Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “machining”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-E-13: Design and Fabrication of Gynacological Brachytherapy Shielding & Non Shielding Applicators Using Indigenously Developed 3D Printing Machine

Purpose: In this innovative work we have developed Gynecological Brachytherapy shielding & Non Shielding Applicators and compared with the commercially available applicators by using the indigenously developed 3D Printing machine. Methods: We have successfully indigenously developed the 3D printing machine. Which contain the 3 dimensional motion platform, Heater unit, base plate, ect… To fabricate the Gynecological Brachytherapy shielding & non shielding applicators the 3D design were developed in the computer as virtual design. This virtual design is made in a CAD computer file using a 3D modeling program. Separate programme for the shielding & non shielding applicators. We have also provided the extra catheter insert provision in the applicator for the multiple catheter. The DICOM file of the applicator were then converted to stereo Lithography file for the 3D printer. The shielding & Non Shielding Applicators were printed on a indigenously developed 3D printer material. The same dimensions were used to develop the applicators in the acrylic material also for the comparative study. A CT scan was performed to establish an infill-density calibration curve as well as characterize the quality of the print such as uniformity and the infill pattern. To commission the process, basic CT and dose properties of the printing materials were measured in photon beams and compared against water and soft tissue. Applicator were then scanned to confirm the placement of multiple catheter position. Finally dose distributions with rescanned CTs were compared with those computer-generated applicators. Results: The doses measured from the ion Chamber and X-Omat film test were within 2%. The shielded applicator reduce the rectal dose comparatively with the non shielded applicator. Conclusion: As of submission 3 unique cylinders have been designed, printed, and tested dosimetrically. A standardizable workflow for commissioning custom 3D printed applicators was codified and will be reported.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-375: Optimization of a New Co-60 Machine for Intensity Modulated Radiation Therapy: A Monte Carlo Characterization Study

Purpose: To provide a wide range of dose output for intensity modulation purposes while minimizing the beam penumbra for a new rotating cobalt therapy system. The highest dose rate needs to be maximized as well. Methods: The GEPTS Monte Carlo system is used to calculate the dose distribution from each tested Co-60 head for a wide range of field sizes (1×1 to 40×40 cm2). This includes the transport of photons (and secondary electrons) from the source through the collimation system (primary collimator, Y and × jaws, and MLCs) and finally in the water phantom. Photon transport includes Compton scattering (with electron binding effect), Rayleigh scattering, Photoelectric effect (with detailed simulation of fluorescence x-rays). Calculations are done for different system designs to reduce geometric penumbra and provide dose output modulation. Results: Taking into account different clinical requirements, the choice of a movable head (SAD = 70 to 80 cm) is made. The 120-leaf MLC (6-cm thick) entrance is at 32 cm from the bottom of the source (to reduce penumbra while allowing larger patient clearance). Three system designs (refereed here as S1–3) were simulated with different effective source sizes (2mm, 10mm and 17mm diameter). The effective point source is at mid-height of the 25-mm-long source. Using a 12000-Ci source, the designed Co-60 head can deliver a wide range of dose outputs (0.5 − 4 Gy/mn). A dose output of 2.2 Gy/mn can be delivered for a 10cm × 10cm field size with 1-cm penumbra using a 10mm effective source. Conclusion: A new 60Co-based VMAT machine is designed to meet different clinical requirements in term of dose output and beam penumbra. Outcomes from this study can be used for the design of 60Co machines for which a renewed interest is seen.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-462: Lessons Learned From a Machine Incident Reporting System

Purpose: Linear accelerators must operate with minimal downtime. Machine incident logs are a crucial tool to meet this requirement. They providing a history of service and demonstrate whether a fix is working. This study investigates the information content of a large department linear accelerator incident log. Methods: Our department uses an electronic reporting system to provide immediate information to both key department staff and the field service department. This study examines reports for five linac logs during 2015. The report attributes for analysis include frequency, level of documentation, who solved the problem, and type of fix used. Results: Of the reports, 36% were documented as resolved. In another 25% the resolution allowed treatment to proceed although the reported problem recurred within days. In 5% only intermediate troubleshooting was documented. The remainder lacked documentation. In 60% of the reports, radiation therapists resolved the problem, often by clearing the appropriate faults or reinitializing a software or hardware service. 22% were resolved by physics and 10% by field service engineers. The remaining 8% were resolved by IT, Facilities, or resolved spontaneously. Typical fixes, in order of scope, included clearing the fault and moving on, closing and re-opening the patient session or software, cycling power to a sub-unit, recalibrating a device (e.g., optical surface imaging), and calling in Field Service (usually resolving the problem through maintenance or component replacement). Conclusion: The reports with undocumented resolution represent a missed opportunity for learning. Frequency of who resolves a problem scales with the proximity of the person’s role (therapist, physicist, or service engineer), which is inversely related to the permanence of the resolution. Review of lessons learned from machine incident logs can form the basis for guidance to radiation therapists and medical physicists to minimize equipment downtime and ensure safe operation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-G-TeP2-04: Comprehensive Machine Isocenter Evaluation with Separation of Gantry, Collimator, and Table Variables

