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Nickel-plated device, with machined recesses matching the multipin pattern of particular circuit module, facilitates repairs to electronic systems and reduces chance of damage to adjacent components. Nickel-plating reduces oxidation and scaling. Recesses retain sufficient amount of molten solder to uniformly wet pins for simultaneous heating and extraction.
Humans bring unique capabilities to space systems and contribute to mission success in a manner that cannot be matched by machines. Nevertheless, from time to time, human error can present a threat to system performance, and system designers must anticipate and manage this risk. NASA’s Human-Rating Requirements for Space Systems call for program managers to conduct a human error analysis (HEA) during system development but does not specify how to do this. In 2018, NASA’s Engineering and Safety Center asked the authors to develop a guidance document on HEA. The resulting position paper outlines a suggested method for HEA and makes it clear that error analysis is about identifying and mitigating problems at a system level, and not about finding fault with individuals. Error management strategies must be directed at error-producing conditions, thereby reducing the likelihood of human error, while retaining the positive contribution that humans make to system operations.
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Purpose: To commission electron Monte Carlo (eMC) algorithm in Eclipse Treatment Planning System (TPS) for TrueBeam Linacs, including the evaluation of dose calculation accuracy for small fields and oblique beams and comparison with the existing eMC model for Clinacs. Methods: Electron beam percent-depth-dose (PDDs) and profiles with and without applicators, as well as output factors, were measured from two Varian TrueBeam machines. Measured data were compared against the Varian TrueBeam Representative Beam Data (VTBRBD). The selected data set was transferred into Eclipse for beam configuration. Dose calculation accuracy from eMC was evaluated for open fields, small cut-out fields, and oblique beams at different incident angles. The TrueBeam data was compared to the existing Clinac data and eMC model to evaluate the differences among Linac types. Results: Our measured data indicated that electron beam PDDs from our TrueBeam machines are well matched to those from our Varian Clinac machines, but in-air profiles, cone factors and open-filed output factors are significantly different. The data from our two TrueBeam machines were well represented by the VTBRBD. Variations of TrueBeam PDDs and profiles were within the 2% /2mm criteria for all energies, and the output factors for fields with and without applicators all agree within 2%. Obliquity factor for two clinically relevant applicator sizes (10×10 and 15×15 cm{sup 2}) and three oblique angles (15, 30, and 45 degree) were measured for nominal R100, R90, and R80 of each electron beam energy. Comparisons of calculations using eMC of obliquity factors and cut-out factors versus measurements will be presented. Conclusion: eMC algorithm in Eclipse TPS can be configured using the VTBRBD. Significant differences between TrueBeam and Clinacs were found in in-air profiles and open field output factors. The accuracy of the eMC algorithm was evaluated for a wide range of cut-out factors and oblique incidence.
Purpose: This study compares gamma passing rates for a cohort of similar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA) plans to determine the equivalence of the patient specific QA plan delivery. The passing criterion is 90% gamma index with 3% dose difference (DD), 3mm distance-to-agreement (DTA) and a dose threshold of 10%. Methods: Gamma passing results of previously treated IMRT QA plans, delivered to Sun Nuclear MapCheck phantom, and VMAT QA plans, delivered to ScandiDos Delta4 phantom, are organized by anatomical site and treatment machine. Both Varian and Elekta machines are included. Pairs of IMRT and VMAT plans are matched based on site, machine, and PTV volume to ensure similar plan cohorts. A two-tailed t-test analysis of the data with an alpha of 0.05 determines if there exists a statistically significant difference. Power was calculated to detect a difference of 4%; all data sets were strong with above a 0.8 power. Results: The first data set consisting of 20 matched pairs of prostate plans was statistically insignificant (p-value=0.90, Power=0.99). The 14 matched pairs set of head and neck plans has a statistically significant Result (p-value=0.028, Power=0.88). The head and neck IMRT gamma indexes have a mean of 93.1% and range of 82%-100% while the VMAT gamma indexes have a mean of 96.7% and range of 92%-100%. The two combined data sets of matched plans had a statistically insignificant Result (p-value=0.073, Power=0.99). Conclusion: Overall, IMRT and VMAT have equivalent passing rates when comparing the gamma analysis using a passing criterion of 3% DD and 3mm DTA. When separated by site, prostate IMRT and VMAT plans have equivalent passing rates while head and neck plans have a statistically significant variation of passing rates. The passing rates for the two modalities are independent of delivery machine for matched PTV target volumes.
