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DOE OSTI2016

The Reducibility of Reactor Physics Depletion and Decay Calculation

Depletion and decay calculations are integral part of reactor physics calculations, necessary to estimate fuel characteristics following a given exposure to radiation. Given the complexity of reactor design and the unprecedented sophistication of the new high fidelity modeling and simulation tools, attempting to calculate a very accurate estimation of fuel characteristics on a pin-by-pin level, there is an ever-increasing need to perform these calculations in a computationally efficient manner. This summary presents a new enabling methodology to achieve higher computational gains while addressing one of the fundamental challenges of existing methods, that is how to preserve the accuracy of the original high fidelity models. This is difficult with current approaches relying on look-up tables or the elimination of non-influential isotopes. To achieve that we employ a reduced order modeling approach which reduces the effective dimensionality of the solution space, represented by the number of tracked nuclides. This approach allows the analyst to devise a user-defined level of reduction supported by rigorous error bounds that quantify the accuracy of the reduced model. This summary explores this new methodology by calculating the effective dimensionality for representative BWR depletion problems that are required to meet user-defined accuracy for the nuclides concentrations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2013

Accuracy of Real-time Couch Tracking During 3-dimensional Conformal Radiation Therapy, Intensity Modulated Radiation Therapy, and Volumetric Modulated Arc Therapy for Prostate Cancer

Purpose: To evaluate the accuracy of real-time couch tracking for prostate cancer. Methods and Materials: Intrafractional motion trajectories of 15 prostate cancer patients were the basis for this phantom study; prostate motion had been monitored with the Calypso System. An industrial robot moved a phantom along these trajectories, motion was detected via an infrared camera system, and the robotic HexaPOD couch was used for real-time counter-steering. Residual phantom motion during real-time tracking was measured with the infrared camera system. Film dosimetry was performed during delivery of 3-dimensional conformal radiation therapy (3D-CRT), step-and-shoot intensity modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Results: Motion of the prostate was largest in the anterior-posterior direction, with systematic ( N-Ary-Summation ) and random ({sigma}) errors of 2.3 mm and 2.9 mm, respectively; the prostate was outside a threshold of 5 mm (3D vector) for 25.0%{+-}19.8% of treatment time. Real-time tracking reduced prostate motion to N-Ary-Summation =0.01 mm and {sigma} = 0.55 mm in the anterior-posterior direction; the prostate remained within a 1-mm and 5-mm threshold for 93.9%{+-}4.6% and 99.7%{+-}0.4% of the time, respectively. Without real-time tracking, pass rates based on a {gamma} index of 2%/2 mm in film dosimetry ranged between 66% and 72% for 3D-CRT, IMRT, and VMAT, on average. Real-time tracking increased pass rates to minimum 98% on average for 3D-CRT, IMRT, and VMAT. Conclusions: Real-time couch tracking resulted in submillimeter accuracy for prostate cancer, which transferred into high dosimetric accuracy independently of whether 3D-CRT, IMRT, or VMAT was used.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

TU-AB-202-07: A Novel Method for Registration of Mid-Treatment PET/CT Images Under Conditions of Tumor Regression for Patients with Locally Advanced Lung Cancers

Purpose: In PET-guided adaptive radiotherapy (RT), changes in the metabolic activity at individual voxels cannot be derived until the duringtreatment CT images are appropriately registered to pre-treatment CT images. However, deformable image registration (DIR) usually does not preserve tumor volume. This may induce errors when comparing to the target. The aim of this study was to develop a DIR-integrated mechanical modeling technique to track radiation-induced metabolic changes on PET images. Methods: Three patients with non-small cell lung cancer (NSCLC) were treated with adaptive radiotherapy under RTOG 1106. Two PET/CT image sets were acquired 2 weeks before RT and 18 fractions after the start of treatment. DIR was performed to register the during-RT CT to the pre-RT CT using a B-spline algorithm and the resultant displacements in the region of tumor were remodeled using a hybrid finite element method (FEM). Gross tumor volume (GTV) was delineated on the during-RT PET/CT image sets and deformed using the 3D deformation vector fields generated by the CT-based registrations. Metabolic tumor volume (MTV) was calculated using the pre- and during–RT image set. The quality of the PET mapping was evaluated based on the constancy of the mapped MTV and landmark comparison. Results: The B-spline-based registrations changed MTVs by 7.3%, 4.6% and −5.9% for the 3 patients and the correspondent changes for the hybrid FEM method −2.9%, 1% and 6.3%, respectively. Landmark comparisons were used to evaluate the Rigid, B-Spline, and hybrid FEM registrations with the mean errors of 10.1 ± 1.6 mm, 4.4 ± 0.4 mm, and 3.6 ± 0.4 mm for three patients. The hybrid FEM method outperforms the B-Spline-only registration for patients with tumor regression Conclusion: The hybrid FEM modeling technique improves the B-Spline registrations in tumor regions. This technique may help compare metabolic activities between two PET/CT images with regressing tumors. The author gratefully acknowledges the financial support from the National Institutes of Health Grant.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Toward in vivo lung's tissue incompressibility characterization for tumor motion modeling in radiation therapy

