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DOE OSTI2018

Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations

Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Simulation of Mechanical Fractionation of Chopped Whole-Plant Corn (WPC) Using Discrete Element Method (DEM)

Fractionating whole-plant corn (WPC) in a single-pass harvesting system requires studies on the WPC-to-equipment interaction for improved property control, as well as mechanical and air-driven separation processes compared to the traditional multi-pass grain and stover harvesting system. The discrete element method (DEM) technique has the potential to simulate WPC mechanical fractionation and support simulation-based design of WPC separation processes. In this study, methods to develop DEM particle models of WPC (kernel, cob, stalk, and husk) and their material properties for simulating mass fractionation using the ASABE standard mechanical shaker were proposed. Measurement was done on the axial dimensions (major, intermediate, and minor) and mass of each WPC type (mean sample size is 56), sampled from single-pass harvesting. Applying gaussian multivariate regression and bootstrapping re-sampling techniques, a DEM particle approximate to each WPC was developed. Sensitivity analysis of the DEM Young‘s modulus, Poisson‘s ratio, and interaction parameters of coefficient of restitution, coefficient of rolling friction, and coefficient of static friction on mass fraction was performed after 156 ASABE sieve-shaking DEM simulation runs, generated using Latin Hypercube Design (LHD) design of experiment (DOE) from 19 DEM material parameters. DEM simulation using Hertz-Mindlin with flexible bond contact laws and DOE optimized material properties successfully reproduced the mass fractions retained in ASABE sieves at 9.8% mean relative error and a coefficient of determination of R2 = 0.87. Here, the DEM methodology developed for mechanical WPC mass fractionation could be deployed to perform virtual design of feedstock handling equipment and performance analysis of mechanical fraction systems.

09 BIOMASS FUELS↗

DOE OSTI2016

High precision tilt stage as a key element to a universal test mirror for characterization and calibration of slope measuring instruments

The ultimate performance of surface slope metrology instrumentation, such as long trace profilers and auto-collimator based deflectometers, is limited by systematic errors that are increased when the entire angular range is used for metrology of significantly curved optics. At the ALS X-Ray Optics Laboratory, in collaboration with the HZB/BESSY-II and PTB (Germany) metrology teams, we are working on a calibration method for deflectometers, based on a concept of a universal test mirror (UTM) [V. V. Yashchuk et al., Proc. SPIE 6704, 67040A (2007)]. Potentially, the UTM method provides high performance calibration and accounts for peculiarities of the optics under test (e.g., slope distribution) and the experimental arrangement (e.g., the distance between the sensor and the optic under test). At the same time, the UTM calibration method is inherently universal, applicable to a variety of optics and experimental arrangements. In this work, we present the results of tests with a key component of the UTM system, a custom high precision tilt stage, which has been recently developed in collaboration with Physik Instrumente, GmbH. The tests have demonstrated high performance of the stage and its capability (after additional calibration) to provide angular calibration of surface slope measuring profilers over the entire instrumental dynamic range with absolute accuracy better than 30 nrad. The details of the stage design and tests are presented. We also discuss the foundation of the UTM method and calibration algorithm, as well as the possible design of a full scale UTM system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Qutrit Randomized Benchmarking

Ternary quantum processors offer significant potential computational advantages over conventional qubit technologies, leveraging the encoding and processing of quantum information in qutrits (three-level systems). Therefore, to evaluate and compare the performance of such emerging quantum hardware it is essential to have robust benchmarking methods suitable for a higher-dimensional Hilbert space. We demonstrate extensions of industry standard randomized benchmarking (RB) protocols, developed and used extensively for qubits, suitable for ternary quantum logic. Using a superconducting five-qutrit processor, we find an average single-qutrit process infidelity of 3.8×10 -3 . Through interleaved RB, we characterize a few relevant gates, and employ simultaneous RB to fully characterize crosstalk errors. Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.85. Our results present and demonstrate RB-based tools to characterize the performance of a qutrit processor, and a general approach to diagnose control errors in future qudit hardware.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Characterization of XR-RV3 GafChromic{sup ®} films in standard laboratory and in clinical conditions and means to evaluate uncertainties and reduce errors