The implementation of a reverse time migration algorithm on the Connection Machine, a massively parallel computer is described. Essential architectural features of this machine as well as programming concepts are presented. The data structures and parallel operations for the implementation of the reverse time migration algorithm are described. The algorithm matches the Connection Machine architecture closely and executes almost at the peak performance of this machine.
Purpose: Flattening filter free (FFF) linear accelerators can increase treatment efficiency and plan quality. There are multiple methods of defining a FFF beam. The Elekta control system supports tuning of the delivered FFF beam energy to enable matching of the percentage depth-dose (PDD) of the flattened beam at 10 cm depth. This is compared to FFF beams where the linac control parameters are identical to those for the flattened beam. All beams were delivered on an Elekta Synergy accelerator with an Agility multi-leaf collimator installed and compared to the standard, flattened beam. The aim of this study is to compare “matched” FFF beams to both “unmatched” FFF beams and flattened beams to determine the benefits of matching beams. Methods: For the three modes of operation 6 MV flattened, 6 MV matched FFF, 6 MV unmatched FFF, 10 MV flattened, 10 MV matched FFF, and 10 MV unmatched FFF beam profiles were obtained using a plotting tank and were measured in steps of 0.1 mm in the penumbral region. Beam penumbra was defined as the distance between the 80% and 20% of the normalized dose when the inflection points of the unflattened and flattened profiles were normalized with the central axis dose of the flattened field set as 100%. PDD data was obtained at field sizes ranging from 3 cm × 3 cm to 40 cm × 40 cm. Radiation protection measurements were additionally performed to determine the head leakage and environmental monitoring through the maze and primary barriers. Results: No significant change is made to the beam penumbra for FFF beams with and without PDD matching, the maximum change in penumbra for a 10 cm × 10 cm field was within the experimental error of the study. The changes in the profile shape with increasing field size are most significant for the matched FFF beam, and both FFF beams showed less profile shape variation with increasing depth when compared to flattened beams, due to consistency in beam energy spectra across the radiation field. The PDDs of the FFF beams showed less variation with field size, the d{sub max} value was deeper for the matched FFF beam than the FFF beam and deeper than the flattened beam for field sizes greater than 5 cm × 5 cm. The head leakage when using the machine in FFF mode is less than half that for a flattened beam, but comparable for both FFF modes. The radiation protection dose-rate measurements show an increase of instantaneous dose-rates when operating the machines in FFF mode but that increase is less than the ratio of MU/min produced by the machine. Conclusions: The matching of a FFF beam to a flattened beam at a depth of 10 cm in water by increasing the FFF beam energy does not reduce any of the reported benefits of FFF beams. Conversely, there are a number of potential benefits resulting from matching the FFF beam; the depth of maximum dose is deeper, the out of field dose is potentially reduced, and the beam quality and penetration more closely resembles the flattened beams currently used in clinical practice, making dose distributions in water more alike. Highlighted in this work is the fact that some conventional specifications and methods for measurement of beam parameters such as penumbra are not relevant and further work is required to address this situation with respect to “matched” FFF beams and to determine methods of measurement that are not reliant on an associated flattened beam.
Expressions for transverse emittance growth due to turn-by-turn passes through the H-minus stripping foil in Booster are developed here from simple principles of statistics and simple assumptions about the initial distribution of particles incident on the foil. These are meant to complement work already presented by Zeno [1, 2, 3] and Brown [4]. The expressions show that while the average emittance hEi of the distribution simply increases linearly with turn number, the emittance E based on the mean square particle position does so with an additional oscillatory term that depends on the machine tune. It is shown that this term can be ignored as long as the turn number is su ciently large and the tune is su ciently far from integer and half-integer values. Under these conditions the relation between hEi and E is simply hEi = 2 E. This relation is shown to hold for a Gaussian distribution that is matched to the machine lattice. Two symmetry conditions which help characterize the particle distribution are identi ed. These provide justi cation for calling E an emittance. It is shown that if the conditions are satis ed by the initial distribution, they will not be satis ed after a single traversal of the foil and one turn around the machine. However, on subsequent turns the distribution can (and does) return to satisfying the conditions. Moreover, for su ciently large turn number, the symmetry conditions are approximately satis ed. As already noted in [4], the emittance growth per turn is proportional to the lattice beta at the foil and the mean square angular kick received by protons passing through the foil. We take the former to be 5 m. The latter is obtained from simulations performed with the code TRIM [5]. Having these numbers in hand, actual numbers for emittance growth are presented. The reader may wish to start with Section 11 and refer to previous sections as needed or desired.