Purpose: A novel technique is proposed to characterize lung tissue incompressibility variation during respiration. Estimating lung tissue incompressibility parameter variations resulting from air content variation throughout respiration is critical for computer assisted tumor motion tracking. Continuous tumor motion is a major challenge in lung cancer radiotherapy, especially with external beam radiotherapy. If not accounted for, this motion may lead to areas of radiation overdosage for normal tissue. Given the unavailability of imaging modality that can be used effectively for real-time lung tumor tracking, computer assisted approach based on tissue deformation estimation can be a good alternative. This approach involves lung biomechanical model where its fidelity depends on input tissue properties. This investigation shows that considering variable tissue incompressibility parameter is very important for predicting tumor motion accurately, hence improving the lung radiotherapy outcome. Methods: First, an in silico lung phantom study was conducted to demonstrate the importance of employing variable Poisson's ratio for tumor motion predication. After it was established that modeling this variability is critical for accurate tumor motion prediction, an optimization based technique was developed to estimate lung tissue Poisson's ratio as a function of respiration cycle time. In this technique, the Poisson's ratio and lung pressure value were varied systematically until optimal values were obtained, leading to maximum similarity between acquired and simulated 4D CT lung images. This technique was applied in an ex vivo porcine lung study where simulated images were constructed using the end exhale CT image and deformation fields obtained from the lung's FE modeling of each respiration time increment. To model the tissue, linear elastic and Marlow hyperelastic material models in conjunction with variable Poisson's ratio were used. Results: The phantom study showed that the tumor motion trajectory and its final locations obtained from simulations with and without considering tissue incompressibility variation were very different. For example, tumor displacements in the z direction were -11.23 and -38.10 mm obtained with the Marlow hyperelastic material model in conjunction with constant and variable Poisson's ratio, respectively. By comparing the acquired 4D-CT image sequence of the porcine lung with their image sequence counterparts obtained from the hyperelastic model with constant and variable Poisson's ratio, it was shown that using variable tissue incompressibility reduced errors significantly in tumor motion prediction. Conclusions: This investigation demonstrates the importance of incompressibility variation estimation and utilization for accurate tumor tracking in computer assisted lung external beam radiation therapy. An optimization framework was developed to estimate a Poisson's ratio function in terms of respiration cycle time using experimental image data of the lung. Utilizing this function along with respiratory system FE modeling may lead to more effective tumor targeting, hence potentially improving the outcome of lung external beam radiation therapy techniques. This is particularly true for stereotactic body radiation therapy where only one or a few fraction treatments are applied, precluding the possibility of averaging out dosimetric deviations introduced by the respiratory motion.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

\texttt{qec\_code\_sim}: An open-source Python framework for estimating the effectiveness of quantum-error correcting codes on superconducting qubits

Quantum computers are highly susceptible to errors due to unintended interactions with their environment. It is crucial to correct these errors without gaining information about the quantum state, which would result in its destruction through back-action. Quantum Error Correction (QEC) provides information about occurred errors without compromising the quantum state of the system. However, the implementation of QEC has proven to be challenging due to the current performance levels of qubits -- break-even requires fabrication and operation quality that is beyond the state-of-the-art. Understanding how qubit performance factors into the success of a QEC code is a valuable exercise for tracking progress towards fault-tolerant quantum computing. Here we present \texttt{qec\_code\_sim}, an open-source, lightweight Python framework for studying the performance of small quantum error correcting codes under the influence of a realistic error model appropriate for superconducting transmon qubits, with the goal of enabling useful hardware studies and experiments. \texttt{qec\_code\_sim} requires minimal software dependencies and prioritizes ease of use, ease of change, and pedagogy over execution speed. As such, it is a tool well-suited to small teams studying systems on the order of one dozen qubits.

