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DOE OSTI2022

A Square Pulse Thermoreflectance Technique for the Measurement of Thermal Properties

We report on a laser-based square pulse thermoreflectance (SPTR) technique for the measurement of thermal properties for a wide range of materials. SPTR adopts the pump-probe thermoreflectance principle to monitor the evolution of local temperature after square pulse excitation. The technique features a compact setup, high spatial resolution, and fast data collection. By comparing the acquired SPTR signals with a continuum heat transfer model, material thermal properties can be obtained. Taking advantage of various spot sizes and modulation frequencies, SPTR can measure both the thermal diffusivity and thermal conductivity of poorly to moderately conductive materials and the thermal conductivity of conductive materials with satisfactory accuracy, with potential to be applied to more conductive materials. The technique was validated on three materials: fused silica, single crystal CaF2 and single crystal nickel (with conductivities ranging from 1 W·m -1 ·K -1 to 100 W·m -1 ·K -1 ) with typical measurement errors of 5 % to 20 %. The leading sources of error have been identified by Monte Carlo simulations, and the primary limitations of SPTR are discussed. The compact, fiberized platform we describe here will allow instruments based on this methodology to be deployed in complex, multi-analytical environments for the type of high-throughput correlative analyses that are key to materials design and discovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Multi-wavelength Brillouin erbium-doped fiber laser sensor with high tunable temperature sensing coefficient

A multi-wavelength Brillouin erbium-doped fiber laser sensor is proposed and investigated experimentally. It is a linear laser cavity multi-wavelength fiber laser, formed by fixing two high-reflectivity Sangac loop mirrors at both ends. We investigate the laser system output characteristics and test the sensor performance for different temperature. Temperature/humidity and reliable linear-cavity fiber laser sensing operation were successfully achieved. Experimental results show that the temperature sensitivity is 27.15 MHz/ °C and the measurement error caused by frequency drift is about ±0.285 °C.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Microsegregation Measurement: Methods and Applications

Microsegregation is chemical variation at the microstructural level in a solidified ingot, which can cause undesirable effects on material properties. Its formation is driven by different levels of solute elemental solubility between solid and liquid phases, resulting in solute buildup or depletion in the liquid phase during solidification. Characterization of microsegregation can be essential for understanding its formation and removing it through heat treatment. Measurement is frequently carried out via scanning electron microscope (SEM), though metallographic analysis, mechanical testing and optical radiography have been used. SEM analysis can consist of point measurements on single dendrites, line scans across or along dendrites, elemental maps, and extended line-based or grid-based analyses extending over many dendrites, which can generate large amounts of data. Line-based or grid-based data can be analyzed using scatterplots of element concentrations to visualize segregation behavior and sorting/ranking of data by a single element or a combination of elements, to give compositional profiles as a function of extent of solidification. For both qualitative and quantitative analyses, effects of phase transformations and secondary phases need to be considered as well as sorting of measurement errors when generating compositional profiles. Applications of analysis include determination of partition ratios, examination of solute–element interactions, determination of effects of cooling rate and back diffusion, applications to freckling studies, validations of modeling, and development of heat-treatment profiles.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Investigating the mechanisms responsible for the lack of surface energy balance closure in a central Amazonian tropical rainforest

This work investigates the diurnal and seasonal behavior of the energy balance residual (E) that results from the observed difference between available energy and the turbulent fluxes of sensible heat (H) and latent heat (LE) at the FLUXNET BR-Ma2 site located in the Brazilian central Amazon rainforest. The behavior of E is analyzed by extending the eddy covariance averaging length from 30 min to 4 h and by applying an Information Flow Dynamical Process Network to diagnose processes and conditions affecting E across different seasons. Results show that the seasonal turbulent flux dynamics and the Bowen ratio are primarily driven by net radiation (R n ), with substantial sub-seasonal variability. The Bowen ratio increased from 0.25 in April to 0.4 at the end of September. Extension of the averaging length from 0.5 (94.6% closure) to 4 h and thus inclusion of longer timescale eddies and mesoscale processes closes the energy balance and lead to an increase in the Bowen ratio, thus highlighting the importance of additional H to E. Information flow analysis reveals that the components of the energy balance explain between 25 and 40% of the total Shannon entropy with higher values during the wet season than the dry season. Dry season information flow from the buoyancy flux to E are 30–50% larger than that from H, indicating the potential importance of buoyancy fluxes to closing E. While the low closure highlights additional sources not captured in the flux data and random measurement errors contributing to E, the findings of the information flow and averaging length analysis are consistent with the impact of mesoscale circulations, which tend to transport more H than LE, on the lack of closure.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