Purpose: To develop and demonstrate application of a method that characterizes deviation of linac x-ray beams from the centroid of the volumetric radiation isocenter as a function of gantry, collimator, and table variables. Methods: A set of Winston-Lutz ball-bearing images was used to determine the gantry radiation isocenter as the midrange of deviation values resulting from gantry and collimator rotation. Also determined were displacement of table axis from gantry isocenter and recommended table axis adjustment. The method, previously reported, has been extended to include the effect of collimator walkout by obtaining measurements with 0 and 180 degree collimator rotation for each gantry angle. Twelve images were used to characterize the volumetric isocenter for the full range of available gantry, collimator, and table rotations. Results: Three Varian True Beam, two Elekta Infinity and four Versa HD linacs at five institutions were tested using identical methodology. Varian linacs exhibited substantially less deviation due to head sag than Elekta linacs (0.4 mm vs. 1.2 mm on average). One linac from each manufacturer had additional isocenter deviation of 0.3 to 0.4 mm due to jaw instability with gantry and collimator rotation. For all linacs, the achievable isocenter tolerance was dependent on adjustment of collimator position offset, transverse position steering, and alignment of the table axis with gantry isocenter, facilitated by these test results. The pattern and magnitude of table axis wobble vs. table angle was reproducible and unique to each machine. Conclusion: This new method provides a comprehensive set of isocenter deviation values including all variables. It effectively facilitates minimization of deviation between beam center and target (ball-bearing) position. This method was used to quantify the effect of jaw instability on isocenter deviation and to identify the offending jaw. The test is suitable for incorporation into a routine machine QA program. Software development was performed by Radiological Imaging Technology, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation

Purpose: To validate a machine learning approach to Virtual IMRT QA for accurately predicting gamma passing rates using different QA devices at different institutions. Methods: A Virtual IMRT QA was constructed using a machine learning algorithm based on 416 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3mm with 10% threshold. An independent set of 139 IMRT measurements from a different institution, with QA data based on portal dosimetry using the same gamma index and 10% threshold, was used to further test the algorithm. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results: In addition to predicting passing rates with 3% accuracy for all composite plans using diode-array detectors, passing rates for portal dosimetry on per-beam basis were predicted with an error <3.5% for 120 IMRT measurements. The remaining measurements (19) had large areas of low CU, where portal dosimetry has larger disagreement with the calculated dose and, as such, large errors were expected. These beams need to be further modeled to correct the under-response in low dose regions. Important features selected by Lasso to predict gamma passing rates were: complete irradiated area outline (CIAO) area, jaw position, fraction of MLC leafs with gaps smaller than 20 mm or 5mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted Average Irregularity Factor, duty cycle among others. Conclusion: We have demonstrated that the Virtual IMRT QA can predict passing rates using different QA devices and across multiple institutions. Prediction of QA passing rates could have profound implications on the current IMRT process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-AB-BRA-12: Quality Assurance of An Integrated Magnetic Resonance Image Guided Adaptive Radiotherapy Machine Using Cherenkov Imaging

Purpose: To investigate the viability of using Cherenkov imaging as a fast and robust method for quality assurance tests in the presence of a magnetic field, where other instruments can be limited. Methods: Water tank measurements were acquired from a clinically utilized adaptive magnetic resonance image guided radiation therapy (MR-IGRT) machine with three multileaf-collimator equipped 60Co sources. Cherenkov imaging used an intensified charge coupled device (ICCD) camera placed 3.5m from the treatment isocenter, looking down the bore of the 0.35T MRI into a water tank. Images were post-processed to make quantitative comparison between Cherenkov light intensity with both film and treatment planning system predictions, in terms of percent depth dose curves as well as lateral beam profile measurements. A TG-119 commissioning test plan (C4: C-Shape) was imaged in real-time at 6.33 frames per second to investigate the temporal and spatial resolution of the Cherenkov imaging technique. Results: A .33mm/pixel Cherenkov image resolution was achieved across 1024×1024 pixels in this setup. Analysis of the Cherenkov image of a 10.5×10.5cm treatment beam in the water tank successfully measured the beam width at the depth of maximum dose within 1.2% of the film measurement at the same point. The percent depth dose curve for the same beam was on average within 2% of ionization chamber measurements for corresponding depths between 3–100mm. Cherenkov video of the TG-119 test plan provided qualitative agreement with the treatment planning system dose predictions, and a novel temporal verification of the treatment. Conclusions: Cherenkov imaging was successfully used to make QA measurements of percent depth dose curves and cross beam profiles of MRI-IGRT radiotherapy machines after only several seconds of beam-on time and data capture; both curves were extracted from the same data set. Video-rate imaging of a dynamic treatment plan provided new information regarding temporal dose deposition. This study has been funded by NIH grants R21EB17559 and R01CA109558, as well as Norris Cotton Cancer Center Pilot funding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors

Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describing relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. Finally, these results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

One-component order parameter in URu 2 Si 2 uncovered by resonant ultrasound spectroscopy and machine learning

The unusual correlated state that emerges in URu 2 Si 2 below T HO = 17.5 K is known as “hidden order” because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are “hidden.” We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T HO . We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems.