Lopez, Santiago↗

DOE OSTI2016

A high-precision instrument for analyzing nonlinear dynamic behavior of bearing cage

The high-precision ball bearing is fundamental to the performance of complex mechanical systems. As the speed increases, the cage behavior becomes a key factor in influencing the bearing performance, especially life and reliability. This paper develops a high-precision instrument for analyzing nonlinear dynamic behavior of the bearing cage. The trajectory of the rotational center and non-repetitive run-out (NRRO) of the cage are used to evaluate the instability of cage motion. This instrument applied an aerostatic spindle to support and spin test the bearing to decrease the influence of system error. Then, a high-speed camera is used to capture images when the bearing works at high speeds. A 3D trajectory tracking software TEMA Motion is used to track the spot which marked the cage surface. Finally, by developing the MATLAB program, a Lissajous’ figure was used to evaluate the nonlinear dynamic behavior of the cage with different speeds. The trajectory of rotational center and NRRO of the cage with various speeds are analyzed. The results can be used to predict the initial failure and optimize cage structural parameters. In addition, the repeatability precision of instrument is also validated. In the future, the motorized spindle will be applied to increase testing speed and image processing algorithms will be developed to analyze the trajectory of the cage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

High-speed spiral imaging technique for an atomic force microscope using a linear quadratic Gaussian controller

This paper demonstrates a high-speed spiral imaging technique for an atomic force microscope (AFM). As an alternative to traditional raster scanning, an approach of gradient pulsing using a spiral line is implemented and spirals are generated by applying single-frequency cosine and sine waves of slowly varying amplitudes to the X and Y-axes of the AFM’s piezoelectric tube scanner (PTS). Due to these single-frequency sinusoidal input signals, the scanning process can be faster than that of conventional raster scanning. A linear quadratic Gaussian controller is designed to track the reference sinusoid and a vibration compensator is combined to damp the resonant mode of the PTS. An internal model of the reference sinusoidal signal is included in the plant model and an integrator for the system error is introduced in the proposed control scheme. As a result, the phase error between the input and output sinusoids from the X and Y-PTSs is reduced. The spirals produced have particularly narrow-band frequency measures which change slowly over time, thereby making it possible for the scanner to achieve improved tracking and continuous high-speed scanning rather than being restricted to the back and forth motion of raster scanning. As part of the post-processing of the experimental data, a fifth-order Butterworth filter is used to filter noises in the signals emanating from the position sensors and a Gaussian image filter is used to filter the images. A comparison of images scanned using the proposed controller (spiral) and the AFM PI controller (raster) shows improvement in the scanning rate using the proposed method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2011

Runtime Detection of C-Style Errors in UPC Code

Unified Parallel C (UPC) extends the C programming language (ISO C 99) with explicit parallel programming support for the partitioned global address space (PGAS), which provides a global memory space with localized partitions to each thread. Like its ancestor C, UPC is a low-level language that emphasizes code efficiency over safety. The absence of dynamic (and static) safety checks allows programmer oversights and software flaws that can be hard to spot. In this paper, we present an extension of a dynamic analysis tool, ROSE-Code Instrumentation and Runtime Monitor (ROSECIRM), for UPC to help programmers find C-style errors involving the global address space. Built on top of the ROSE source-to-source compiler infrastructure, the tool instruments source files with code that monitors operations and keeps track of changes to the system state. The resulting code is linked to a runtime monitor that observes the program execution and finds software defects. We describe the extensions to ROSE-CIRM that were necessary to support UPC. We discuss complications that arise from parallel code and our solutions. We test ROSE-CIRM against a runtime error detection test suite, and present performance results obtained from running error-free codes. ROSE-CIRM is released as part of the ROSE compiler under a BSD-style open source license.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Latent uncertainties of the precalculated track Monte Carlo method