On the surface energy balance closure at different temporal scales

Measurements of the surface energy fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made in the Columbia River Basin (Oregon) in an area of complex terrain during a 10-month long portion of the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to study the surface energy budget (SEB) and surface fluxes over different temporal scales. This study analyzes and discusses SEB closure based on half-hourly, daily, monthly, seasonal, and sub-annual (~10-month) temporal averages. The data were collected over all four seasons for different states of the underlying ground surface (dry, wet, and frozen). Our half-hourly direct measurements of energy balance show that the sum of the turbulent sensible and latent heat fluxes systematically underestimate positive net radiation by around 20–30% during daytime and overestimate negative net radiation at night. This imbalance of the surface energy budget is comparable to other terrestrial sites. However, on average, the residual energy imbalance is significantly reduced at daily, weekly, and monthly averaging timescales, and moreover, the SEB can be closed for this site within reasonable limits on seasonal and sub-annual timescales (311-day averaging for the entire field campaign dataset). Increasing the averaging time to daily and longer time intervals substantially reduces the ground heat flux and storage terms, because energy locally entering the soil, air column, and vegetation in the morning is released in the afternoon and evening. Averaging on daily to sub-annual timescales also reduces random instrumental measurement errors and other uncertainties as well as smooths out a hysteresis effect (phase lag) in the SEB relationship between different components. This study shows that SEB closure is better for dry soils compared to wet soils and the statistical dependence of the turbulent fluxes and net radiation for freezing soil surfaces appears weak, if not non-existent, apparently due to lack of the latent heat of fusion term in the traditional SEB equation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

A multi-objective optimization approach to optimal sensor location problem in $\mathrm{IGCC}$ power plants

Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. Here this work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at solving the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Operator inference with roll outs for learning reduced models from scarce and low-quality data

Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, often polluted with noise and affected by measurement errors and other perturbations, which makes learning the dynamics of systems challenging. Here, in this work, we propose to combine data-driven modeling via operator inference with the dynamic training via roll outs of neural ordinary differential equations. Operator inference with roll outs inherits interpretability, scalability, and structure preservation of traditional operator inference while leveraging the dynamic training via roll outs over multiple time steps to increase stability and robustness for learning from low-quality and noisy data. Numerical experiments with data describing shallow water waves and surface quasi-geostrophic dynamics demonstrate that operator inference with roll outs provides predictive models from training trajectories even if data are sampled sparsely in time and polluted with noise of up to 10%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Diffuse back-illumination temperature imaging (DBI-TI), a novel soot thermometry technique

To meet stringent emissions regulations on soot emissions, it is critical to further advance the fundamental understanding of in-cylinder soot formation and oxidation processes. Among several optical techniques for soot quantification, diffuse back-illumination extinction imaging (DBI-EI) has recently gained traction mainly due to its ability to compensate for beam steering, which if not addressed, can cause unacceptably high measurement uncertainty. Until now, DBI-EI has only been used to measure the amount of soot along the line of sight, and here, we extend the capabilities of a DBI-EI setup to also measure in-cylinder soot temperature. This proof of concept of diffuse back-illumination temperature imaging (DBI-TI) as a soot thermometry technique is presented by implementing DBI-TI in a single cylinder, heavy-duty, optical diesel engine to provide 2-D line-of-sight integrated soot temperature maps. The potential of DBI-TI to be an accurate thermometry technique for use in optical engines is analyzed. The achievable accuracy is due in part to simultaneous measurement of the soot extinction, which circumvents the uncertainty in dispersion coefficients that depend on the optical properties of soot and the wavelength of light utilized. Analysis shows that DBI-TI provides temperature estimates that are closer to the mass-averaged soot temperature when compared to other thermometry techniques that are more sensitive to soot temperature closer to the detector. Furthermore, uncertainty analysis and Monte Carlo (MC) simulations provide estimates of the temperature measurement errors associated with this technique. The MC simulations reveal that for the light intensities and optical densities encountered in these experiments, the accuracy of the DBI-TI technique is comparable or even better than other established optical thermometry techniques. Thus, DBI-TI promises to be an easily implementable extension to the existing DBI-EI technique, thereby extending its ability to provide comprehensive line-of-sight integrated information on soot.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo

Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty; which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature. In this paper, a flexible Bayesian approach to functional alignment is presented, which appropriately accounts for noise in the data without any pre-smoothing required. Additionally, by running parallel MCMC chains, the method can account for multiple optimal alignments via the multi-modal posterior distribution of the warping functions. To most efficiently sample the warping functions, the approach relies on a modification of the standard Hamiltonian Monte Carlo to be well-defined on the infinite-dimensional Hilbert space. In this work, this flexible Bayesian alignment method is applied to both simulated data and real data sets to show its efficiency in handling noisy functions and successfully accounting for multiple optimal alignments in the posterior; characterizing the uncertainty surrounding the warping functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Highlights: • A new hybrid approach is proposed to improve forecasts of hydro-meteorological time series. • Rank-Set Pair Analysis combined with wavelet de-noising markedly improves forecasting accuracy. • The performance of the proposed approach proves best among its present competitors even when the extreme value occurs. Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

58 GEOSCIENCES↗

DOE OSTI2018

Residential mobility and childhood leukemia

Highlights: • Children who moved were older, had younger mothers, and lower SES. • Non-movers showed stronger associations with EMF exposures and childhood leukemia. • Adjustment for variables predicting mobility, save dwelling, did not alter results. • Mobility does not appear to explain observed links between EMF and leukemia. Studies of environmental exposures and childhood leukemia studies do not usually account for residential mobility. Yet, in addition to being a potential risk factor, mobility can induce selection bias, confounding, or measurement error in such studies. Using data collected for California Powerline Study (CAPS), we attempt to disentangle the effect of mobility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Demonstration of repeatability in a high-energy-density planar shear mixing layer experiment

On laser-driven platforms the assumption of experiment repeatability is particularly important due to a typically low data acquisition rate that doesn’t often allow for data redundancy. If the platform is repeatable, then measurements of the repeatable dynamics from multiple experiments can be treated as measurements of the same system. In high-energy-density hydrodynamic instability experiments the interface growth is assumed to be one of the repeatable aspects of the system. In this paper we demonstrate the repeatability of the instability growth in the counter-propagating shear experiment at the OMEGA laser facility, where the instability growth is characterized by the tracer layer thickness or mix-width evolution. Furthermore, in our previous experiment campaigns we have assumed the instability growth was repeatable enough to identify trends, but in this work we explicitly show that the mix-width measurements for nominally identical experiments are repeatable within the measurement error bars.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring

The presence of faults/ fractures or highly permeable zones in the primary sealing caprock of a CO2 storage reservoir can result in leakage of CO2. Monitoring of leakage requires the capability to detect and resolve the onset, location, and volume of leakage in a systematic and timely manner. Pressure-based monitoring possesses such capabilities. This study demonstrates a basis for monitoring network design based on the characterization of CO2 leakage scenarios through an assessment of the integrity and permeability of the caprock inferred from above zone pressure measurements. Four representative heterogeneous fractured seal types are characterized to demonstrate seal permeability ranging from highly permeable to impermeable. Based on Bayesian classification theory, the probability of each fractured caprock scenario given above zone pressure measurements with measurement error is inferred. The sensitivity to injection rate and caprock thickness is also evaluated and the probability of proper classification is calculated. The time required to distinguish between above zone pressure outcomes and the associated leakage scenarios is also computed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Magnetic Resonance Imaging Assessment of Spinal Cord and Cauda Equina Motion in Supine Patients With Spinal Metastases Planned for Spine Stereotactic Body Radiation Therapy

