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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Short-lived noble gas effluent trends from a research reactor

An understanding of anthropogenic sources of radioactive noble gases in the atmosphere is needed to enhance the discrimination ability of the International Monitoring System's sensors. These sources include commercial and research nuclear reactors and medical isotope production facilities. While abiding by local environmental ordinances these facilities all emit noble gas radioisotopes through normal operation. Here, this research presents measurements and analysis of noble gas isotopes ( 41 Ar, 135 Xe, 135m Xe, 137 Xe, 138 Xe, 87 Kr, 88 Kr, and 89 Kr) made directly at the stack of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. The Xe and Kr noble gases are concurrently observed with 41 Ar, a neutron activation product, when the reactor is operational. The magnitude of the Xe and Kr noble gases released is not constant over the HFIR cycle, but they temporally match the 41 Ar trend. An isotope activity ratio analysis of these shorter lived isotopes combined with the observation of the cycle's temporal trend helps understand the noble gas production mechanism at the HFIR. Isotopes with short half-lives are not useful for long-range environmental monitoring. However, these measurements could potentially be combined with atmospheric modeling to predict the background source term of the longer-lived Xe ratios at a monitoring station.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region

In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating precipitation, streamflow, and inundation forecasting skills during extreme weather events: A case study for an urban watershed

Integrated forecasting systems for precipitation, streamflow, and floodplain inundation are of critical importance in mitigating the impacts of destructive floods caused by extreme weather events. However, the skills of streamflow and floodplain inundation forecasts derived from various Quantitative Precipitation Forecasts (QPF) require a greater level of understanding. In this paper, a set of QPF developed by the National Weather Service (NWS) were used to drive a flood modeling system obtained utilizing offline coupling of a physics-based distributed hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM), and a hydrodynamic model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). This flood modeling system was used to produce forecasts of streamflow and floodplain inundation maps during three major flood events in the Brays Bayou Watershed (Houston, Texas, USA) for a range of QPF durations (6–72 h). Then, to investigate the effects of increasing QPF durations on the forecasts, the forecasting skills of precipitation, streamflow, and floodplain inundation were quantified. The results show that: 1) QPF skills for more intense and sustained events such as hurricanes and tropical storms are higher than for shorter, less intense events; 2) while QPF and streamflow forecasting skills decrease as QPF durations increase, inundation forecasts under longer QPF durations (24 or 72 h) show higher skills; 3) extending the maximum QPF duration in operational hydrologic modeling from 24 h (under normal circumstances) to 72 h (for extreme events) may increase the skills of long lead time forecasts for large-scale events like Hurricane Harvey.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The root-zone soil moisture spectrum in a mediterranean ecosystem

Storage of water within soil pores of the root zone introduce memory effects in the dynamics of soil moisture that are considerably longer than the integral timescale of many atmospheric processes. Thus, hydro-climatic states can be “sustained” through land-surface heat and water vapor fluxes primarily because they can “feed off” on this long-term soil moisture memory. Root-zone soil moisture memory is only but one feature characterizing the spectrum of soil moisture dynamics, which is analyzed here using a combination of long-term measurements and models. In particular, the spectrum of root-zone soil moisture content in a Mediterranean ecosystem is examined using 14-years of half-hourly measurements. A distinguishing hydro-climatic feature in such ecosystems is that sources (mainly rainfall) and sinks (mainly evapotranspiration) of soil moisture are roughly out of phase with each other. For over 4 decades of time scales and 7 decades of energy, the canonical shape of the measured soil moisture spectrum is shown to be approximately Lorentzian determined by the soil moisture variance and its memory but with two exceptions: the occurrences of a peak at diurnal-to-daily time scales and a weaker peak at near annual time scales. Model calculations and spectral analysis demonstrate that diurnal and seasonal variations in hydroclimate forcing responsible for variability in evapotranspiration had minor impact on the normalized shape of the soil moisture spectrum. However, their impact was captured by adjustments in the temporal variance. These findings indicate that precipitation and not evapotranspiration variability dominates the multi-scaling properties of soil moisture variability consistent with prior climate model simulations. Furthermore, the soil moisture memory inferred by the annual peak of soil moisture (340 d) is consistent with climate model simulations, while the memory evaluated from the loss function of a linearized mass balance approach leads to a smaller value (50 d), highlighting the effect of weak non-stationarity on soil moisture variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2020