Purpose: While significant progress has been made in speeding up Monte Carlo (MC) dose calculation methods, they remain too time-consuming for the purpose of inverse planning. To achieve clinically usable calculation speeds, a precalculated Monte Carlo (PMC) algorithm for proton and electron transport was developed to run on graphics processing units (GPUs). The algorithm utilizes pregenerated particle track data from conventional MC codes for different materials such as water, bone, and lung to produce dose distributions in voxelized phantoms. While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from the limited number of unique tracks in the pregenerated track bank is missing from the paper. With a proper uncertainty analysis, an optimal number of tracks in the pregenerated track bank can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pregenerated for electrons and protons using EGSnrc and GEANT4 and saved in a database. The PMC algorithm for track selection, rotation, and transport was implemented on the Compute Unified Device Architecture (CUDA) 4.0 programming framework. PMC dose distributions were calculated in a variety of media and compared to benchmark dose distributions simulated from the corresponding general-purpose MC codes in the same conditions. A latent uncertainty metric was defined and analysis was performed by varying the pregenerated track bank size and the number of simulated primary particle histories and comparing dose values to a “ground truth” benchmark dose distribution calculated to 0.04% average uncertainty in voxels with dose greater than 20% of D{sub max}. Efficiency metrics were calculated against benchmark MC codes on a single CPU core with no variance reduction. Results: Dose distributions generated using PMC and benchmark MC codes were compared and found to be within 2% of each other in voxels with dose values greater than 20% of the maximum dose. In proton calculations, a small (≤1 mm) distance-to-agreement error was observed at the Bragg peak. Latent uncertainty was characterized for electrons and found to follow a Poisson distribution with the number of unique tracks per energy. A track bank of 12 energies and 60000 unique tracks per pregenerated energy in water had a size of 2.4 GB and achieved a latent uncertainty of approximately 1% at an optimal efficiency gain over DOSXYZnrc. Larger track banks produced a lower latent uncertainty at the cost of increased memory consumption. Using an NVIDIA GTX 590, efficiency analysis showed a 807 × efficiency increase over DOSXYZnrc for 16 MeV electrons in water and 508 × for 16 MeV electrons in bone. Conclusions: The PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty of 1% with a large efficiency gain over conventional MC codes. Before performing clinical dose calculations, models to calculate dose contributions from uncharged particles must be implemented. Following the successful implementation of these models, the PMC method will be evaluated as a candidate for inverse planning of modulated electron radiation therapy and scanned proton beams.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2023

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Energy & Fuels↗

DOE OSTI2021

CSISAR--Complete System Integrated SAR

The CSISAR tool is GUI based and very simple to use. The algorithms are robust, and the unique processing flows, that the user is stepped through, virtually eliminate the possibility of error in GEOINT production. An integrated data manager is a key part of the CSISAR system. This data manager keeps track of the data available to a user and informs the user of what data is available and what can be done with that data. This keeps the user from having to be trained in the nuances of the algorithms. CSISAR also has an integrated product manager, which helps the user identify, view and manage previously made products. CSISAR was originally developed in 2010-2011 as a Windows based system. It was updated in 2015 to be a Linux based system. This SAND report is intended to make the Product Description and User’s Guide for CSISAR (originally included within the software) more widely available. New is a brief addition of Linux-specific installation details.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Stillwater Hybrid Geo-Solar Power Plant Optimization Analyses

The Stillwater Power Plant is the first hybrid plant in the world able to bring together a medium-enthalpy geothermal unit with solar thermal and solar photovoltaic systems. Solar field and power plant models have been developed to predict the performance of the Stillwater geothermal / solar-thermal hybrid power plant. The models have been validated using operational data from the Stillwater plant. A preliminary effort to optimize performance of the Stillwater hybrid plant using optical characterization of the solar field has been completed. The Stillwater solar field optical characterization involved measurement of mirror reflectance, mirror slope error, and receiver position error. The measurements indicate that the solar field may generate 9% less energy than the design value if an appropriate tracking offset is not employed. A perfect tracking offset algorithm may be able to boost the solar field performance by about 15%. The validated Stillwater hybrid plant models were used to evaluate hybrid plant operating strategies including turbine IGV position optimization, ACC fan speed and turbine IGV position optimization, turbine inlet entropy control using optimization of multiple process variables, and mixed working fluid substitution. The hybrid plant models predict that each of these operating strategies could increase net power generation relative to the baseline Stillwater hybrid plant operations.

15 GEOTHERMAL ENERGY↗

DOE OSTI2011

Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams. Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a ''reference standard'' was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours. Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 {+-} 9.7% s.d., and the average Hausdorff distance was 3.2 {+-} 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%. Conclusions: Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

Tracking stochastic resonance curves using an assisted reference model

The optimal noise amplitude for Stochastic Resonance (SR) is located employing an Artificial Neural Network (ANN) reference model with a nonlinear predictive capability. A modified Kalman Filter (KF) was coupled to this reference model in order to compensate for semi-quantitative forecast errors. Three manifestations of stochastic resonance, namely, Periodic Stochastic Resonance (PSR), Aperiodic Stochastic Resonance (ASR), and finally Coherence Resonance (CR) were considered. Using noise amplitude as the control parameter, for the case of PSR and ASR, the cross-correlation curve between the sub-threshold input signal and the system response is tracked. However, using the same parameter the Normalized Variance curve is tracked for the case of CR. The goal of the present work is to track these curves and converge to their respective extremal points. The ANN reference model strategy captures and subsequently predicts the nonlinear features of the model system while the KF compensates for the perturbations inherent to the superimposed noise. This technique, implemented in the FitzHugh-Nagumo model, enabled us to track the resonance curves and eventually locate their optimal (extremal) values. This would yield the optimal value of noise for the three manifestations of the SR phenomena.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Reducing experimental uncertainty in the calculation of cavity swelling in a transmission electron microscope through crystallographically aided void volume tracking

