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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Employing Eye Trackers to Reduce Nuisance Alarms

When process operators anticipate an alarm prior to its annunciation, that alarm loses information value and becomes a nuisance. This study investigated using eye trackers to measure and adjust the salience of alarms with three methods of gaze-based acknowledgement (GBA) of alarms that estimate operator anticipation. When these methods detected possible alarm anticipation, the alarm’s audio and visual salience was reduced. A total of 24 engineering students (male = 14, female = 10) aged between 18 and 45 were recruited to predict alarms and control a process parameter in three scenario types (parameter near threshold, trending, or fluctuating). The study evaluated whether behaviors of the monitored parameter affected how frequently the three GBA methods were utilized and whether reducing alarm salience improved control task performance. The results did not show significant task improvement with any GBA methods (F(3,69) = 1.357, p = 0.263, partial η 2 = 0.056). However, the scenario type affected which GBA method was more utilized (X 2 (2, N = 432) = 30.147, p < 0.001). Alarm prediction hits with gaze-based acknowledgements coincided more frequently than alarm prediction hits without gaze-based acknowledgements (X 2 (1, N = 432) = 23.802, p < 0.001, OR = 3.877, 95% CI 2.25–6.68, p < 0.05). Participant ratings indicated an overall preference for the three GBA methods over a standard alarm design (F(3,63) = 3.745, p = 0.015, partial η 2 = 0.151). This study provides empirical evidence for the potential of eye tracking in alarm management but highlights the need for additional research to increase validity for inferring alarm anticipation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Realization of a high luminosity muon beam at the Jefferson Lab

The proton is the major component of the visible matter of our universe, so being able to determine its fundamental properties, such as its charge radius, is of vital importance. In recent years, several experiments have been performed for the accurate measurement of the proton radius with different experimental techniques. The main and most common are based on two different techniques: ep scattering and Lamb shift spectroscopy of regular hydrogen atoms. The latter can also be performed on muonic hydrogen atoms with a significant improvement in accuracy. Recent measurements that use this method have highlighted a discrepancy between the results obtained with the diffusion of electrons and those obtained with the spectroscopic technique leading to the so-called ?proton radius puzzle?. The explanation of why this difference between the measured values exists is still a matter of discussion. The last attempt to address the puzzle was the PRad Experiment. It was performed with a unique experimental setup that gave the scientists an excellent control on the systematic errors that are usually present in electron-proton scattering experiments. This has significantly increased the precision of the measurement, bringing it in the same area as the spectroscopic ones done on muonic hydrogen atoms, which are considered the most precise. However, to solve the puzzle once and for all, even more precise electron scattering measurements are needed. Alternatively, new methods can be used, such as eg. muon-proton scattering at low transferred momenta in order to lower the systematic errors that comes with the measurement. This thesis tries to address this problem by studying the set up of an intense muon beam at the Jefferson Laboratory (JLab) that can be used to carry out a high-precision m ? p scattering experiment. Using Montecarlo simulations I studied the muon production resulting from the interaction of the primary electron beam with the beam-dump at JLab Hall-A. The simulations were performed using the FLUKA package via the FLAIR graphical interface. In this study, I modified an existing geometry (built for the ?Beam Dump eXperiment?) and performed several studies in order to optimize the muon beam parameters. In order to estimate the beam intensity, I studied the muon generation and transportation through the concrete bunker surrounding the Hall-A beam-dump. Detailed studies on muon attenuation and dispersion at different depth allowed me to track the beam profile in space and momentum as a function of the distance from the beam-dump. Last, but not least, I studied a possible tagging system for the muon beam. The tagging system will allow us to know with good precision (O(1=1000)) the energy of each muon of the beam. Based on the results obtained, I created an event generator that incorporates the detailed description of the muon beam (energy, position, emittance) useful to define and optimize the experimental setup for a future measurement of the proton radius at the JLab.

