from a combination of visible spectroscopy measurements and their corresponding S/XB coefficients, showed decreases of ~ 50% in O+, N+, and C+ populations during powder injection and a moderate reduction of these low-Z impurities (~ 50%) and W (~ 10%) in the discharges that followed powder injection. Along with the improved wall conditions, WEST discharges with B powder injection observed improved confinement, as the stored energy WMHD, neutron rate, and electron temperature Te increased significantly (10–25% for WMHD and 60–200% for the neutron rate) at constant input power. These increases in confinement scale up with the powder drop rate and are likely due to the suppression of ion temperature gradient (ITG) turbulence from changes in Zeff and/or modifications to the electron density profile.

Boronization↗
DOE OSTI2021

Accelerator Real-time Edge AI for Distributed Systems (READS) (Proposal)

Over the last decade, Machine Learning (ML) technologies have slowly made their way into the accelerator community. Rapid advances in recent years in deep learning, particularly reinforcement learning for control system applications and the accessibility of deep learning in embedded hardware, have generated renewed interest and spawned a number of applications. The Fermilab Accelerator Complex, shown in Fig. 1, has provided High Energy Physics (HEP) experiments with proton beams for nearly fifty years. The current focus of the laboratory is its world-class experimental program at the intensity frontier. While increasing beam intensity certainly presents its own challenges, preserving beam size while minimizing beam losses – particles lost through interactions with the beam vacuum pipe – turns out to be, in many ways, the main challenge. The accelerator is controlled via a complex system of hundreds of thousands of devices. Enabling fine tuning and real-time optimization of their parameters using ML methods and stepping beyond experience-based reasoning of human operators are key to the success of future intensity upgrades. Our objective will be to integrate ML into accelerator operations and furthermore, provide an accessible framework, which can also be used by a broad range of other accelerator systems with dynamic tuning needs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Soft pions and transport near the chiral critical point

During the expansion of a heavy ion collision, the system passes close to the $\textit{O}$(4) critical point of QCD, and thus the fluctuations of the order parameter $(σ, \overrightarrow{π}$) are expected to be enhanced. In this work, our goal is to compute how these enhanced fluctuations modify the transport coefficients of QCD near the pseudocritical point. We also make a phenomenological estimate for how chiral fluctuations could effect the momentum spectrum of soft pions. We first formulate the appropriate stochastic hydrodynamic equations close to the $\textit{O}$(4) critical point. Then, working in mean field, we determine the correlation functions of the stress tensor and the currents which result from this stochastic real-time theory, and use these correlation functions to determine the scaling behavior of the transport coefficients. The hydrodynamic theory also describes the propagation of pion waves, fixing the scaling behavior of the dispersion curve of soft pions. We present scaling functions for the shear viscosity and the charge conductivities near the pseudocritical point and estimate the absolute magnitude of the critical fluctuations to these parameters and the bulk viscosity. Using the calculated pion dispersion curve, we estimate the expected critical enhancement of soft pion yields, and this estimate provides a plausible explanation for the excess seen in experiment relative to ordinary hydrodynamic computations. Our results motivate further phenomenological and numerical work on the implications of chiral symmetry on real-time properties of thermal QCD near the pseudocritical point.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗
DOE OSTI2025

Fermilab Phoebus

The Fermilab Phoebus code contains FNAL site-specific code to customize the Control Systems Studio Phoebus application. Phoebus is a tool used to create and present graphical displays based on current real-time and historic values of sensors, gauges, alarms, and many other devices in accelerators and physics experiments here and at other laboratories around the world.

Velarde, Mariana González [Fermi National Accelera↗
DOE OSTI2024

Determination of the mean energy of fast electron losses and anisotropies through thick-target emission on WEST

Abstract A new method to obtain the mean energy of fast electron losses in fusion plasmas using a versatile multi-energy hard x-ray (HXR) detector is presented. The method is based on measuring the thick-target emission of tungsten in the divertor region produced by fast electron losses interacting with the target and modeling the tungsten spectra by a Monte Carlo code which simulates the interaction between a beam of electrons and a solid target. The mean energy of the fast electron losses is determined through the comparison between the experimental and synthetic emission. The results show that fast electron losses during lower hybrid current drive discharges at WEST have a mean energy of 90–140 keV and represent only 2% of the total heat flux at the target. Additionally, anisotropic HXR emission has been detected for the first time at the WEST core and edge plasma, with opposite directions. It is due to the forward-peak emission of two distinctive populations of fast electrons: co-current fast electrons in the core and counter-current fast electron losses at the inner strike point. In view of future experiments like ITER where electron cyclotron current drive will generate a fast electron population, this technique could serve as a real-time monitor of fast electron losses and eventually feed an actuator on the current drive generation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