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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Hydrodynamic and Longitudinal Impedance Analysis of Cerebrospinal Fluid Dynamics at the Craniovertebral Junction in Type I Chiari Malformation

Elevated or reduced velocity of cerebrospinal fluid (CSF) at the craniovertebral junction (CVJ) has been associated with type I Chiari malformation (CMI). Thus, quantification of hydrodynamic parameters that describe the CSF dynamics could help assess disease severity and surgical outcome. In this study, we describe the methodology to quantify CSF hydrodynamic parameters near the CVJ and upper cervical spine utilizing subject-specific computational fluid dynamics (CFD) simulations based on in vivo MRI measurements of flow and geometry. Hydrodynamic parameters were computed for a healthy subject and two CMI patients both pre- and post-decompression surgery to determine the differences between cases. For the first time, we present the methods to quantify longitudinal impedance (LI) to CSF motion, a subject-specific hydrodynamic parameter that may have value to help quantify the CSF flow blockage severity in CMI. In addition, the following hydrodynamic parameters were quantified for each case: maximum velocity in systole and diastole, Reynolds and Womersley number, and peak pressure drop during the CSF cardiac flow cycle. The following geometric parameters were quantified: cross-sectional area and hydraulic diameter of the spinal subarachnoid space (SAS). The mean values of the geometric parameters increased post-surgically for the CMI models, but remained smaller than the healthy volunteer. All hydrodynamic parameters, except pressure drop, decreased post-surgically for the CMI patients, but remained greater than in the healthy case. Peak pressure drop alterations were mixed. To our knowledge this study represents the first subject specific CFD simulation of CMI decompression surgery and quantification of LI in the CSF space. Further study in a larger patient and control group is needed to determine if the presented geometric and/or hydrodynamic parameters are helpful for surgical planning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Integrate FARM with PID controllers: IES Simulation Ecosystem Control System Development

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM was designed to support the HERON software module in the solution of the optimal dispatch problem for IES units. As the result of HERON-FARM dispatch simulation, the set-point trajectories are optimized to meet constraints on both the production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and the process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.) at a coarse time resolution (every 10 or 100 seconds) over long time horizons (several days or weeks). In case the operational constraints need to be met at finer time resolution, the computational burden of HERON-FARM would linearly increase with the sampling rate, and sub-optimal solutions might be obtained. System responses characterized by overshoots and damped oscillations temporarily violating the imposed constraints might occur during abrupt power transients. In this report, a hierarchical control system architecture for the operation of the Dynamic Energy Technology and Integration Laboratory (DETAIL) facility constructed at INL was proposed. First, the preliminary studies on the proposed control strategy for operating the facility and the designed PI controllers were reviewed. In particular, the current approach for generating the set-point trajectories was studied, and its limits were identified. To this aim, the inclusion of a Supervisory Control layer embedding a modified version of the FARM algorithm for preserving the system safe operation over both long and real-time horizons was proposed. In this way, FARM would be applied twice, i.e., the original version (“FARM Validator”) aiding the solution of the power dispatch problem, and the modified version (“FARM Supervisory” coordinating the PI controllers to address the real-time control tasks. Despite the kernel of the two modules is the same algorithm, their roles, tasks, and capabilities are quite different. A detailed description of the role of FARM at addressing low-level control tasks is provided, along with tentative operational procedures for training the models embedded into the algorithm by using the collected experimental data.

42 ENGINEERING↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps

A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the b-jet, c-jet, and light-flavour jet classification probabilities from jet-tagging algorithms in simulation are derived for b-jets using $t\bar{t} \rightarrow e\mu \nu \nu bb$ data. After application of the derived calibration maps, closure between simulation and observation is achieved for jet flavour observables used in ATLAS analyses of Large Hadron Collider (LHC) Run 2 proton-proton collision data. This continuous calibration opens up new possibilities for the future use of jet flavour information in LHC analyses and also serves as a guide for deriving high-dimensional corrections to simulation via transportation maps, an important development for a broad range of inference tasks.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗
DOE OSTI2012