Purpose: To investigate the optimal use of XR-RV3 GafChromic{sup ®} films to assess patient skin dose in interventional radiology while addressing the means to reduce uncertainties in dose assessment. Methods: XR-Type R GafChromic films have been shown to represent the most efficient and suitable solution to determine patient skin dose in interventional procedures. As film dosimetry can be associated with high uncertainty, this paper presents the EURADOS WG 12 initiative to carry out a comprehensive study of film characteristics with a multisite approach. The considered sources of uncertainties include scanner, film, and fitting-related errors. The work focused on studying film behavior with clinical high-dose-rate pulsed beams (previously unavailable in the literature) together with reference standard laboratory beams. Results: First, the performance analysis of six different scanner models has shown that scan uniformity perpendicular to the lamp motion axis and that long term stability are the main sources of scanner-related uncertainties. These could induce errors of up to 7% on the film readings unless regularly checked and corrected. Typically, scan uniformity correction matrices and reading normalization to the scanner-specific and daily background reading should be done. In addition, the analysis on multiple film batches has shown that XR-RV3 films have generally good uniformity within one batch (<1.5%), require 24 h to stabilize after the irradiation and their response is roughly independent of dose rate (<5%). However, XR-RV3 films showed large variations (up to 15%) with radiation quality both in standard laboratory and in clinical conditions. As such, and prior to conducting patient skin dose measurements, it is mandatory to choose the appropriate calibration beam quality depending on the characteristics of the x-ray systems that will be used clinically. In addition, yellow side film irradiations should be preferentially used since they showed a lower dependence on beam parameters compared to white side film irradiations. Finally, among the six different fit equations tested in this work, typically used third order polynomials and more rational and simplistic equations, of the form dose inversely proportional to pixel value, were both found to provide satisfactory results. Fitting-related uncertainty was clearly identified as a major contributor to the overall film dosimetry uncertainty with up to 40% error on the dose estimate. Conclusions: The overall uncertainty associated with the use of XR-RV3 films to determine skin dose in the interventional environment can realistically be estimated to be around 20% (k = 1). This uncertainty can be reduced to within 5% if carefully monitoring scanner, film, and fitting-related errors or it can easily increase to over 40% if minimal care is not taken. This work demonstrates the importance of appropriate calibration, reading, fitting, and other film-related and scan-related processes, which will help improve the accuracy of skin dose measurements in interventional procedures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Clinical benefits of new immobilization system for hypofractionated radiotherapy of intrahepatic hepatocellular carcinoma by helical tomotherapy

Objective: A comprehensive clinical evaluation was conducted, assessing the Body Pro-Lok immobilization and positioning system to facilitate hypofractionated radiotherapy of intrahepatic hepatocellular carcinoma (HCC), using helical tomotherapy to improve treatment precision. Methods: Clinical applications of the Body Pro-Lok system were investigated (as above) in terms of interfractional and intrafractional setup errors and compressive abdominal breath control. To assess interfractional setup errors, a total of 42 patients who were given 5 to 20 fractions of helical tomotherapy for intrahepatic HCC were analyzed. Overall, 15 patients were immobilized using simple vacuum cushion (group A), and the Body Pro-Lok system was used in 27 patients (group B), performing megavoltage computed tomography (MVCT) scans 196 times and 435 times, respectively. Pretreatment MVCT scans were registered to the planning kilovoltage computed tomography (KVCT) for error determination, and group comparisons were made. To establish intrafractional setup errors, 17 patients with intrahepatic HCC were selected at random for immobilization by Body Pro-Lok system, undergoing MVCT scans after helical tomotherapy every week. A total of 46 MVCT re-scans were analyzed for this purpose. In researching breath control, 12 patients, randomly selected, were immobilized by Body Pro-Lok system and subjected to 2-phase 4-dimensional CT (4DCT) scans, with compressive abdominal control or in freely breathing states, respectively. Respiratory-induced liver motion was then compared. Results: Mean interfractional setup errors were as follows: (1) group A: X, 2.97 ± 2.47 mm; Y, 4.85 ± 4.04 mm; and Z, 3.77 ± 3.21 mm; pitch, 0.66 ± 0.62°; roll, 1.09 ± 1.06°; and yaw, 0.85 ± 0.82°; and (2) group B: X, 2.23 ± 1.79 mm; Y, 4.10 ± 3.36 mm; and Z, 1.67 ± 1.91 mm; pitch, 0.45 ± 0.38°; roll, 0.77 ± 0.63°; and yaw, 0.52 ± 0.49°. Between-group differences were statistically significant in 6 directions (p < 0.05). Mean intrafractional setup errors with use of the Body Pro-Lok system were as follows: X, 0.41 ± 0.46 mm; Y, 0.86 ± 0.80 mm; Z, 0.33 ± 0.44 mm; and roll, 0.12 ± 0.19°. Mean liver-induced respiratory motion determinations were as follows: (1) abdominal compression: X, 2.33 ± 1.22 mm; Y, 5.11 ± 2.05 mm; Z, 2.13 ± 1.05 mm; and 3D vector, 6.22 ± 1.94 mm; and (2) free breathing: X, 3.48 ± 1.14 mm; Y, 9.83 ± 3.00 mm; Z, 3.38 ± 1.59 mm; and 3D vector, 11.07 ± 3.16 mm. Between-group differences were statistically different in 4 directions (p < 0.05). Conclusions: The Body Pro-Lok system is capable of improving interfractional and intrafractional setup accuracy and minimizing tumor movement owing to respirations in patients with intrahepatic HCC during hypofractionated helical tomotherapy.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

