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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
NASA NTRS2014

Variations in Modeled Dengue Transmission over Puerto Rico Using a Climate Driven Dynamic Model

Dengue fever is a mosquito‐borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Because of variations in topography, ocean influences and atmospheric processes, temperature and rainfall patterns vary across Puerto Rico and so do dengue virus transmission rates. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input, ground‐based observations for temperature input, and laboratory confirmed dengue cases reported by the Centers for Disease Control and Prevention for parameter calibration, we modeled dengue transmission at the county level across Puerto Rico from 2010‐2013 using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations for each county in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. The top 1% of model simulations that best reproduced the reported dengue case data were then analyzed to determine the most important parameters for dengue virus transmission in each county, as well as the relative influence of climate variability on transmission. These results can be used by public health workers to implement dengue control methods that are targeted for specific locations and climate conditions.

Morin, Cory↗
DOE OSTI2019

Managing an Aging Pump-and-Treat Groundwater Remediation System at the Fernald Preserve - 19557

The Fernald Preserve, Ohio, Site underwent restoration and remediation under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). With the exception of the Great Miami Aquifer, the CERCLA remediation was declared physically complete on October 29, 2006, and the site was turned over to the US Department of Energy Office of Legacy Management for long-term care and maintenance. The Record of Decision under CERCLA specifies a concentration-based cleanup that requires the extraction and treatment of Great Miami Aquifer groundwater above Final Remediation Levels (FRLs) until the full, beneficial use potential of the aquifer is achieved, including use as a drinking water source. The area of the aquifer targeted for remediation is 126.5 hectares (312.70 acres). The groundwater pumping remediation was designed by modeling cleanup with the Variable Saturated Analysis Model in 3 Dimensions (VAM-3D) modeling code, developed by Hydrogeologic Inc. The original design called for 23 extraction wells operating at 379 liters per minute (lpm) (100 gallons per minute [gpm]) to 1135 lpm (300 gpm) each for a system total of 18,073 lpm (4775 gpm). In 2014, the system was optimized down to 20 extraction wells operating at 379 to 1893 lpm each for a system total of 19,180 lpm (5075 gpm). To date, pumping has been an effective remediation technology for the aquifer. At the end of 2017, the area of the aquifer remaining above FRLs was down to 38.2 hectares (94.4 acres). From August 1993 through December 2017 (24 years), 6235 kilograms (kg) (13,733 pounds) of uranium were removed from the aquifer by the pumping of 1.74 x 10{sup 11} liters (46.2 billion gallons) of water. Since 2010, treatment of groundwater to achieve regulatory discharge limits at the Great Miami River has not been needed on a routine basis. The ongoing remediation is now essentially a pumping operation, and this water does not require treatment. Metrics reported in the annual Site Environmental Report for the groundwater remediation indicate that, based on the model-predicted estimate of the mass of uranium that needs to be removed from the aquifer to achieve a concentration-based cleanup, the cleanup is approximately 86% - 87% complete. Current model predictions indicate that pumping will need to continue an additional 16 years (until 2035) in some of the wells. During the additional 16 years, it is estimated that approximately 1012 kg (2231 pounds) of uranium will be removed from the aquifer. System pumping rates are designed to decrease over the course of the remaining 16 years, dropping down to a system total of 11,638 lpm (3075 gpm) in 2022 and a system total of 4163 lpm (1100 gpm) in 2033. Even with the designed decreases in pumping rates, it is estimated that approximately 1 x 10{sup 11} liters (26.6 billion gallons) of water will be pumped from the aquifer to remove 1012 kg (2231 pounds) of uranium. This indicates that the remaining 16 years of operation will be much less efficient than the first 24 years. Decreasing efficiency is common for pumping operations. The worst-case scenario is that decreasing concentration trends stabilize above the cleanup FRLs and remediation goals are never achieved. Performance assessments of the pumping remedy will need to be routinely conducted to determine if pumping should be discontinued, if it should be continued but optimized, or if the pumping technology can be supplemented with a different technology that will improve the outcome of the remediation and make the operation more efficient and successful. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Unlocking Dysprosium Constraints for China’s 1.5 °C Climate Target