Petascale Data Storage Institute (Final Report)

pNFS is an extension to NFSv4 that helps clients overcome NFS scalability and performance barriers. Like NFS, pNFS is a client/server protocol implemented with secure and reliable remote procedure calls. pNFS departs from conventional NFS by allowing clients to access storage directly and in parallel. This helps overcome server bottlenecks inherent to NAS access methods. Making pNFS available to petascale researchers required coordinated progress in several dimensions, a confluence of multiple processes that took nearly ten years. The protocol had to be specified and published by the IETF. Nascent implementations had to track changes in the draft specification, changes in the Linux kernel, and interoperate with one another. The process by which modifications are accepted into the Linux kernel by maintainers and developers (and ultimately by Linus Torvalds himself) itself required consensus and compromise. Finally, pNFS support needed to be provided by the major Linux distributors (Red Hat, SUSE, etc.) and storage vendors (Netapp, EMC, IBM, Microsoft, etc.) on both the engineering and product sides. The specification requirement was met in December 2008, when the IETF approved the NFSv4.1 specification, which incorporates pNFS and pNFS file layouts, as a Proposed Standard as well as the (separate) specifications of pNFS object and block layouts. Throughout the PDSI project, CITI was the key contributor to the Linux-based, open source implementation of NFSv4.1 and pNFS. Considerable effort was devoted to the process of refining the IETF specification, which underwent dozens of preliminary drafts, rebasing implementations to the latest Linux kernel, which itself changed quarterly, and to testing interoperability with other developers. At last, the Linux pNFS implementation was incorporated into the Linux mainline kernel, although this was achieved after the PDSI project ended.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

WE-G-BRA-06: Application of Systems and Control Theory-Based Hazard Analysis to Radiotherapy

Purpose: The process of delivering radiation occurs in a complex socio-technical system heavily reliant on human operators. Furthermore, both humans and software are notoriously challenging to account for in traditional hazard analysis models. High reliability industries such as aviation have approached this problem through using hazard analysis techniques grounded in systems and control theory. The purpose of this work is to apply the Systems Theoretic Accident Model Processes (STAMP) hazard model to radiotherapy. In particular, the System-Theoretic Process Analysis (STPA) approach is used to perform a hazard analysis of a proposed on-line adaptive cranial radiosurgery procedure that omits the CT Simulation step and uses only CBCT for planning, localization, and treatment. Methods: The STPA procedure first requires the definition of high-level accidents and hazards leading to those accidents. From there, hierarchical control structures were created followed by the identification and description of control actions for each control structure. Utilizing these control structures, unsafe states of each control action were created. Scenarios contributing to unsafe control action states were then identified and translated into system requirements to constrain process behavior within safe boundaries. Results: Ten control structures were created for this new CBCT-only process which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Twenty three control actions were identified that contributed to over 80 unsafe states of those control actions resulting in over 220 failure scenarios. Conclusion: The interaction of people, hardware, and software are highlighted through the STPA approach. STPA provides a hierarchical model for understanding the role of management decisions in impacting system safety so that a process design requirement can be traced back to the hazard and accident that it is intended to mitigate. Varian Medical Systems, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

TU-H-CAMPUS-IeP1-03: Comparison of Monte Carlo Simulation and Conversion Factor Based Method On Estimation of Effective Dose in Pediatric Patients Undergoing Interventional Cardiac Procedures

Purpose: Interventional cardiac procedures utilize frequent fluoroscopy and cineangiography, which impose considerable radiation risk to patients, especially pediatric patients. Accurate calculation of effective dose is important in order to estimate cancer risk over the rest of their lifetime. This study evaluates the difference in effective dose calculated by Monte Carlo simulation with those estimated by locally-derived conversion factors (CF-local) and by commonly quoted conversion factors from Karambatsakidou et al (CF-K). Methods: Effective dose (E),of 12 pediatric patients, age between 2.5–19 years old, who had undergone interventional cardiac procedures, were calculated using PCXMC-2.0 software. Tube spectrum, irradiation geometry, exposure parameters and dose-area product (DAP) of each projection were included in the software calculation. Effective doses for each patient were also estimated by two Methods: 1) CF-local: conversion factor derived locally by generalizing results of 12 patients, multiplied by DAP of each patient gives E-local. 2) CF-K: selected factor from above-mentioned literature, multiplied by DAP of each patient gives E-K. Results: Mean of E, E-local and E-K were 16.01 mSv, 16.80 mSv and 22.25 mSv respectively. A deviation of −29.35% to +34.85% between E and E-local, while a greater deviation of −28.96% to +60.86% between E and EK were observed. E-K overestimated the effective dose for patients at age 7.5–19. Conclusion: Effective dose obtained by conversion factors is simple and quick to estimate radiation risk of pediatric patients. This study showed that estimation by CF-local may bear an error of 35% when compared with Monte Carlo calculation. If using conversion factors derived by other studies may result in an even greater error, of up to 60%, due to factors that are not catered for in the estimation, including patient size, projection angles, exposure parameters, tube filtration, etc. Users must be aware of these potential inaccuracies when simple conversion method is employed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-D-209-06: Head and Neck Tissue Dose From X-Ray Scatter to Physicians Performing Cardiovascular Procedures