Purpose: To assess motion of the spinal cord and cauda equina, which are critical neural tissues (CNT), which is important when evaluating the planning organ-at-risk margin required for stereotactic body radiation therapy. Methods and Materials: We analyzed CNT motion in 65 patients with spinal metastases (11 cervical, 39 thoracic, and 24 lumbar spinal segments) in the supine position using dynamic axial and sagittal magnetic resonance imaging (dMRI, 3T Verio, Siemens) over a 137-second interval. Motion was segregated according to physiologic cardiorespiratory oscillatory motion (characterized by the average root mean square deviation) and random bulk shifts associated with gross patient motion (characterized by the range). Displacement was evaluated in the anteroposterior (AP), lateral (LR), and superior-inferior (SI) directions by use of a correlation coefficient template matching algorithm, with quantification of random motion measure error over 3 separate trials. Statistical significance was defined according to P<.05. Results: In the AP, LR, and SI directions, significant oscillatory motion was observed in 39.2%, 35.1%, and 10.8% of spinal segments, respectively, and significant bulk motions in all cases. The median oscillatory CNT motions in the AP, LR, and SI directions were 0.16 mm, 0.17 mm, and 0.44 mm, respectively, and the maximal statistically significant oscillatory motions were 0.39 mm, 0.41 mm, and 0.77 mm, respectively. The median bulk displacements in the AP, LR, and SI directions were 0.51 mm, 0.59 mm, and 0.66 mm, and the maximal statistically significant displacements were 2.21 mm, 2.87 mm, and 3.90 mm, respectively. In the AP, LR, and SI directions, bulk displacements were greater than 1.5 mm in 5.4%, 9.0%, and 14.9% of spinal segments, respectively. No significant differences in axial motion were observed according to cord level or cauda equina. Conclusions: Oscillatory CNT motion was observed to be relatively minor. Our results support the importance of controlling bulk patient motion and the practice of applying a planning organ-at-risk margin.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Results From the Imaging and Radiation Oncology Core Houston's Anthropomorphic Phantoms Used for Proton Therapy Clinical Trial Credentialing

Purpose: The purpose of this study was to summarize the findings of anthropomorphic proton phantom irradiations analyzed by the Imaging and Radiation Oncology Core Houston QA Center (IROC Houston). Methods and Materials: A total of 103 phantoms were irradiated by proton therapy centers participating in clinical trials. The anthropomorphic phantoms simulated heterogeneous anatomy of a head, liver, lung, prostate, and spine. Treatment plans included those for scattered, uniform scanning, and pencil beam scanning beam delivery modalities using 5 different treatment planning systems. For every phantom irradiation, point doses and planar doses were measured using thermoluminescent dosimeters (TLD) and film, respectively. Differences between measured and planned doses were studied as a function of phantom, beam delivery modality, motion, repeat attempt, treatment planning system, and date of irradiation. Results: The phantom pass rate (overall, 79%) was high for simple phantoms and lower for phantoms that introduced higher levels of difficulty, such as motion, multiple targets, or increased heterogeneity. All treatment planning systems overestimated dose to the target, compared to TLD measurements. Errors in range calculation resulted in several failed phantoms. There was no correlation between treatment planning system and pass rate. The pass rates for each individual phantom are not improving over time, but when individual institutions received feedback about failed phantom irradiations, pass rates did improve. Conclusions: The proton phantom pass rates are not as high as desired and emphasize potential deficiencies in proton therapy planning and/or delivery. There are many areas for improvement with the proton phantom irradiations, such as treatment planning system dose agreement, range calculations, accounting for motion, and irradiation of multiple targets.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Enforcing positivity in intrusive PC-UQ methods for reactive ODE systems

We explore the relation between the development of a non-negligible probability of negative states and the instability of numerical integration of the intrusive Galerkin ordinary differential equation system describing uncertain chemical ignition. To prevent this instability without resorting to either multi-element local polynomial chaos (PC) methods or increasing the order of the PC representation in time, we propose a procedure aimed at modifying the amplitude of the PC modes to bring the probability of negative state values below a user-defined threshold. This modification can be effectively described as a filtering procedure of the spectral PC coefficients, which is applied on-the-fly during the numerical integration when the current value of the probability of negative states exceeds the prescribed threshold. We demonstrate the filtering procedure using a simple model of an ignition process in a batch reactor. This is carried out by comparing different observables and error measures as obtained by non-intrusive Monte Carlo and Gauss-quadrature integration and the filtered intrusive procedure. The filtering procedure has been shown to effectively stabilize divergent intrusive solutions, and also to improve the accuracy of stable intrusive solutions which are close to the stability limits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