Some key low-carbon technologies, ranging from wind turbines to electric vehicles, are underpinned by the strong rare-earth-based permanent magnets of the Nd, Pr (Dy)–Fe–Nb type (NdFeB). These NdFeB magnets, which are sensitive to demagnetization with temperature elevation (the Curie point), require the addition of variable amounts of dysprosium (Dy), where an elevation of the Curie point is needed to meet operational conditions. Given that China is the world’s largest REE supplier with abundant REE reserves, the impact of an ambitious 1.5 °C climate target on China’s Dy supply chain has sparked widespread concern. Here, we explore future trends and innovation strategies associated with the linkage between Dy and NdFeBs under various climate scenarios in China. We find China alone is expected to exhaust the global present Dy reserve within the next 2–3 decades to facilitate the 1.5 °C climate target. By implementing global available innovation strategies, such as material substitution, reduction, and recycling, it is possible to avoid 48%–68% of China’s cumulative demand for Dy. Nevertheless, ongoing efforts in REE exploration and production are still required to meet China’s growing Dy demand, which will face competition from the United States, European Union, and other countries with ambitious climate targets. Here our analysis urges China and those nations to form wider cooperation in REE supply chains as well as in NdFeB innovation for the realization of a global climate-safe future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Determination of sin{sup 2}θ{sub W} using ν (ν{sup ¯})-nucleus scattering

We have studied nonisoscalarity and medium effects in the extraction of weak mixing angle using Paschos and Wolfenstein relation in the iron nucleus. Paschos and Wolfenstein(PW) relation is valid for an isoscalar target. We have modified the PW relation for nonisoscalar target as well as incorporated the medium effects like Pauli blocking, Fermi motion, nuclear binding energy and pion rho cloud contributions. In our calculations we have used the relativistic nuclear spectral function which includes nucleon correlations. Finally local density approximation is applied to translate the numerical results to the finite nuclei. We have studied the dependence of sin{sup 2}θ{sub W} on Bjorken variables x and y, four momentum transfer square (Q{sup 2}), energy of the neutrino and antineutrino, and effect of excess neutrons over protons in the nuclear target.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Seed photon fields of blazars in the internal shock scenario

We extend our approach of modeling spectral energy distribution (SED) and light curves of blazars to include external Compton (EC) emission due to inverse Compton scattering of an external anisotropic target radiation field. We describe the time-dependent impact of such seed photon fields on the evolution of multifrequency emission and spectral variability of blazars using a multi-zone time-dependent leptonic jet model, with radiation feedback, in the internal shock model scenario. We calculate accurate EC-scattered high-energy (HE) spectra produced by relativistic electrons throughout the Thomson and Klein-Nishina regimes. We explore the effects of varying the contribution of (1) a thermal Shakura-Sunyaev accretion disk, (2) a spherically symmetric shell of broad-line clouds, the broad-line region (BLR), and (3) a hot infrared emitting dusty torus (DT), on the resultant seed photon fields. We let the system evolve to beyond the BLR and within the DT and study the manifestation of the varying target photon fields on the simulated SED and light curves of a typical blazar. The calculations of broadband spectra include effects of γ-γ absorption as γ-rays propagate through the photon pool present inside the jet due to synchrotron and inverse Compton processes, but neglect γ-γ absorption by the BLR and DT photon fields outside the jet. Thus, our account of γ-γ absorption is a lower limit to this effect. Here, we focus on studying the impact of parameters relevant for EC processes on HE emission of blazars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

High penetration of inverter-based resources into power systems causes lower inertia, and it becomes more challenging to maintain grid stability during contingency events. While conventional demand response and shedding at the load side suffer from slow response, impact by unavailability, and issues from communication, variable-speed drives (VSDs) are becoming strong candidates for fast frequency response (FFR) thanks to their fast response and reduced user impact from variable power adjustment capability. In this letter, we propose an autonomous frequency support control method for a residential refrigerator with a VSD, a promising target small end-use load for FFR. We analyze the effect of the proposed controller on grid stability, and we verify the performance through laboratory experiments with a commercially available refrigerator.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux ( H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making–as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2006

Grayscale Optical Correlator Workbench

Grayscale Optical Correlator Workbench (GOCWB) is a computer program for use in automatic target recognition (ATR). GOCWB performs ATR with an accurate simulation of a hardware grayscale optical correlator (GOC). This simulation is performed to test filters that are created in GOCWB. Thus, GOCWB can be used as a stand-alone ATR software tool or in combination with GOC hardware for building (target training), testing, and optimization of filters. The software is divided into three main parts, denoted filter, testing, and training. The training part is used for assembling training images as input to a filter. The filter part is used for combining training images into a filter and optimizing that filter. The testing part is used for testing new filters and for general simulation of GOC output. The current version of GOCWB relies on the mathematical software tools from MATLAB binaries for performing matrix operations and fast Fourier transforms. Optimization of filters is based on an algorithm, known as OT-MACH, in which variables specified by the user are parameterized and the best filter is selected on the basis of an average result for correct identification of targets in multiple test images.