Purpose: The purpose of this work was to characterize the spatial distribution of scatter radiation to the head and neck of a physician performing an x-ray interventional procedure and assess brain, eye lens, and carotid artery dose. Methods: Radiographic x-ray beams were tuned to match the peak energy (56 to 106 keV) and HVL (3.5 to 6.5 mm Al) of x-ray scatter originating from a patient during a fluoroscopic procedure. The radiographic beam was directed upon a Rando phantom from an inferior-left location to mimic a typical patient-operator geometric relationship. A lead-equivalent protective garment was secured to the phantom. Direct exposure Gafchromic film (XRQA2) was placed between the transverse plane layers of the head and neck region of the phantom and exposed with 4 scatter-equivalent radiographic beams. A 3×3 cm{sup 2} film placed at the left collar of the phantom was used to monitor incident dose in the position of a radiation monitoring badge. The films were converted to 2D dose distribution maps using FilmQA Pro software and an Epson 11000-XL scanner. The 2D dose distributions maps were normalized by the left collar dose and the percent of left collar dose (%LCD) was calculated for select tissues. Results: The dose maps had high dynamic range (10{sub 4}) and spatial detail. Considering all transverse planes and 4 scatter beam qualities, the median %LCD values were: whole brain 8.5%, left brain 13%, right brain 5.4%, left eye lens 67%, right eye lens 25%, left carotid artery 72%, and right carotid artery 28%. Conclusion: Scatter radiation dose to an operator can be simulated using a tuned radiographic beam and used to expose a phantom and Gafchromic film, thereby creating detailed 2D dose distribution maps. This work facilitates individualized estimation of dose to select head and neck tissues based on an operator’s radiation monitoring badge value.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-171: Robust Atlas Based Segmentation of the Prostate and Peripheral Zone Regions On MRI Utilizing Multiple MRI System Vendors

Purpose: Automatically generated prostate MRI contours can be used to aid in image registration with CT or ultrasound and to reduce the burden of contouring for radiation treatment planning. In addition, prostate and zonal contours can assist to automate quantitative imaging features extraction and the analyses of longitudinal MRI studies. These potential gains are limited if the solutions are not compatible across different MRI vendors. The goal of this study is to characterize an atlas based automatic segmentation procedure of the prostate collected on MRI systems from multiple vendors. Methods: The prostate and peripheral zone (PZ) were manually contoured by an expert radiation oncologist on T2-weighted scans acquired on both GE (n=31) and Siemens (n=33) 3T MRI systems. A leave-one-out approach was utilized where the target subject is removed from the atlas before the segmentation algorithm is initiated. The atlas-segmentation method finds the best nine matched atlas subjects and then performs a normalized intensity-based free-form deformable registration of these subjects to the target subject. These nine contours are then merged into a single contour using Simultaneous Truth and Performance Level Estimation (STAPLE). Contour comparisons were made using Dice similarity coefficients (DSC) and Hausdorff distances. Results: Using the T2 FatSat (FS) GE datasets the atlas generated contours resulted in an average DSC of 0.83±0.06 for prostate, 0.57±0.12 for PZ and 0.75±0.09 for CG. Similar results were found when using the Siemens data with a DSC of 0.79±0.14 for prostate, 0.54±0.16 and 0.70±0.9. Contrast between prostate and surrounding anatomy and between the PZ and CG contours for both vendors demonstrated superior contrast separation; significance was found for all comparisons p-value < 0.0001. Conclusion: Atlas-based segmentation yielded promising results for all contours compared to expertly defined contours in both Siemens and GE 3T systems providing fast and automatic segmentation of the prostate. Funding Support, Disclosures, and Conflict of Interest: AS Nelson is a partial owner of MIM Software, Inc. AS Nelson, and A Swallen are current employees at MIM Software, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Development of a Severe Accident Analysis Engine Using Approximate Reasoning