Efficient SF 6 /N 2 separation at high pressures using a zirconium-based mesoporous metal–organic framework

Adsorptive separation of SF 6 /N 2 mixtures is an important issue since SF 6 is a significant greenhouse gas. Although separation performances should be evaluated under high pressure conditions to find an efficient adsorbent candidate for pressure swing adsorption (PSA) processes, no studies on metal–organic frameworks (MOFs) have focused on SF 6 /N 2 separation at high pressures above 1 bar. In this study, we evaluated the potential of three hydrothermally stable adsorbents, including two MOFs (UiO-66 and UiO-67) and zeolite-13X, for SF 6 /N 2 separation under normal PSA operation pressures (~10 bar). Interestingly, UiO-67 with a high surface area and large pore size exhibited very high SF6/N2 selectivity (30~37) at 10 bar as well as a significantly large SF6 working capacity (5.94 mmol g-1) between 10 bar and 1 bar. Moreover, UiO-67 showed facile regeneration at the ambient temperature as well as good cyclic adsorption and desorption behavior during 20 cycles. These results show that UiO-67 is a potential adsorbent for adsorptive SF 6 /N 2 separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bioinspired oxidation of benzyl alcohol: The role of environment and nuclearity of the catalyst evaluated by multivariate analysis

Inspired by copper-containing enzymes such as galactose oxidase and catechol oxidase, in which distinct coordination environments and nuclearities lead to specific catalytic activities, we summarize here the catalytic properties of dinuclear and mononuclear copper species towards benzyl alcohol oxidation using a multivariate statistical approach. Here, the new dinuclear [Cu 2 (μ-L 1 )(μ-pz)] 2+ (1) is compared against the mononuclear [CuL 2 Cl] (2), where (L 1 ) - and (L 2 ) - are the respective deprotonated forms of 2,6-bis((bis(pyridin-2-ylmethyl)amino)methyl)-4-methylphenol, and 3-((bis(pyridin-2-ylmethyl)amino)methyl)-2-hydroxy-5-methylbenzaldehyde and (pz) - is a pyrazolato bridge. Copper(II) perchlorate (CP) is used as control. The catalytic oxidation of benzyl alcohol is pursued, aiming to assess the role of the ligand environment and nuclearity. The multivariate statistical approach allows for the search of optimal catalytic conditions, considering variables such as catalyst load, hydrogen peroxide load, and time. Species 1, 2 and CP promoted selective production of benzaldehyde at different yields, with only negligible amounts of benzoic acid. Under normalized conditions, 2 showed superior catalytic activity. This species is 3.5-fold more active than the monometallic control CP, and points out to the need for an efficient ligand framework. Species 2 is 6-fold more active than the dinuclear 1, and indicates the favored nuclearity for the conversion of alcohols into aldehydes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Apolipoprotein A5 reduces clearance of VLDL by altering apolipoprotein E content

Apolipoprotein A-V (APOA5) is a critical regulator of circulating triglyceride (TG) levels. Its deletion leads to elevated plasma TG concentrations by altering the metabolism of very low-density lipoprotein (VLDL) particles in vivo. One way APOA5 exerts its effects is through modulation of lipoprotein lipase (LPL) activity, specifically by disrupting inhibitory interactions between LPL and angiopoietin-like proteins (ANGPTLs). However, the impact of APOA5 on VLDL composition and its potential to alter VLDL metabolism in other ways remains poorly understood. To address this, we investigated the influence of APOA5 on the VLDL proteome, LPL activation, and hepatic remnant uptake. Using VLDL from Apoa5 knockout (KO) and wild-type (WT) mice, we found no evidence that APOA5 directly enhances LPL activity in purified or plasma systems. However, VLDL from Apoa5 K mice was cleared significantly more slowly by cultured hepatocytes. Proteomics experiments from two independent laboratories identified consistent depletion of 17 proteins involved in lipoprotein metabolism, inflammation, and immune response in Apoa5 KO VLDL, including APOE and serum amyloid A1 (SAA1). Remarkably, reintroduction of recombinant mouse APOA5 to the KO plasma partially restored the WT VLDL proteome, including APOE, and normalized VLDL uptake by hepatocytes without altering LPL lipolysis. These findings reveal that APOA5 influences hepatic clearance of VLDL remnants by modulating particle composition, particularly APOE content. This study expands the functional scope of APOA5 in TG metabolism and underscores its role in VLDL remodeling and remnant clearance, offering new insights with implications for understanding hypertriglyceridemia and its roles in inflammation and immune response.