EFCOG Best Practice HPI for Knowledge Workers ISM-HPI-22-02

This document is a collection of these best practices as determined by team members. This best practice attempts to: Realize opportunities to break the myth where people believe that HPI does not apply to them as they perform no physical work. Recommend options to create an environment that promotes intellectual collaboration and trust, enabling candor and vulnerability. Explain how errors manifest differently from the same human fallibility. Knowledge workers (KW) have errors that take different perspectives to find and mitigate the unique manifestation of these conditions. Help KW identify the critical steps (or risk important steps) in their processes. Reduce risk/consequence from KW errors (limit latent errors as well as finding latent conditions), building resiliency into KW tasks. Mitigation strategies may be different.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2016

MO-FG-BRA-06: Electromagnetic Beacon Insertion in Lung Cancer Patients and Resultant Surrogacy Errors for Dynamic MLC Tumour Tracking

Purpose: To assess endo-bronchial electromagnetic beacon insertion and to quantify the geometric accuracy of using beacons as a surrogate for tumour motion in real-time multileaf collimator (MLC) tracking of lung tumours. Methods: The LIGHT SABR trial is a world-first clinical trial in which the MLC leaves move with lung tumours in real time on a standard linear accelerator. Tracking is performed based on implanted electromagnetic beacons (CalypsoTM, Varian Medical Systems, USA) as a surrogate for tumour motion. Five patients have been treated and have each had three beacons implanted endo-bronchially under fluoroscopic guidance. The centre of mass (C.O.M) has been used to adapt the MLC in real-time. The geometric error in using the beacon C.O.M as a surrogate for tumour motion was measured by measuring the tumour and beacon C.O.M in all phases of the respiratory cycle of a 4DCT. The surrogacy error was defined as the difference in beacon and tumour C.O.M relative to the reference phase (maximum exhale). Results: All five patients have had three beacons successfully implanted with no migration between simulation and end of treatment. Beacon placement relative to tumour C.O.M varied from 14 to 74 mm and in one patient spanned two lobes. Surrogacy error was measured in each patient on the simulation 4DCT and ranged from 0 to 3 mm. Surrogacy error as measured on 4DCT was subject to artefacts in mid-ventilation phases. Surrogacy error was a function of breathing phase and was typically larger at maximum inhale. Conclusion: Beacon placement and thus surrogacy error is a major component of geometric uncertainty in MLC tracking of lung tumours. Surrogacy error must be measured on each patient and incorporated into margin calculation. Reduction of surrogacy error is limited by airway anatomy, however should be taken into consideration when performing beacon insertion and planning. This research is funded by Varian Medical Systems via a collaborative research agreement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. Here, we leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T 1/2 ) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T 1/2 values. We then compare ML T 1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T 1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T 1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T 1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T 1/2 values have large errors and show no correlation to estimated T 1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

SU-E-T-186: Cloud-Based Quality Assurance Application for Linear Accelerator Commissioning

Purpose: To identify anomalies and safety issues during data collection and modeling for treatment planning systems Methods: A cloud-based quality assurance system (AQUIRE - Automated QUalIty REassurance) has been developed to allow the uploading and analysis of beam data aquired during the treatment planning system commissioning process. In addition to comparing and aggregating measured data, tools have also been developed to extract dose from the treatment planning system for end-to-end testing. A gamma index is perfomed on the data to give a dose difference and distance-to-agreement for validation that a beam model is generating plans consistent with the beam data collection. Results: Over 20 linear accelerators have been commissioning using this platform, and a variety of errors and potential saftey issues have been caught through the validation process. For example, the gamma index of 2% dose, 2mm DTA is quite sufficient to see curves not corrected for effective point of measurement. Also, data imported into the database is analyzed against an aggregate of similar linear accelerators to show data points that are outliers. The resulting curves in the database exhibit a very small standard deviation and imply that a preconfigured beam model based on aggregated linear accelerators will be sufficient in most cases. Conclusion: With the use of this new platform for beam data commissioning, errors in beam data collection and treatment planning system modeling are greatly reduced. With the reduction in errors during acquisition, the resulting beam models are quite similar, suggesting that a common beam model may be possible in the future. Development is ongoing to create routine quality assurance tools to compare back to the beam data acquired during commissioning. I am a medical physicist for Alzyen Medical Physics, and perform commissioning services.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Towards sharp error analysis of extended Lagrangian molecular dynamics

