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DOE OSTI2017

The Calibration System of the E989 Experiment at Fermilab

The muon anomaly aµ is one of the most precise quantity known in physics experimentally and theoretically. The high level of accuracy permits to use the measurement of aµ as a test of the Standard Model comparing with the theoretical calculation. After the impressive result obtained at Brookhaven National Laboratory in 2001 with a total accuracy of 0.54 ppm, a new experiment E989 is under construction at Fermilab, motivated by the diff of aexp SM µ − aµ ∼ 3σ. The purpose of the E989 experiment is a fourfold reduction of the error, with a goal of 0.14 ppm, improving both the systematic and statistical uncertainty. With the use of the Fermilab beam complex a statistic of × 21 with respect to BNL will be reached in almost 2 years of data taking improving the statistical uncertainty to 0.1 ppm. Improvement on the systematic error involves the measurement technique of ωa and ωp, the anomalous precession frequency of the muon and the Larmor precession frequency of the proton respectively. The measurement of ωp involves the magnetic field measurement and improvements on this sector related to the uniformity of the field should reduce the systematic uncertainty with respect to BNL from 170 ppb to 70 ppb. A reduction from 180 ppb to 70 ppb is also required for the measurement of ωa; new DAQ, a faster electronics and new detectors and calibration system will be implemented with respect to E821 to reach this goal. In particular the laser calibration system will reduce the systematic error due to gain fl of the photodetectors from 0.12 to 0.02 ppm. The 0.02 ppm limit on systematic requires a system with a stability of 10−4 on short time scale (700 µs) while on longer time scale the stability is at the percent level. The 10−4 stability level required is almost an order of magnitude better than the existing laser calibration system in particle physics, making the calibration system a very challenging item. In addition to the high level of stability a particular environment, due to the presence of a 14 m diameter storage ring, a highly uniform magnetic field and the detector distribution around the storage ring, set specific guidelines and constraints. This thesis will focus on the final design of the Laser Calibration System developed for the E989 experiment. Chapter 1 introduces the subject of the anomalous magnetic moment of the muon; chapter 2 presents previous measurement of g-2, while chapter 3 discusses the Standard Model prediction and possible new physics scenario. Chapter 4 describes the E989 experiment. In this chapter will be described the experimental technique and also will be presented the experimental apparatus focusing on the improvements necessary to reduce the statistical and systematic errors. The main item of the thesis is discussed in the last two chapters: chapter 5 is focused on the Laser Calibration system while chapter 6 describes the Test Beam performed at the Beam Test Facility of Laboratori Nazionali di Frascati from the 29th February to the 7th March as a final test for the full calibrations system. An introduction explain the physics motivation of the system and the diff t devices implemented. In the final chapter the setup used will be described and some of the results obtained will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1986

Combining Structural and Substructural Mathematical Models

Automation reduces input length and potential data-entry errors. Matrix Automated Reduction and Coupling (MARC) program used for combining NASTRAN substructural models with primary structural model MARC also constructs job-control language (JCL) stream for NASTRAN batch job that utilizes previously-written user library of dynamic models. Minimizes lengthy input and reduces potential data-entry errors. MARC procedure used in assembling Space Shuttle orbiter dynamic models since 1983 and reduced NASTRAN modeling input time by as much as 50 percent. MARC program written in FORTRAN IV for interactive execution.

Choa, V. K.↗

NASA NTRS1993

An optimized finite-difference scheme for wave propagation problems

Two fully-discrete finite-difference schemes for wave propagation problems are presented, a maximum-order scheme and an optimized (or spectral-like) scheme. Both combine a seven-point spatial operator and an explicit six-stage time-march method. The maximum-order operator is fifth-order in space and is sixth-order in time for a linear problem with periodic boundary conditions. The phase and amplitude errors of the schemes obtained using Fourier analysis are given and compared with a second-order and a fourth-order method. Numerical experiments are presented which demonstrate the usefulness of the schemes for a range of problems. For some problems, the optimized scheme leads to a reduction in global error compared to the maximum-order scheme with no additional computational expense.