LPL↗
DOE OSTI2025

The congenital APOA1 K107del mutation disrupts the lipid-free conformation of monomeric APOA1 and impairs oligomerization

Apolipoprotein A-I (APOA1) oligomerization is thought to be essential for high-density lipoprotein (HDL) formation and metabolism. Naturally occurring mutations can disrupt normal APOA1 folding and self-association, leading to dysfunctional HDL formation and cardiovascular disease. The congenital APOA1 variant p.K131del (APOA1 K107del ) has been associated with cardiovascular pathologies such as low HDL-cholesterol levels and aortic amyloidosis, and multiple studies indicate structural changes in APOA1 conformation underlie associated dysfunction. In the current study, we confirmed that APOA1 K107del exhibits no notable defect in lipid-binding. However, using polyacrylamide gel electrophoresis (PAGE) and size-exclusion chromatography (SEC), we found that loss of lysine 107 resulted in a remarkable shift in the distribution of APOA1 oligomers with a much higher proportion of monomers present in APOA1 K107del compared to wild-type APOA1. Further investigation using quantitative cross-linking revealed a major disruption of interactions in helical regions reported to participate in domain swaps necessary for proper self-association. This structural disruption appears to impair N- and C-termini interactions and dynamics that lead to non-specific aggregation. In conclusion, these findings support the hypothesis that lysine 107 is critical for proper folding and self-association of lipid-free APOA1 which could impact HDL biogenesis.

APOA1 (p.K131del)↗
DOE OSTI2021

The impact of 2 H 9/2 → 4 I 13/2 emission from Er 3+ ions on ratiometric optical temperature sensing with Yb 3+ /Er 3+ co-doped upconversion materials

Yb 3+ /Er 3+ co-doped upconversion materials are widely used for luminescence intensity ratio (LIR) thermometry, where the relative intensity ratio of the green luminescence transitions ( 2 H 11/2 4 I 15/2 and 4 S 3/2 4 I 15/2 ) of Er 3+ dopant ions changes with temperature. In this work we report on the impact of an additional transition from the 2 H 9/2 level to the intermediate 4 I 13/2 level, which overlaps with the green luminescence normally used for LIR thermometry. The 2 H 9/2 4 I 13/2 emission overlaps extensively with the 4 S 3/2 4 I 15/2 emission and is more sensitive to pump power. The wavelength intervals used to integrate both 2 H 11/2 4 I 15/2 and 4 S 3/2 4 I 15/2 luminescence need be selected carefully in order to achieve accurate temperature readouts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Loading direction dependence of asymmetric response of pyramidal slip in rolled AZ31 magnesium alloy

Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes. This work focuses on the pyramidal slip responses of rolled AZ31 magnesium alloy under two loading conditions, compressive and tensile loading along the normal direction. Under the condition where the compressive loading direction is closely parallel to the c-axis of the unit cell, tensile twinning and basal slips are prohibited, dislocations then active and tend to accumulate at grain boundaries and form dislocation walls. Meanwhile, these dislocations exhibit zigzag morphologies, which result from the cross-slip from {$10\bar{1}1$} first-order pyramidal plane to {$11\bar{2}2$} second-order pyramidal plane, then back to {$10\bar{1}1$} first-order pyramidal plane. Under the condition where tensile twins are prevalent, {$10\bar{1}1$} first-order and {$11\bar{2}2$} second-order pyramidal dislocations are favorable to be activated. Both types of dislocations behave climb-like dissociations onto the basal plane, forming zigzag dislocations. first-order pyramidal plane to second-order pyramidal plane, then back to first-order pyramidal plane. Under the condition where tensile twins are prevalent, first-order and second-order pyramidal dislocations are favorable to be activated. Both types of dislocations behave climb-like dissociations onto the basal plane, forming zigzag dislocations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2026

Correlation between microstructure and residual stress formation in friction stir welded armor steels characterized by neutron diffraction