Fulci, Antonino↗
DOE OSTI2016

SU-F-T-41: 3D MTP-TRUS for Prostate Implant

Purpose: Prostate brachytherapy is an effective treatment for early prostate cancer. The current prostate implant is limited to using 2D transrectal ultrassound (TRUS) or machenical motor driven 2D array either in the end or on the side. Real-time 3D images can improve the accuracy of the guidance of prostate implant. The concept of our system is to allow realtime full visualization of the entire prostate with the multiple transverse scan. Methods: The prototype of 3D Multiple-Transverse-Plane Transrectal Ultrasound probe (MTP-TRUS) has been designed by us and manufactured by Blatek inc. It has 7 convex linear arrays and each array has 96 elements. It is connected to cQuest Fire bird research system (Cephasonics inc.) which is a flexible and configurable ultrasound-development platform. The size of cQuest Firebird system is compact and supports the real-time wireless image transferring. A relay based mux board is designed for the cQuest Firebird system to be able to connect 672 elements. Results: The center frequency of probe is 6MHz±10%. The diameter of probe is 3cm and the length is 20cm. The element pitch is 0.205 mm. Array focus is 30mm and spacing 1.6cm. The beam data for each array was measured and met our expectation. The interface board of MTP-TURS is made and able to connect to cQuest Firebird system. The image display interface is still under the development. Our real-time needle tracking algorithm will be implemented too. Conclusion: Our MTP-TRUS system for prostate implant will be able to acquire real-time 3D images of prostate and do the real-time needle segmentation and tracking. The system is compact and have wireless function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-BRB-05: Using a Research Real-Time Control Interface to Go Beyond Dynamic MLC Tracking

Current state-of-the art digital C-arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient-specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities. Learning Objectives: Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites. Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement. Understand methods for testing and validating delivery system performance. Identify tools available on current delivery systems for implementation and control for such treatments. Obtain the update in clinical applications, trials and regulatory approval. K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical Systems V. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowship S. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
DOE OSTI2022

Procedure Parsing: A Method for Parsing Handwritten Documents into Computer-Based Procedures

The nuclear industry is heavily procedure driven, where almost everything has a step-by-step instruction that is expected to be followed in detail. Historically, these procedures were printed on paper copies. Recently, the industry transitioned towards electronic copies (i.e., PDFs on tablets). One major drive for this transition is the introduction of human error and loss of situation awareness when using paper copies. However, electronic copies of documents inherently have the same error traps as their paper cousins. Therefore, there is an increased interest in a way to utilize the information in the step-by-step guidance, but to present it in a dynamic manner that guides the user and adapts to any encountered conditions. Researchers at Idaho National Laboratory propose a flexible, automated method based on document parsing and augmented by natural language processing (NLP) techniques, to address these shortcomings and capitalize on these recent advancements in machine learning. The proposed method provides a cost-effective solution for computer-assisted procedure parsing of hand-written control room procedures, originally authored in Word or PDF formats, into instructions that can be displayed as computer-based procedures (CBP) in a modern graphical user interface. The researchers devised, implemented and demonstrated the Operating Procedure Extender for Novel Systems (OPENS) method in 2020. The key to OPENS is to map the original procedure text into a context-free grammar, tying content to equipment, locations, and other steps, actions, etc. This formal grammar is then used to isolate and define keywords and actions verbs, such as “measure” or “evaluate” and tie them to specific equipment referenced within that step or located in other steps, substeps, actions, subactions and tables throughout the procedure. OPENS generates an abstract syntax tree from the document which it uses to store a copy of this information in the open-standard, machine-readable and human-readable file formats XML and JSON. The XML is useful to preserve the relational aspects of the procedure for referencing tables and branching information so the user can be directed to the next appropriate active step based on the values entered for that step and previous steps. The JSON is useful for storing and exchanging data objects used to track responses to previous steps and state changes in simulated environments. In future iterations, these formats can also be used for storing more detailed information about input during plant operation or simulation. The techniques the researcher developed could further be improved by integration of recent advancements in machine learning. NLP methods could standardize documents, correct for grammatical error, and provide automated semantic validation. The researcher expects that self-supervised techniques applied to collections of natural language instructions could strengthen the model with broader context. All these methods together give us a practical way to automatically extract protocols from documents and user interactions, empowering researchers, procedure writers and nuclear operators while moving the industry forward.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Measurement of the production of charm jets tagged with D 0 mesons in pp collisions at $\sqrt{\mathrm{s}}=7$ TeV

The production of charm jets in proton-proton collisions at a center-of-mass energy of $\sqrt{\mathrm{s}}=7$ TeV was measured with the ALICE detector at the CERN Large Hadron Collider. The measurement is based on a data sample corresponding to a total integrated luminosity of 6.23 nb -1 , collected using a minimum-bias trigger. Charm jets are identified by the presence of a D 0 meson among their constituents. The D 0 mesons are reconstructed from their hadronic decay D 0 → $K^-π^+$. The D 0 -meson tagged jets are reconstructed using tracks of charged particles (track-based jets) with the anti-$k$ T algorithm in the jet transverse momentum range 5 < p T , j e t c h 30 GeV/ c and pseudorapidity |$η$ jet | < 0.5. The fraction of charged jets containing a D 0 -meson increases with p T , j e t c h from 0.042 ± 0.004 (stat) ± 0.006 (syst) to 0.080 ± 0.009 (stat) ± 0.008 (syst). The distribution of D 0 -meson tagged jets as a function of the jet momentum fraction carried by the D 0 meson in the direction of the jet axis ( z &#x2225;<!-- ∥ --> c h ) is reported for two ranges of jet transverse momenta, 5 < p T , j e t c h < 15 GeV/ c and 15 < p T , j e t c h < 30 GeV/ c in the intervals 0.2 < z &#x2016; c h < 1.0 and 0.4 < z &#x2016; c h < 1.0 respectively. The data are compared with results from Monte Carlo event generators (PYTHIA 6, PYTHIA 8 and Herwig 7) and with a Next-to-Leading-Order perturbative Quantum Chromodynamics calculation, obtained with the POWHEG method and interfaced with PYTHIA 6 for the generation of the parton shower, fragmentation, hadronisation and underlying event.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗
DOE OSTI2013