Zingg, D. W.↗

DOE OSTI2026

Transplatformer: translating toxicogenomic profiles between generations of platforms

Background Transcriptomic profiling technologies have advanced the analysis of biological and toxicological responses. However, substantial differences in probe design, dynamic range, gene coverage, and preprocessing pipelines across platforms introduce artifacts that limit cross-study integration and hinder the reuse of historical datasets. We aim to develop computational methods for accurate cross-platform translation to maximize the value of legacy resources. Results We present TransPlatformer a deep learning framework for translating gene expression profiles across heterogeneous toxicogenomics platforms. TransPlatformer employs a novel attention-based architecture to map high-dimensional fold-change vectors from legacy microarray technologies to current platforms. Models are trained and evaluated using DrugMatrix, spanning three technological generations. We investigate mixed-tissue, single-tissue, and cross-tissue training paradigms and benchmark performance against multilayer perceptron and matrix-completion baselines. In mixed-tissue training, TransPlatformer achieves a greater than 50% reduction in mean absolute error (0.043 vs. 0.09) and nearly doubles Pearson correlation ( ≈ 0.71 vs. 0.37) relative to baseline methods. Importantly, TransPlatformer preserves rare but biologically meaningful over- and under-expressed signals, with mean absolute error below 0.22. Single-tissue models yield further improvements for well-represented organs, such as a 10% reduction in liver mean absolute error, while underscoring the need for data augmentation strategies in low-sample tissues.ra Conclusions TransPlatformer provides an effective and scalable computational solution for cross-platform transcriptomic translation. By enabling biologically faithful harmonization of gene expression data, the proposed approach facilitates the reuse of legacy toxicogenomics datasets, enhances downstream biomarker discovery, and supports more reproducible predictive modeling in toxicology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Quantifying the impact of precision errors on quantum approximate optimization algorithms

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that fixed precision implementations of QAOA circuits are subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters at the cost of increasing circuit depth.

quantum algorithms↗

NASA NTRS1991

An analytically linearized helicopter model with improved modeling accuracy

An analytically linearized model for helicopter flight response including rotor blade dynamics and dynamic inflow, that was recently developed, was studied with the objective of increasing the understanding, the ease of use, and the accuracy of the model. The mathematical model is described along with a description of the UH-60A Black Hawk helicopter and flight test used to validate the model. To aid in utilization of the model for sensitivity analysis, a new, faster, and more efficient implementation of the model was developed. It is shown that several errors in the mathematical modeling of the system caused a reduction in accuracy. These errors in rotor force resolution, trim force and moment calculation, and rotor inertia terms were corrected along with improvements to the programming style and documentation. Use of a trim input file to drive the model is examined. Trim file errors in blade twist, control input phase angle, coning and lag angles, main and tail rotor pitch, and uniform induced velocity, were corrected. Finally, through direct comparison of the original and corrected model responses to flight test data, the effect of the corrections on overall model output is shown.

Jensen, Patrick T.↗

DOE OSTI2016

Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging

Purpose: Patient motion is a common problem during dynamic positron emission tomography (PET) scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction (CTAC) artifacts that result from PET–CT misalignment. Methods: A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time-frames, and direction of motion was recorded; discrepancy between observers was resolved by consensus review. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based cardiac-torso phantom, with characteristic time activity curves (TACs) assigned to the heart wall (myocardium) and blood regions. Simulated projection data were corrected for attenuation and reconstructed using filtered back-projection. All simulations were performed without noise added, and a single CT image was used for attenuation correction and aligned to the early- or late-frame PET images. Results: In the patient cohort, mild motion of 0.5 ± 0.1 cm occurred in 24% and moderate motion of 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion, with 30% in the superior direction. Anterior/posterior motion was predominant (29%) in the posterior direction. Left/right motion occurred in 24% of cases, with similar proportions in the left and right directions. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left toward the adjacent lung region, resulting in a severe undercorrection for attenuation of the heart wall. Simulations also indicated that the magnitude of MBF errors resulting from motion in the superior/inferior and anterior/posterior directions was similar (up to 250%). Body motion effects were more detrimental for higher resolution PET imaging (2 vs 10 mm full-width at half-maximum), and for motion occurring during the mid-to-late time-frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors, but did not account for the residual PET–CTAC misalignment artifacts. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction. Conclusions: Patient body motion can produce MBF estimation errors up to 500%. To reduce these errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time-frames, since these conditions produce the largest errors in MBF, particularly for high resolution PET imaging. Ideally, motion correction should be done before or during image reconstruction to eliminate PET-CTAC misalignment artifacts.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1984

Effects of motion base and g-seat cueing of simulator pilot performance

In order to measure and analyze the effects of a motion plus g-seat cueing system, a manned-flight-simulation experiment was conducted utilizing a pursuit tracking task and an F-16 simulation model in the NASA Langley visual/motion simulator. This experiment provided the information necessary to determine whether motion and g-seat cues have an additive effect on the performance of this task. With respect to the lateral tracking error and roll-control stick force, the answer is affirmative. It is shown that presenting the two cues simultaneously caused significant reductions in lateral tracking error and that using the g-seat and motion base separately provided essentially equal reductions in the pilot's lateral tracking error.