Hanan, Jay↗
DOE OSTI2025

Sensitivity of an early dark matter search using the electromagnetic calorimeter as a target for the Light Dark Matter eXperiment

The Light Dark Matter eXperiment (LDMX) is proposed to employ a thin tungsten target and a multi-GeV electron beam to carry out a missing momentum search for the production of dark matter candidate particles. We study the sensitivity for a complementary missing-energy-based search using the LDMX Electromagnetic Calorimeter as an active target with a focus on early running. In this context, we construct an event selection from a limited set of variables that projects sensitivity into previously-unexplored regions of light dark matter phase space — down to an effective dark photon interaction strength y of approximately 2 × 10 −13 (5 × 10 −12 ) for a 1 MeV (10 MeV) dark matter candidate mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1977

A model for the pilot's use of motion cues in roll-axis tracking tasks

Simulated target-following and disturbance-regulation tasks were explored with subjects using visual-only and combined visual and motion cues. The effects of motion cues on task performance and pilot response behavior were appreciably different for the two task configurations and were consistent with data reported in earlier studies for similar task configurations. The optimal-control model for pilot/vehicle systems provided a task-independent framework for accounting for the pilot's use of motion cues. Specifically, the availability of motion cues was modeled by augmenting the set of perceptual variables to include position, rate, acceleration, and accleration-rate of the motion simulator, and results were consistent with the hypothesis of attention-sharing between visual and motion variables. This straightforward informational model allowed accurate model predictions of the effects of motion cues on a variety of response measures for both the target-following and disturbance-regulation tasks.

Levison, W. H.↗
DOE OSTI2018

High-Efficiency VCR Engine with Variable Valve Actuation and New Supercharging Technology

Higher efficiency engines are needed for full size pickup trucks, utility vans and medium duty trucks. The primary objective of the program was to demonstrate the potential for improving the fuel economy of a full-size pickup truck by 40 percent while producing at least 281 horsepower. Commercialization requirements include: 1. High torque values at low vehicle speeds: This capability is needed for accelerating a heavy vehicle from a standstill, accelerating uphill, towing or any combination of these factors. 2. High power output: This capability is needed for accelerating onto a highway or passing at higher speeds. The vehicle may be carrying a heavy load, going uphill and/or towing. 3. Compliance with tailpipe emission regulations: This requirement must be met, and is difficult or costly to attain with diesel engine technology. 4. Low fuel consumption: This capability is needed both for reducing operating costs and to reduce greenhouse gas (GHG) vehicle emissions. The capability is also essential for complying with fuel economy standards. 5. Affordability: The powertrain cost must be consistent with mass market pricing. 6. Engine size and weight: The engine must fit in the existing engine bays. Engine weight must also be minimized. 7. Favorable noise vibration and harshness (NVH): The VCR must not present unique NVH challenges. The current program was successful. The efficiency target was achieved and the engine produced 300 horsepower.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Iso-uncertainty control in an experimental fluoroscopy system

Purpose: X-ray fluoroscopy remains an important imaging modality in a number of image-guided procedures due to its real-time nature and excellent spatial detail. However, the radiation dose delivered raises concerns about its use particularly in lengthy treatment procedures (>0.5 h). The authors have previously presented an algorithm that employs feedback of geometric uncertainty to control dose while maintaining a desired targeting uncertainty during fluoroscopic tracking of fiducials. The method was tested using simulations of motion against controlled noise fields. In this paper, the authors embody the previously reported method in a physical prototype and present changes to the controller required to function in a practical setting. Methods: The metric for feedback used in this study is based on the trace of the covariance of the state of the system, tr(C). The state is defined here as the 2D location of a fiducial on a plane parallel to the detector. A relationship between this metric and the tube current is first developed empirically. This relationship is extended to create a manifold that incorporates a latent variable representing the estimated background attenuation. The manifold is then used within the controller to dynamically adjust the tube current and maintain a specified targeting uncertainty. To evaluate the performance of the proposed method, an acrylic sphere (1.6 mm in diameter) was tracked at tube currents ranging from 0.5 to 0.9 mA (0.033 s) at a fixed energy of 80 kVp. The images were acquired on a Varian Paxscan 4030A (2048 × 1536 pixels, ∼100 cm source-to-axis distance, ∼160 cm source-to-detector distance). The sphere was tracked using a particle filter under two background conditions: (1) uniform sheets of acrylic and (2) an acrylic wedge. The measured tr(C) was used in conjunction with a learned manifold to modulate the tube current in order to maintain a specified uncertainty as the sphere traversed regions of varying thickness corresponding to the acrylic sheets in the background. Results: With feedback engaged, the tracking error was found to correlate well with the specified targeting uncertainty. Tracking of the fiducial was found to be robust to changes in the attenuation presented by the varying background conditions. For a desired uncertainty of 5.0 mm, comparison of the feedback framework with a comparable system employing fixed exposure demonstrated dose savings of 29%. Conclusions: This work presents a relation between a state descriptor, tr(C), the x-ray tube current used, and an estimate of the background attenuation. This relation is leveraged to modulate the tube current in order to maintain a desired geometric uncertainty during fluoroscopy. The authors’ work demonstrates the use of the method in a real x-ray fluoroscopy system with physical motion against varying backgrounds. The method offers potential savings in imaging dose to patients and staff while maintaining tracking uncertainty during fluoroscopy-guided treatment procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Plasma Protein Turnover Rates in Rats Using Stable Isotope Labeling, Global Proteomics, and Activity-Based Protein Profiling