Fringe biasing: A variance reduction technique for optically thick meshes

Fringe biasing is a stratified sampling scheme applicable to Monte Carlo thermal radiation transport codes. The thermal emission source in optically thick cells is partitioned into separate contributions from the cell interiors (where the likelihood of the particles escaping the cells is virtually zero) and the 'fringe' regions close to the cell boundaries. Thermal emission in the cell interiors can now be modelled with fewer particles, the remaining particles being concentrated in the fringes so that they are more likely to contribute to the energy exchange between cells. Unlike other techniques for improving the efficiency in optically thick regions (such as random walk and discrete diffusion treatments), fringe biasing has the benefit of simplicity, as the associated changes are restricted to the sourcing routines with the particle tracking routines being unaffected. This paper presents an analysis of the potential for variance reduction achieved from employing the fringe biasing technique. The aim of this analysis is to guide the implementation of this technique in Monte Carlo thermal radiation codes, specifically in order to aid the choice of the fringe width and the proportion of particles allocated to the fringe (which are interrelated) in multi-dimensional simulations, and to confirm that the significant levels of variance reduction achieved in simulations can be understood by studying the behaviour for simple test cases. The variance reduction properties are studied for a single cell in a slab geometry purely absorbing medium, investigating the accuracy of the scalar flux and current tallies on one of the interfaces with the surrounding medium. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Methods to Observe Tribological Failures in Self-Mated Steel Contacts

Scuffing, a type of wear found in highly stressed or poorly lubricated contacts, is characterized by a rapid increase in friction and severe plastic deformation of the near-surface material. Scuffing has proven difficult to study because it initiates unpredictably, progresses rapidly, and typically develops within an inaccessible contact interface. Although there have been successful in-situ studies of scuffing in real-time, the transparent counter body needed for these studies changes the interactions between the surfaces and the lubricant, which affects the scuffing process in unknown ways. This paper describes the development of X-ray-compatible tribometry to study the scuffing of self-mated steels in-situ and in real-time. The method uses a crossed cylinders configuration with a thin (500 μm thick) stationary component and a small (≈200 μm) contact width to maximize X-ray interactions with atoms within the stress field generated by the contact. The resulting instrument and method are used to benchmark the scuffing response of self-mated 52,100 steel under tribologically challenging ‘oil-off’ lubrication conditions. The results demonstrate reliable scuffing in this configuration despite the relatively small contact areas and loads used. Following scuffing, gross plastic deformation was observed on both surfaces along with significant subsurface grain refinement and flow only on the stationary surface, which experienced constant contact. Interestingly, high friction initiated at specific locations of the migratory surface, which experienced intermittent contact, and then propagated across the track over time, suggesting that local conditions of the migratory surface dominated friction leading into the failure event.

36 MATERIALS SCIENCE↗
DOE OSTI2015

GPU Acceleration of Mean Free Path Based Kernel Density Estimators for Monte Carlo Neutronics Simulations

Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics for one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Mesoporous Thin Film Architectures: Addressing Material Demands through Molecular Self-Assembly

Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.

oxides↗
DOE OSTI2017

Combinatorial appraisal of transition states for in situ pair distribution function analysis

In situ total scattering measurements are increasingly utilized to follow atomic and nanoscale structural details of phase transitions and other transient processes in materials. This contribution presents an automated method and associated tool set to analyze series of diffraction and pair distribution function data with a linear combination of end-member states. It is demonstrated that the combinatorial appraisal of transition states (CATS) software tracks phase changes, relative phase fractions and length scales of interest in experimental data series. Here it is further demonstrated, using a series of local structure data simulations, that the misfit of such a model can reveal details of phase aggregation and growth related to the pair distribution function's sensitivity to interphase correlations. CATS may be applied to quantitative evaluation of many transient processes, including amorphous-to-crystalline phase transitions, the evolution of solid-solution behaviors, the precipitation and growth of aggregates, and other atomic to nanoscale details of crystallization and phase transformation phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Zgoubi Users' Guide