Ashworth, B. R.↗

NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗

DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗

DOE OSTI2018

Convergence of the multigrid reduction in time algorithm for the linear elasticity equations

This paper presents some recent advances for parallel-in-time methods applied to linear elasticity. With recent computer architecture changes leading to stagnant clock speeds, but ever increasing numbers of cores, future speedups will be available through increased concurrency. Thus, sequential algorithms, such as time stepping, will suffer a bottleneck. This paper explores multigrid reduction in time (MGRIT) for an important application area, linear elasticity. Previously, efforts at parallel-in-time for elasticity have experienced difficulties, for example, the beating phenomenon. As a result, practical parallel-in-time algorithms for this application area currently do not exist. This paper proposes some solutions made possible by MGRIT (e.g., slow temporal coarsening and FCF-relaxation) and, more importantly, a different formulation of the problem that is more amenable to parallel-in-time methods. Using a recently developed convergence theory for MGRIT and Parareal, we show that the changed formulation of the problem avoids the instability issues and allows the reduction of the error using two temporal grids. We then extend our approach to the multilevel case, where we demonstrate how slow temporal coarsening improves convergence. Finally, the paper ends with supporting numerical results showing a practical algorithm enjoying speedup benefits over the sequential algorithm.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

Limits on static shape control for space structures

This paper deals with correction of shape distortion due to zero-mean normally distributed errors in structural sizes. The concept of ideal actuators - actuators that can produce any desired displacement field - is introduced. Using this concept, a bound on the possible improvement in the expected value of the root-mean-square shape error is obtained. The shape correction associated with the ideal actuators is also characterized. An actuator effectiveness index is developed by comparing the displacement field generated by the actuator to the ideal. The results are specialized to a simple form for truss structures composed of nominally identical members. The bound and effectiveness index are tested on a 55-m radiometer antenna truss structure. It is found that previously obtained results for optimum actuators had a performance close to the bound obtained here. Also, it is found that large numbers of actuators are needed for large reductions in shape errors. Furthermore, the actuators associated with the optimum design are shown to have high effectiveness indices. Since only a small fraction of truss elements tend to have high effectiveness indices, the use of the effectiveness index can greatly reduce the number of truss members that need to be considered actuator sites.

Haftka, Raphael T.↗

NASA NTRS1988

Porous plug for reducing orifice induced pressure error in airfoils

A porous plug is provided for the reduction or elimination of positive error caused by the orifice during static pressure measurements of airfoils. The porous plug is press fitted into the orifice, thereby preventing the error caused either by fluid flow turning into the exposed orifice or by the fluid flow stagnating at the downstream edge of the orifice. In addition, the porous plug is made flush with the outer surface of the airfoil, by filing and polishing, to provide a smooth surface which alleviates the error caused by imperfections in the orifice. The porous plug is preferably made of sintered metal, which allows air to pass through the pores, so that the static pressure measurements can be made by remote transducers.

Elizabeth B Plentovich↗

NASA NTRS1981

Modifying Stokes' function to reduce the error of geoid undulation computations

Errors in computations of the geoid undulation, which determines the position of the geoid with respect to a reference ellipsoid, introduced by various modifications of the Stokes' function in the Stokes integral formula for the undulation are examined in light of currently available harmonic coefficients. The variants considered include the conventional Molodenskii truncation theory, in which integration is limited to a spherical cap, and the variations suggested by Molodenskii (1958) and Meissl (1971). In computations of the undulation from gravity anomalies in a spherical cap and terrestrial and satellite harmonic potential coefficients (GEM 9 and GEM 10B), it is shown that either of the modifications to the conventional Molodenskii theory results in substantial reductions in truncation errors, with the simpler modification of Meissl even providing superior results in certain circumstances.

Jekeli, C.↗

NASA NTRS1992

Potential of long-baseline infrared interferometry for narrow-angle astrometry

Narrow-angle astrometry has many astrophysical applications, from the measurement of parallaxes to the search for planets around nearby stars. Ground-based long-focus telescopes with photoelectric detectors have achieved accuracies of about 2-3 milliarcsec in 1 h. This accuracy is limited primarily by the atmosphere, and is consistent with models of atmospheric turbulence. However, applying these turbulence models to observations with long-baseline interferometers yields astrometric errors that are far smaller than can be achieved with long-focus telescopes. The predictions for the ultimate accuracy of ground-based narrow-angle astrometry using long-baseline IR (2.2 micron) stellar interferometers are very promising. With the excellent seeing at a high altitude site like Mauna Kea, the atmospheric limit for a 1 h astrometric measurement is expected to be of the order of 10 microarcsec for 1 h of integration. This two-order-of-magnitude improvement over conventional measurements is due to two effects. One is that a long-baseline IR interferometer can find useful reference stars very near an arbitrary target star, so that the atmospherically-induced motions of both stars are highly correlated. The second is that the baseline length can be much larger than the separation of the stellar beams in the turbulent atmosphere, resulting in a reduction in astrometric error with increasing baseline length.