The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrained latent variables. The XLMD method relaxes the constraints by introducing a fictitious mass ε for the latent variables and solving a set of singularly perturbed ordinary differential equations. While favorable numerical performance of XLMD has been demonstrated in several different contexts in the past decade, mathematical analysis of the method remains scarce. Here, we propose the first error analysis of the XLMD method in the context of a classical polarizable force field model. While the dynamics with respect to the atomic degrees of freedom are general and nonlinear, the key mathematical simplification of the polarizable force field model is that the constraints on the latent variables are given by a linear system of equations. We prove that when the initial value of the latent variables is compatible in a sense that we define, XLMD converges as the fictitious mass ε is made small with $\mathscr{O}$(ε) error for the atomic degrees of freedom and with $\mathscr{O}$($\sqrt{ε}$) error for the latent variables, when the dimension of the latent variable d' is 1. Furthermore, when the initial value of the latent variables is improved to be optimally compatible in a certain sense, we prove that the convergence rate can be improved to $\mathscr{O}$(ε) for the latent variables as well. Numerical results verify that both estimates are sharp not only for d'=1, but also for arbitrary d'. In the setting of general d', we do obtain convergence, but with the non-sharp rate of $\mathscr{O}$($\sqrt{ε}$) for both the atomic and latent variables.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

DRSPALL: Impact of the Modification of the Numerical Spallings Model on Waste Isolation Pilot Plant Performance Assessment.

The numerical code DRSPALL (from direct release spallings) is written to calculate the volume of Waste Isolation Pilot Plant (WIPP) solid waste subject to material failure and transport to the surface as a result of a hypothetical future inadvertent drilling intrusion. An error in the implementation of the DRSPALL finite difference equations was discovered as documented in Software Problem Report (SPR) 13-001. The modifications to DRSPALL to correct the finite difference equations are detailed, and verification and validation testing has been completed for the modified DRSPALL code. The complementary cumulative distribution function (CCDF) of spallings releases obtained using the modified DRSPALL is higher compared to that found in previous WIPP performance assessment (PA) calculations. Compared to previous PAs, there was an increase in the number of vectors that result in a nonzero spallings volume, which generally translates to an increase in spallings releases. The overall mean CCDFs for total releases using the modified DRSPALL are virtually unchanged, thus the modification to DRSPALL did not impact WIPP PA calculation results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Transactive Electric Water Heater Agent: Design and Performance Evaluation

Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLABD model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH. Index Terms—Demand flexibility

demand flexibility, distribution system, electric ↗

DOE OSTI2020

Raytracing the long trace profiler

The optical design of the Long Trace Profiler optical system is explored with a commercial raytrace program, Zemax OpticStudio™ (ZOS)1, with the intent of finding and correcting sources of systematic error. ZOS provides both geometric raytracing tools and physical optics Gaussian beam propagation and diffraction image calculation tools, and two design modes, sequential component (SC) and non-sequential component (NSC) that are optimized for different aspects of the design process. The original LTP-II system employs a singlet lens with a 1250 mm focal length. It is optimized to provide minimum distortion over a surface slope angle range of ±5 mrad. Using the ZOS tools, we are able to simulate ghost rays that produce distortion in the beam spot image and can minimize the distortion by deliberate misalignment of the beamsplitter (BS) components. Unfortunately, the reference beam is compromised because of the component tilts. The most recent LTP500 system design simplifies the optical system and makes the reference beam usable again, even with misalignment of the polarizing beamsplitter (PBS). Two lenses are designed for the LTP500 - a cemented doublet that has been fabricated, and a singlet with one aspheric surface. Both have focal distances of 500 mm with an expanded angular measurement range of ±10 mrad. The aspheric singlet provides superior performance. ZOS allows the import of wavefront measurement data produced by commercial interferometer software. We apply the wavefront error measurement from the cemented doublet to the model to show that the 19nm RMS wavefront error needs to be improved by at least a factor of ten in order to reduce the systematic error to a level that will allow the LTP to approach its design limit of a few tens of nanoradians.

Takacs, Peter Z.↗

DOE OSTI2023

Ground vehicle lane-keeping assistance system via differential flatness output feedback control and algebraic derivative estimation

Vehicle run-off-road is one of the most frequent and fatal traffic accidents in the United States. Various lane-keeping assistance (LKA) systems have been developed in the last decade to help drivers stay on the road. Most of them are built upon linear driver–vehicle–road (DVR) models and treat road curvature as a disturbance. Albeit effective, their control performance would degrade if road curvature varies rapidly. Here, this paper proposes a novel nonlinear DVR model by integrating a driver steering model into vehicle–road kinematics, which explicitly considers road curvature. Particularly, this nonlinear DVR system has been proven to be differentially flat, and a flatness-based LKA system is designed. Additionally, Model-Free Control is introduced to compensate for system modeling errors. Hardware-in-the-loop simulations and diver-in-the-loop experiments validate the proposed control framework and demonstrate the performance enhancement with respect to a representative linear robust LKA system.

42 ENGINEERING↗