The computer code zgoubi calculates trajectories of charged particles in magnetic and electric fields. At the origin specially adapted to the definition and adjustment of beam lines and magnetic spectrometers, it has so evolved that it allows the study of systems including complex sequences of optical elements such as dipoles, quadrupoles, arbitrary multipoles, FFAG magnets and other magnetic or electric devices, and is able as well to handle periodic structures. Compared to other codes, it presents several peculiarities, as follows - a non-exhaustive list : • a numerical method for integrating the Lorentz equation, based on Taylor series, which optimizes computing time and provides high accuracy and strong symplecticity, • spin tracking, using the same numerical method as for the Lorentz equation, • account of stochastic photon emission, and its effects on particle dynamics, • calculation of the synchrotron radiation electric field and spectra in arbitrary magnetic fields, from the ray-tracing outcomes, • the possibility of using a mesh, which allows ray-tracing from simulated or measured (1-D, 2-D or 3-D) electric and magnetic field maps, • a number Monte Carlo procedures : unlimited number of trajectories, in-flight decay, stochastic radiation, etc., • built-in fitting procedures allowing arbitrary variables and a large variety of constraints, easily expandable, • multi-turn tracking in circular accelerators including features proper to machine parameter calculation and survey, • simulation of time-varying power supplies, and • simulation of arbitrary radio-frequency programs. The initial version of zgoubi was dedicated to ray-tracing in magnetic elements, beam lines, spectrometers. It was perfected for the purpose of studying, and operating, the four spectrometers SPES I, II, III, IV at the Laboratoire National SATURNE (CEA-Saclay, France), and, later, SPEG at GANIL (Caen, France). Developments regarding spin tracking and ray-tracing in combined electric and magnetic fields were implemented, in the late 1980s and early 1990s respectively. In the mid-1990s, the computation of synchrotron radiation electromagnetic impulse and spectra was introduced, for the purpose of synchrotron radiation diagnostic R&D at LEP, and further applied to the design of the SR diagnostics installations at LHC in the early 2000s. In the mean time, several new optical elements were added, such as electro-magnetic and other electrostatic lenses. Used since several years for special studies in periodic machines (e.g., SATURNE at Saclay, COSY at Julich, LEP and LHC at CERN), zgoubi has also undergone extensive developments regarding storage ring related features. Many developments have been accomplished since the early 2000s in the frame of a number of project design and beam dynamics studies, as the neutrino factory, lepton and hadron colliders, spin studies at AGS and RHIC, etc. As a consequence the list of optical elements and the compendium of numerical methods, so-called “Glossary of Keywords” list, pp. 7 and 169, has stretched with new simulation and computing procedures, ranging from fitting (FIT2 procedure ; additional constraints) to overlapping magnetic field capabilities (DIPOLES, FFAG), spin manipulation (SPINR), optical elements (BEAMBEAM), radiation damping tools (SRLOSS) and many others (FAISTORE, MAP2D-E, OPTICS, PICKUPS, TWISS, etc.). The graphic interface to zgoubi (zpop, Part D) has undergone extensive developments, making it a convenient companion tool to the use of zgoubi.

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Provenance-Enabling DOE Visualization Applications

VisTrails, Inc.’s goal main charter has been to develop provenance management solutions for software tools. As the use of computation increases, keeping track of computational processes and the data they consume and derive is essential for many applications. Provenance (also referred to as audit trail, lineage, and pedigree) captures information about the steps used to generate a given data product. Such information provides important documentation that is key to preserving the data, to determining the data’s quality and authorship, and to reproducing as well as validating the results. Given the growing complexity of the computational processes and the volume of data they manipulate, manual approaches to maintaining provenance are both tedious and error-prone. At VisTrails, Inc., we have implemented infrastructure that can be used to provenance-enable existing tools. We have developed a series of plugins for specific applications, including ParaView and VisIt, which enable these applications to capture provenance. While developing these plugins, we identified general patterns across different applications, and these were used to inform the design of a general framework: the VisTrails Provenance Software Development Kit (SDK). The SDK encapsulates a set of core functionality including methods for capture, navigation, analysis and re-use of provenance. It allows application or plugin developers to more effectively provide provenance support: they can use the core functionality and write code only to handle application specific components. The SDK includes features that can be used in any application, for example, a refined interface for browsing the intermediate and final results captured. While the application-specific plugins were written in Python, the SDK was written from scratch in C++. This has led not only to substantial gains in performance but also the ability to integrate the SDK with applications written in other languages, including Java. To ensure that the SDK supported all of the functionality in the application-specific plugins, we have rewritten some of the existing plugins using the SDK. The SDK can be downloaded from www.vistrals.com/sdk.html.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