42 ENGINEERING↗
DOE OSTI2022

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

58 GEOSCIENCES↗
DOE OSTI2024

Machine learning–based extreme event attribution

The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning–based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Scalable multiplexed machine learning gas sensor chips for food classification

Multiplexed gas sensor arrays combined with machine learning have unlocked previously inaccessible applications for scent-based sensing. Current platforms are limited by overlapping sensing materials with similar compositions, leading to highly correlated responses, or multistep deposition processes that hinder scalability. In this work, we developed a 16-element monolithic chip with fully distinct sensing layers, enabling a truly heterogeneous array. The system consists of highly sensitive carbon nanotube field effect transistors that are functionalized through a single-step microdispensing method compatible with automated pipetting systems. The resulting chip produces characteristic signal patterns in response to object-specific scent profiles and, when combined with machine learning algorithms, can perform automated object identification. We demonstrate the classification of 16 different objects, including food spoilage and nut allergens, with a 92.6% overall prediction accuracy.

Bassil, Carla [University of California, Berkeley,↗
DOE OSTI2025

Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a "sniff-and-seek" mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus-molybdenum disulfide (bP-MoS2) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

Zhang, Dehui↗
DOE OSTI2013

Physical meaning of one-machine and multimachine tokamak scalings

Specific features of energy confinement scalings constructed using different experimental databases for tokamak plasmas are considered. In the multimachine database, some pairs of engineering variables are collinear; e.g., the current I and the input power P both increase with increasing minor radius a. As a result, scalings derived from this database are reliable only for discharges in which such ratios as I/a{sup 2} or P/a{sup 2} are close to their values averaged over the database. The collinearity of variables allows one to exclude the normalized Debye radius d* from the scaling expressed in a nondimensional form. In one-machine databases, the dimensionless variables are functionally dependent, which allow one to cast a scaling without d*. In a database combined from two devices, the collinearity may be absent, so the Debye radius cannot generally be excluded from the scaling. It is shown that the experiments performed in support of the absence of d* in the two-machine scaling are unconvincing. Transformation expressions are given that allow one to compare experiments for the determination of scaling in any set of independent variables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Supercomputer assisted generation of machine learning agents for the calibration of building energy models. In: XSEDE '13 Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, Article No. 22

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the "Autotune" research which employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.

Sanyal, Jibonananda↗
DOE OSTI2019

Machine learning based design space exploration for hybrid main-memory design

We develop a machine learning (ML) based design space exploration (DSE) method that builds predictive models for various responses of a hybrid main-memory system. To overcome the challenges associated with latency, capacity, and power of memory systems in future extreme-scale machines, the hybrid memory architectures are being considered in which novel non-volatile memory (NVM) systems augment the traditional DRAM. However, way before their actual design and implementation, these emerging hybrid memory systems need to be simulated and analyzed to fully understand their capabilities and limitations. As the conventional architectural-level memory simulators require significant amounts of computational costs and time, we propose to utilize ML techniques for developing various memory-response models that can instantly provide a predicted response corresponding to any new memory configuration. Specifically, in this work, we apply four supervised ML techniques to build regression models for memory latency, bandwidth, power, and total read/write responses. The training and validation data for the ML methods are generated using NVMain memory simulator for DRAM, NVM, and their hybrid combinations. We demonstrate the results of the ML based memory-DSE method in terms of the learning curve characteristics for hyperparameter tuning and the statistical error analyses of the designed predictive models.

Sen, Satyabrata↗
DOE OSTI2020

How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?

We investigated the effects of different visual explanations on users' trust in machine learning classification. We proposed three forms of visual explanations of a classification based on identifying relevant training instances. We conducted a user study to evaluate these visual explanations as well as a no explanation condition. We measured users' trust of a classifier, quantified the effects of these three forms of explanations, and assessed the changes in users' trust. We found that participants trust a classifier appropriately when an explanation is available. The combination of human, classification algorithm and understandable explanation makes better decisions than the classifier and human alone.This work advances the state-of-the-art closer to building trust-able machine learning models and informs the design and appropriate use of automated systems.

Yang, Fumeng↗