Friction stir welding (FSW) is a solid-state joining process that minimizes the heat-affected zone (HAZ) compared with fusion-based arc welding, making it well suited for joining martensitic armor steels where hardness and ballistic resistance are critical. This study investigates residual stress formation in three defect-free FSW butt-joint configurations relevant to armored-vehicle fabrication: similar rolled homogeneous armor (RHA–RHA, Case 1), similar high-hardness armor (HHA–HHA, Case 2), and dissimilar HHA–RHA (Case 3) joints produced under temperature-controlled conditions (770 °C). Neutron diffraction was employed to quantify the magnitude and spatial distribution of residual stresses in the longitudinal, transverse, and normal directions and to correlate them with weld microstructure and hardness. Tensile residual stresses were concentrated in the softened HAZ, reaching approximately 300 MPa for Case 2 and 400 MPa for Case 1 (≈50–70 % of the base-metal yield strength; ∼581 MPa for RHA and ∼566 MPa for HHA), while compressive residual stresses dominated the stir zone. The spatial extent of tensile stresses scaled with the width of the softened HAZ, which was largest in the dissimilar HHA–RHA joint and smallest in the HHA–HHA joint. Full-width-at-half-maximum (FWHM) analysis revealed low microstrain in overtempered HAZ regions and high microstrain in the stir zone associated with severe plastic deformation and fresh martensite formation. This work demonstrates that residual stress evolution in FSW of martensitic armor steels is governed not primarily by peak temperature or thermal contraction, as inferred from fusion-welding analogies, but by the competition between transformation-induced volumetric expansion and tempering-induced stress relaxation. The relative dominance of these mechanisms is shown to depend on alloy hardenability and local thermal history, leading to more extensive HAZ softening and broader tensile stress regions in the lower-hardenability RHA steel. These findings establish a transferable mechanistic framework for optimizing solid-state joining strategies in high-strength steels and other transformation-hardening alloys beyond armor applications.

Friction stir welding↗
DOE OSTI2021

Spin-dependent Seebeck and Nernst effects in an ideal skyrmion gas

In this work, we theoretically and numerically study spin-dependent Seebeck and Nernst effects in 2D ferromagnetic materials with the topological spin texture (skyrmion and vortex) ideal gas. From the numerical solution of the matrix Boltzmann equation for a nonequilibrium distribution function and the Lippmann–Schwinger equation for a T-matrix we find the strong nonlinear behaviors in the thermoelectric coefficients depending on skyrmion/vortex diameters and electron concentrations. In particular, the dramatic dependences in the Seebeck and Nernst coefficients take place at larger magnetic texture sizes where the abrupt sign flip in the vortex Seebeck and Nernst coefficients occurs in the narrow region of electron concentrations. In this case the normalized Nernst coefficient changes from +5 to -7. The spin-dependent thermoelectric coefficients are proportional to T at low temperatures for all skyrmion/vortex sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Suhl instabilities in nanoscopic spheroids

We simulate the magnetization dynamics of a permalloy spheroid of nanoscopic size in zero external field, such that both dipolar and exchange interactions are important. Low excitation power is used to obtain the frequencies and mode patterns of many normal modes. At higher power, non-linear three and four mode couplings between magnons carrying orbital angular momentum are observed to give rise to Suhl instabilities. Suhl’s analysis of the selection rules governing the allowed processes is extended to initial states other than uniform precession. These rules are studied and confirmed by the simulations. Both down- and up-conversion are seen as well as three and four-mode processes. As a result, general trends are inferred for preferred instabilities among those that are allowed, although the thresholds for some instabilities appear to be very high.

3D nanomagnetism↗
DOE OSTI2022

Crystal plasticity including a phase-field deformation twinning model for the high-rate deformation of cyclotetramethylene tetranitramine

A new finite strain thermomechanical model for the high-rate deformation of the β-polymorph of cyclotetramethylene tetranitramine (β-HMX) has been developed and applied to simulations of plate impact experiments. The crystal plasticity model is based on a model developed previously for RDX (Luscher et al. 2017), which is extended to incorporate deformation twinning. Twinning during normal plate impacts is simulated with a phase-field twin model. First, material parameters governing the kinetics of dislocation slip are calibrated on the subset of simulations which had negative Schmid factors for the twin system. Second, a parametric study of the twin material parameters was performed to find suitable values. Overall, the results of the simulations with the phase-field twinning model are reported for impacts on several crystal orientations. We find that the twin growth decreases with increasing distance from the impact surface because of dissipation of the shock front via dislocation-mediated plasticity, and that the simulated interface velocity with and without phase-field twinning do not show appreciable differences. These modeling results suggest that the significance of twinning in β-HMX cannot be determined with traditional loading configurations and diagnostics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. Here, in this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.

Machine learning↗
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↗