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....
Impact sensitivity testing was performed using a modified Bureau of Mines (MBOM) impactor manufactured by Safety Management Services, Inc., shown in Figure 1. Type-12 tooling was utilized on this machine with a 2.5kg impactor and matching intermediate mass. This particular machine is capable of a maximum drop height of 115cm with 0.1cm increments, though 1cm increments are typically used. Sample material was placed (35 ± 2mg) onto 1 inch squares of Norton brand 180A Garnet sandpaper. Positive results were detected visually or audibly by the operator as smoke, flash, report, charring/tearing of the sandpaper, etc.
Impact sensitivity testing was performed using a modified Bureau of Mines (MBOM) impactor manufactured by Safety Management Services, Inc. Type-12 tooling was utilized on this machine with a 2.5kg impactor and matching intermediate mass. This particular machine is capable of a maximum drop height of 115cm with 0.1 cm increments, though 1 cm increments are typically used. Sample material was placed (35 ± 2mg) onto 1 inch squares of Norton brand 180A Garnet sandpaper. Positive results were detected visually or audibly by the operator as smoke, flash, report, charring/tearing of the sandpaper, etc. The test was conducted using the Bruceton technique to calculate the height at which there is a 50% chance of initiation (H 50 ).
Impact sensitivity testing was performed using a modified Bureau of Mines (MBOM) impactor manufactured by Safety Management Services, Inc. Type-12 tooling was utilized on this machine with a 2.5kg impactor and matching intermediate mass. This particular machine is capable of a maximum drop height of 115cm with 0.1cm increments, though 1cm increments are typically used. Sample material was placed (35 ± 2mg) onto 1 inch squares of Norton brand 180A Garnet sandpaper. Due to the reactive nature of the peroxide in these mixtures, contact between the sample and sandpaper was minimized (<10s) prior to impact. Positive results were detected visually or audibly by the operator as smoke, flash, report, tearing of the sandpaper, etc. The test was conducted using the Bruceton technique to calculate the height at which there is a 50% chance of initiation (H 50 ).
The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying nonequilibrium control costs, leading to a collection of thermodynamic speed limits, which rely on the assumption that the target probability distribution is perfectly realized. This is almost never the case in experiments or numerical simulations, so here we address the situation in which the external controller is imperfect. We obtain a lower bound for the dissipated work in generic nonequilibrium control problems that (1) is asymptotically tight and (2) matches the thermodynamic speed limit in the case of optimal driving. Along with analytically solvable examples, we refine this imperfect driving notion to systems in which the controlled degrees of freedom are slow relative to the nonequilibrium relaxation rate, and identify independent energy contributions from fast and slow degrees of freedom. Furthermore, we develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. Furthermore, this latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols we learn saturate the bound.
Modular line block comprises matched modular elements machined to accept fluid lines of different diameters. Modules can support different fluid-line configurations. Top and bottom surfaces are machined to accept dovetail strip used for holding modules together. End modules have holes drilled through to accept fastening screws.
Scientists and engineers constantly face new challenges, despite myriad advances in computing. More sets of data are collected today from earth and sky than there is time or resources available to carefully analyze them. Some problems either don't have fast algorithms to solve them or have solutions that must be found among millions of options, a situation akin to finding a needle in a haystack. But all hope is not lost: advances in technology and the Internet have empowered the general public to participate in the scientific process via individual computational resources and brain cognition, which isn't matched by any machine. Citizen scientists are volunteers who perform scientific work by making observations, collecting and disseminating data, making measurements, and analyzing or interpreting data without necessarily having any scientific training. In so doing, individuals from all over the world can contribute to science in ways that wouldn't have been otherwise possible.
This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introduce generative models which are, by construction, confined at every step of their sampling trajectory to the manifold of massless N-particle Lorentz-invariant phase space in the center-of-momentum frame. In the case of diffusion models, the "pure noise" forward process endpoint corresponds to the uniform distribution on phase space, which provides a clear starting point from which to identify how correlations among the particles emerge during the reverse (de-noising) process. We demonstrate that our models are able to learn both few-particle and many-particle distributions with various singularity structures, paving the way for future interpretability studies using generative models trained on simulated jet data.