In hypothetical severe accident scenarios, nuclear power plant safety is challenged by core degradation and release of fission products. To aid the plant operator and technical support center staff, research has been performed to develop understanding of severe accident phenomenology, assessing their risk and devise measures to mitigate accident consequences (Sehgal, 2011). Nuclear power plant accidents involve highly complex physical and chemical interactions, a large number of safety related systems, and emergency operating procedures (EOP) and severe accident management guidelines (SAMG). Large number of factors with uncertainty and stress can overwhelm the operators. The overall objective of the present research is to develop a methodology and tools that an operator can use to predict severe accident progression and make effective decisions to arrest and manage the accident under large uncertainties. Towards this goal, this study examines a novel approach that brings to bear a combined use of techniques in artificial intelligence (Russel and Norvig, 2010), including Bayesian Network (BN), Answer Set Programming (ASP) and Fuzzy Inference (FI) to achieve faster-than-real-time, yet sufficiently accurate analysis of severe accident progression. Bayesian network (BN) is a type of probabilistic graphical models that can be used to represent knowledge and conditional dependencies about an uncertain realm. It is useful for capturing the causal relationships in a complex system. It also allows prediction via Bayesian inference (Kelly and Smith, 2012). It is composed of nodes and edges, which express random variables and probabilistic relationships between the nodes, respectively. This relationship, given by conditional probability is calculated using stochastic methods. The BN is used in several systems for analysis and management of accident risk; e.g. (Knochenhauer and Frid, 2010). In those systems, the nodes or so-called 'effective factors' are presumably known within the network by system descriptions in SAMGs. There are two steps to configure the BN, namely (i) identifying compositions within the network and (ii) defining conditional dependency between nodes. The first step is performed following the spirit of ASP (Baral, 2003) using Prolog (Wielemaker, 2005). Knowledge representation is used to build databases required for processing by ASP. Knowledge representation is defined as the expression of knowledge in a way that is understandable to both human and computers. Originally knowledge indicates data, information and facts. To focus, knowledge should be formed to achieve a goal. In a severe accident, key physics and safety related components status are defined according to a failure mode. The conditional dependencies between nodes are generated by approximate reasoning using Fuzzy inference (Zadeh, 1995). Fuzzy logic is a multiple-valued logic to treat ambiguous features such as linguistic description. It indicates possibility, which is distinguished from probability. Fuzzy set consists of linguistic variables according to the fixed rule. In other words, even uncertain states could be expressed as not binary logic, but as many-value logic by rule-based technology. Fuzzy control system is comprised of Fuzzy inference engine that is performed based on the 'If-Then Rule'. The 'If-Then Rule' is of the following form. If Y1 is B11 and Y2 is B12 then Z is C1. If Y1 is B21 and Y2 is B22 then Z is C2. Y1 and Y2 are inputs, which indicate conditional variables. Z indicates decision-making and both B and C are linguistic variables. With this rule-based technique, the approximate reasoning can be realized by Fuzzy inference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

SU-F-T-340: Direct Editing of Dose Volume Histograms: Algorithms and a Unified Convex Formulation for Treatment Planning with Dose Constraints

Purpose: To develop a procedure for including dose constraints in convex programming-based approaches to treatment planning, and to support dynamic modification of such constraints during planning. Methods: We present a mathematical approach that allows mean dose, maximum dose, minimum dose and dose volume (i.e., percentile) constraints to be appended to any convex formulation of an inverse planning problem. The first three constraint types are convex and readily incorporated. Dose volume constraints are not convex, however, so we introduce a convex restriction that is related to CVaR-based approaches previously proposed in the literature. To compensate for the conservatism of this restriction, we propose a new two-pass algorithm that solves the restricted problem on a first pass and uses this solution to form exact constraints on a second pass. In another variant, we introduce slack variables for each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints. We implement the proposed methods in Python using the convex programming package cvxpy in conjunction with the open source convex solvers SCS and ECOS. Results: We show, for several cases taken from the clinic, that our proposed method meets specified constraints (often with margin) when they are feasible. Constraints are met exactly when we use the two-pass method, and infeasible constraints are replaced with the nearest feasible constraint when slacks are used. Finally, we introduce ConRad, a Python-embedded free software package for convex radiation therapy planning. ConRad implements the methods described above and offers a simple interface for specifying prescriptions and dose constraints. Conclusion: This work demonstrates the feasibility of using modifiable dose constraints in a convex formulation, making it practical to guide the treatment planning process with interactively specified dose constraints. This work was supported by the Stanford BioX Graduate Fellowship and NIH Grant 5R01CA176553.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Multigrid Reduction in Time for Nonlinear Parabolic Problems: A Case Study