Shao, M.↗

DOE OSTI2014

SU-E-T-642: Safety Procedures for Error Elimination in Cyberknife Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT)

Purpose: Cyberknife system is used for providing stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) hypofractionation scheme. The whole treatment delivery is based on live imaging of the patient. The minor error made at any stage may bring severe radiation injury to the patient or damage to the system itself. Several safety measures were taken to make the system safer. Methods: The radiation treatment provided thru a 6MV linac attached to Kuka robot (Cyberknife G4, Accuray Inc. Sunnyvale, CA, USA). Several possible errors were identified related to patient alignment, treatment planning, dose delivery and physics quality assurance. During dose delivery, manual and visual checks were introduced to confirm pre and intra-treatment imaging to reduce possible errors. One additional step was introduced to confirm that software tracking-tools had worked correctly with highest possible confidence level. Robotic head move in different orientations over and around the patient body, the rigidity of linac-head cover and other accessories was checked periodically. The vender was alerted when a tiny or bigger piece of equipment needed additional interlocked support. Results: As of our experience treating 525 patients on Cyberknife during the last four years, we saw on and off technical issues. During image acquisition, it was made essential to follow the site-specific imaging protocols. Adequate anatomy was contoured to document the respective doses. Followed by auto-segmentation, manual tweaking was performed on every structure. The calculation box was enclosing the whole image during the final calculation. Every plan was evaluated on slice-by slice basis. To review the whole process, a check list was maintained during the physics 2nd-check. Conclusion: The implementation of manual and visual additional checks introduced along with automated checks for confirmation was found promising in terms of reduction in systematic errors and making the system safer than before.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Neural-network learning of SPOD latent dynamics

Here, we aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). The proposed method is based on three fundamental steps: in the first, once that the mean flow field has been subtracted from the realizations (also referred to as snapshots), we compress the data from a high-dimensional representation to a lower dimensional one by constructing the SPOD latent space; in the second, we build the time-dependent coefficients by projecting the snapshots containing the fluctuations onto the SPOD basis and we learn their evolution in time with the aid of recurrent neural networks; in the third, we reconstruct the high-dimensional data from the learnt lower -dimensional representation. The proposed method is demonstrated on two different test cases, namely, a compressible jet flow, and a geophysical problem known as the Madden-Julian Oscillation. An extensive comparison between SPOD and the equivalent POD-based counterpart is provided and differences between the two approaches are highlighted. The numerical results suggest that the proposed model is able to provide low rank predictions of complex statistically stationary data and to provide insights into the evolution of phenomena characterized by specific range of frequencies. The comparison between POD and SPOD surrogate strategies highlights the need for further work on the characterization of the interplay of error between data reduction techniques and neural network forecasts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

The simplest equivalent circuit of a pulsed dielectric barrier discharge and the determination of the gas gap charge transfer

The concept of the simplest equivalent circuit for a dielectric barrier discharge (DBD) is critically reviewed. It is shown that the approach is consistent with experimental data measured either in large-scale sinusoidal-voltage driven or miniature pulse-voltage driven DBDs. An expression for the charge transferred through the gas gap q(t) is obtained with an accurate account for the displacement current and the values of DBD reactor capacitance. This enables (i) the significant reduction of experimental error in the determination of q(t) in pulsed DBDs, (ii) the verification of the classical electrical theory of ozonizers about maximal transferred charge q{sub max}, and (iii) the development of a graphical method for the determination of q{sub max} from charge-voltage characteristics (Q-V plots, often referred as Lissajous figures) measured under pulsed excitation. The method of graphical presentation of q{sub max} is demonstrated with an example of a Q-V plot measured under pulsed excitation. The relations between the discharge current j{sub R}(t), the transferred charge q(t), and the measurable parameters are presented in new forms, which enable the qualitative interpretation of the measured current and voltage waveforms without the knowledge about the value of the dielectric barrier capacitance C{sub d}. Whereas for quantitative evaluation of electrical measurements, the accurate estimation of the C{sub d} is important.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