Protein turnover is important for general health on cellular and organism scales providing a strategy to replace old, damaged, or dysfunctional proteins. Protein turnover also informs of biomarker kinetics, as a better understanding of synthesis and degradation of proteins increases the clinical utility of biomarkers. Here, turnover rates of plasma proteins in rats were measured in vivo using a pulse-chase stable isotope labeling experiment. During the pulse, rats (n=5) were fed 13C6-labeled lysine (“heavy”) feed for 23 days to label proteins. During the chase, feed was changed to an unlabeled equivalent feed (“light”), and blood was repeatedly sampled from rats over 10 time points for 28 days. Plasma samples were digested with trypsin, and analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS). MaxQuant was used to identify peptides and proteins, and quantify heavy:light lysine ratios. A system of ordinary differential equations was used to calculate protein turnover rates. Using this approach, 273 proteins were identified, and turnover rates were quantified for 157 plasma proteins with half-lives ranging 0.3-103 days. For the ~70 most abundant proteins, variability in turnover rates among rats was low (median coefficient of variation: 0.09). Activity-based protein profiling was applied to pooled plasma samples to enrich serine hydrolases using a fluorophosphonate (FP2) activity-based probe. This enrichment resulted in turnover rates for an additional 17 proteins. This study is the first to measure global plasma protein turnover rates in rats in vivo, measure variability of protein turnover rates in any animal model, and utilize activity-based protein profiling for enhancing measurements of targeted, low-abundant proteins, such as those commonly used as biomarkers. Measured protein turnover rates will be important for understanding of the role of protein turnover in cellular and organism health as well as increasing the utility of protein biomarkers through better understanding of processes governing biomarker kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Measurement of Ratios of ν μ Charged-Current Cross Sections on C, Fe, and Pb to CH at Neutrino Energies 2–20 GeV

We present measurements of ν μ charged-current cross section ratios on carbon, iron, and lead relative to a scintillator (CH) using the fine-grained MINERvA detector exposed to the NuMI neutrino beam at Fermilab. The measurements utilize events of energies 2 < E ν < 20 GeV , with ( E ν ) = 8 GeV , which have a reconstructed μ - scattering angle less than 17° to extract ratios of inclusive total cross sections as a function of neutrino energy E ν and flux-integrated differential cross sections with respect to the Bjorken scaling variable x . These results provide the first high-statistics direct measurements of nuclear effects in neutrino scattering using different targets in the same neutrino beam. Measured cross section ratios exhibit a relative depletion at low x and enhancement at large x . Both become more pronounced as the nucleon number of the target nucleus increases. The data are not reproduced by GENIE , a conventional neutrino-nucleus scattering simulation, or by the alternative models for the nuclear dependence of inelastic scattering that are considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Configuration analysis and optimization on multipolar Galatea trap

Multipolar Galatea magnetic trap simulation model was established with the finite element simulation software COMSOL Multiphysics. Analyses about the magnetic section configuration show that better magnetic configuration should make more plasma stay in the weak magnetic field rather than the annular magnetic shell field. Then an optimization model was established with axial electromagnetic force, weak magnetic field area and average magnetic mirror ratio as the optimization goals and with the currents of myxines as design variables. Select appropriate weight coefficients and get optimization results by applying genetic algorithm. Results show that the superiority of the target value of typical application parameters, including the average magnetic mirror can reduce more than 5%, the weak magnetic field area can increase at least 65%, at the same time, axial electromagnetic force acting on the outer myxines can be reduced to less than 50 N. Finally, the results were proved by COMSOL Multiphysics and the results proved the optimized magnetic trap configuration with more plasma in the weak magnetic field can reduce the plasma diffusion velocity and is more conducive for the constraint of plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