The need for parallelism in the time dimension is being driven by changes in computer architectures, where performance increases are now provided through greater concurrency, not faster clock speeds. This creates a bottleneck for sequential time marching schemes because they lack parallelism in the time dimension. Multigrid reduction in time (MGRIT) is an iterative procedure that allows for temporal parallelism by utilizing multigrid reduction techniques and a multilevel hierarchy of coarse time grids. MGRIT has been shown to be effective for linear problems, with speedups of up to 50 times. The goal of this work is the efficient solution of nonlinear problems with MGRIT, where efficiency is defined as achieving similar performance when compared to an equivalent linear problem. The benchmark nonlinear problem is the p-Laplacian, where p=4 corresponds to a well-known nonlinear diffusion equation and p=2 corresponds to the standard linear diffusion operator, our benchmark linear problem. The key difficulty encountered is that the nonlinear time-step solver becomes progressively more expensive on coarser time levels as the time-step size increases. To overcome such difficulties, multigrid research has historically targeted an accumulated body of experience regarding how to choose an appropriate solver for a specific problem type. To that end, this paper develops a library of MGRIT optimizations and modifications, most important an alternate initial guess for the nonlinear time-step solver and delayed spatial coarsening, that will allow many nonlinear parabolic problems to be solved with parallel scaling behavior comparable to the corresponding linear problem.

parareal↗
DOE OSTI2024

Visual feedback and guided balance training in an immersive virtual reality environment for lower extremity rehabilitation

Balance training is essential for physical rehabilitation procedures, as it can improve functional mobility and enhance cognitive coordination. However, conventional balance training methods may have limitations in terms of motivation, real-time objective feedback, and personalization, which a virtual reality (VR) setup may provide a better alternative. In this work, we present an immersive VR training environment for lower extremity balance rehabilitation with real-time guidance and feedback. The VR training environment immerses the user in a 3D ice rink model where a virtual coach (agent) leads them through a series of balance poses, and the user controls a trainee avatar with their own movements. Here we developed two coaching styles: positive-reinforcement and autonomous-supportive, and two viewpoints of the trainee avatar: first-person and third-person. The proposed environment was evaluated in a user study with healthy, non-clinical participants (n = 16, 24.4 ± 5.7 years old, 9 females). Our results show that participants showed stronger performance in the positive-reinforcement style compared to the autonomous-supportive style. Additionally, in the third-person viewpoint, the participants exhibited more stability in the positive-reinforcement style compared to the autonomous-supportive style. For viewpoint, participants exhibited stronger performance in the first-person viewpoint compared to third-person in the autonomous-supportive style, while they were comparable in the positive-reinforcement style. We observed no significant effects on the foot height and number of mistakes. Furthermore, we report the analysis of user performance with balance training poses and subjective measures based on questionnaires to assess the user experience, usability, and task load. The proposed VR balance training could offer an interactive, adaptive, and engaging environment and open new potential research directions for lower extremity rehabilitation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-T-204: Improving Multiple Isocenter Coincidence: Elekta Beam Modulator with HexaPOD Six Degrees Couchtop

Purpose: With evolution from frame-based to image-guided SRS/SRT technique, the coincidence of multiple isocenters must be within a tight tolerance: MV, kV, table mechanical, and, in this case, HexaPOD 6° patient positioning system. Reported here is a method for improving the effectiveness of an isocenter alignment procedure which ultimately led to isocenter coincidence on an Elekta Beam Modulator LINAC equipped with 4 mm MLC leaves and a fixed secondary jaw to within a radius of 1mm. Methods: Utilizing a self-leveling laser, the Elekta ball (BB) bearing phantom (8mm steel ball), the Modus QA Isocenter Cube software, and a colleague-provided isocenter alignment method, the kV, table mechanical, and HexaPOD isocenters were localized to the MV isocenter. Isocenter coincidence was tested using the Isocenter Cube from Modus QA, brought to kV isocenter using CBCT and the HexaPOD system. MV images were taken at various gantry, collimator, and couch angles and then analyzed with the aforementioned software to determine coincidence of the 5mm steel ball at cube center with MV isocenter. To improve overall coincidence, two errors were addressed iteratively: (1) LINAC mechanical and radiation isocenter coincidence allowing each isocenter volume to have similar variations during gantry and collimator rotation and (2) collimator rotational walkout which reduced the size of both mechanical and radiation isocenters. Beam steering was performed to bring MV isocenter closer to mechanical and collimator walkout was adjusted by physically shifting the MLC bank. Initial procedures were repeated to align all isocenters and perform regular checks for consistency. Results: Post-initial alignment revealed a maximum diameter of 1.8mm for MV isocenter and maximum isocenter alignment error of up to 1.22mm radius which improved to roughly 1.0mm MV isocenter diameter and <0.71mm radius alignment error with a consistency within 0.25mm. Conclusion: With the methods described, all isocenters were brought into alignment within a radius of 1mm.

07 ISOTOPE AND RADIATION SOURCES↗