Accurate measurement of cavity swelling in a transmission electron microscope is essential to define material performance under irradiation, and the conventionally applied spherical assumption for the calculation of cavity volumes in irradiated materials can result in errors ranging between a 25% underestimation and 72% overestimation of volume purely based on the assumption of shape. This assumption is undeniably expedient for calculation but does not sufficiently account for the 3D nature of polyhedral cavities and their shape projection in the transmission electron microscope, and therefore presents too large of an associated uncertainty in swelling determination for faceted cavities. This uncertainty has been defined for many common cavity shapes in FCC and BCC crystal systems, and has been tabulated across the cubic fundamental region. A revised methodology for crystallographically aided void volume tracking, or CAVV-T, is presented and demonstrated on a specimen of neutron irradiated Ni. In-depth discussion on the application of this technique is provided along with resources to allow for conversion between the spherical assumption and this revised method. This work seeks to increase experimental confidence in the characterization and quantification of critical aspects of irradiation damage in materials by applying a crystallographically-resolved approach for cavity swelling calculation.

Haag IV, James V. [Pacific Northwest National Labo↗

DOE OSTI2025

Real-Time Automated pH Control within Batch Processes Relying on Raman pH Measurement

Nuclear fission is an energy source that can provide consistent power with very low associated carbon emissions. However, management of the used nuclear fuel is an important aspect of the application of nuclear power. Recycling of useful components from used fuel is an attractive option, but this involves chemical processing of the fuel. Possible chemical separation technologies that might be used in this regard are sensitive to solution pH. Raman spectroscopy is a promising technique for monitoring the pH of solutions in real time. Classical pH probes are too fragile to be used in the harsh environments encountered in nuclear fuel processing. Raman probes are robust and can withstand these harsh environments to track pH. Coupled with chemometric analysis, the demonstration of the use of Raman spectroscopy to track and predict the pH in carboxylate-buffered systems is made possible. Utilizing this spectroscopy in conjunction with Programmable Logic Controllers mimics industrial control systems used in many modern industrial settings. This showcases a pragmatic approach toward leveraging Raman spectroscopy and chemometric model outputs as inputs for a real-time control system. The model to predict pH created by chemometrics proved to be successful in tracking pH. The optimal pH for TALSPEAK extraction of lanthanides and actinides from aqueous solution is known to proceed in a narrow pH range of around pH = 2.8 ± 0.1. This study uses Raman optical monitoring and automated control to return and maintain solution pH within this range after acid or base perturbations move the solution pH well outside this region. Root-mean-square errors show that pH changes measured using Raman spectroscopy on the batch process solution are reliably measured and used to automatically correct and maintain solution pH. Measurement of solution pH tracks favorably with electrochemical pH probe comparison measurements. As a result, the ability to showcase Raman spectroscopy paired with chemometrics analysis acts as a durable, better alternative data source compared to traditional pH probes to optimize the separation efficiency in the used nuclear fuel processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Adaptive Model-Free Vehicle Path-Tracking via Fast-Converging Prescribed-Time Newton-Based Extremum-Seeking Control

Model-free control (MFC) offers a simple and effective approach to automated vehicle path-tracking without requiring an explicit plant model for control law design. However, gain tuning in MFC is typically carried out through trial-and-error, which can be time-consuming and may lead to suboptimal performance. To address this limitation, extremum-seeking-based adaptive MFC has shown promise by enabling real-time adaptation of control gains, without relying on a predefined vehicle model. Nonetheless, existing ESC approaches often suffer from slow convergence. This paper integrates MFC, employing longitudinal and lateral ultra-local models of a rear-wheel-drive vehicle, with a novel prescribed-time (PT) Newton-based extremum-seeking control (ESC) strategy that ensures rapid convergence of control gains within the prescribed time. Unlike conventional gradient-based ESC methods, the PT Newton-based ESC leverages artificial delays and time-periodic gains, not only to guarantee convergence within the specified time, but also to compensate for feedback delays. Simulation results demonstrate that the proposed approach significantly improves gain adaptation speed and tracking accuracy. This work advances adaptive model-free vehicle control by offering a high-performance, delay-resilient alternative to existing ESCMFC frameworks.

Waleed khan, Muhammad [The University of Texas at ↗

DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