Results for “control simulation testing” — Page 96

DOE OSTI2016

WE-DE-202-00: Connecting Radiation Physics with Computational Biology

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-DE-202-03: Modeling of Biological Processes - What Happens After Early Molecular Damage?

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

RLC4CLR (Reinforcement Learning Controller for Critical Load Restoration Problems)

RLC4CLR demonstrates using a reinforcement learning controller (RLC) to solve a critical load restoration (CLR) problem, which improves the grid resilience after a substation outage event. RLC4CLR consists of two parts. (1) RL environment: This environment encapsulates the CLR problem to be solved and provides interfacing functions to follow the standard OpenAI Gym format. A power system simulator, i.e., OpenDSS, is included to provide the power flow solution. Controller inputs and outputs (RL state and action) as well as the reward are defined in this environment as well. In summary, the RL environment is the problem formulation from which the RL agent can learn. (2) RL training script: The training script enables the RL agent to learn its control policy by interacting with the RL environment. For RL training, an open-sourced RL library, i.e., RLlib, is leveraged which is based on a distributed computing framework (Ray). The training script is designed to be able to be run on both local machine or the NREL HPC system. Other components of RLC4CLR include input data, e.g., grid model (standard IEEE test feeders), and other files used for results analysis.

Zhang, Xiangyu↗

DOE OSTI2019

Demonstration of MSR salt property database operation with a reactor analysis tool

Successful modeling and simulation of molten salt reactors (MSRs) requires quality thermodynamic and thermophysical data for the working fluid. A goal of the Molten Salt Reactor Campaign is to develop a quality-controlled database of these properties that will be available for a multitude of different salt types. Furthermore, properties shall be defined in a standardized format that can then be used by reactor modeling and analysis tools. This report details work done to develop an approach for integrating salt properties into a MSR analysis tool. This activity required defining a format for thermophysical properties that could be produced from salt data delivered by Argonne National Laboratory (ANL) and which could be read into a reactor analysis tool. The reactor analysis tool used for this activity was the Consortium for Advanced Simulation of Light Water Reactors-developed tool, VERA. The changes implemented in VERA to accept the thermophysical properties file are discussed and tested and the process of reading thermophysical properties into VERA for a MSR simulation is demonstrated. In addition to being able to get fluid properties into the simulation tool, it is also necessary that a process exists for adding new salt data to the salt database. The NEAMS Workbench is being expanded with a new package called ThermPhys that will provide a user interface for adding new salts to the database while performing input validation and ensuring data quality.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Initial Core Physics and Safety Analyses Supporting the Versatile Test Reactor

The Versatile Test Reactor (VTR) is a proposed integrated facility for investigations into the behavior of fuels and materials subjected to high, fast neutron fluxes prototypical of fast reactor environments. Detailed design of the reactor is currently being performed by a group of U.S. National Laboratories, including Argonne National Laboratory. The current design of the reactor is a 300 MW sodium-cooled pool-type fast reactor fueled with ternary U-20Pu-10Zr metallic fuel. This report documents reactor core physics and preliminary safety analyses that have been performed. The reference VTR core has 313 assembly positions, 66 of which contain fuel assemblies. The remaining assembly positions are loaded with reflector, shield, control rod duct, and test assemblies. Ten positions are available in the reference design for experimental or test assemblies, however the VTR will support test assemblies in any position in the core for alternate core loadings. The performance of the reference VTR core has been assessed for an assumed equilibrium cycle. The core power distribution and reactivity feedback coefficients have been calculated. Preliminary assessments of the reactivity coefficients have shown that each of them results in a negative reactivity change for increasing temperatures. Safety analysis of a VTR layout has been performed using the SAS4A/SASSYS-1 fast reactor safety analysis code. A model has been developed to represent the current core design and a reference heat transport system design. The model has been used to simulate and perform analysis of protected and unprotected loss of heat sink, station blackout, and transient overpower accident scenarios. These scenarios represent the three main ways to perturb the reactor core, through changes to the core inlet temperature, mass flow rate, or reactivity insertions and are credible bounding scenarios for the evaluated transient categories. The results of these simulations have been assessed against simplified criteria for Anticipated Operational Occurrences for the protected transients and for Beyond Design Basis Events for the unprotected transients. Because the primary heat transport system is able to transition quickly and effectively to natural circulation and because the shutdown heat removal system provides sufficient heat rejection capability, large margins for all criteria were predicted for all protected transients. Large margins were also predicted for the unprotected loss of heat sink and station blackout transients due to the strong negative reactivity feedbacks generated by the core. Because of the high power density in the reactor, smaller fuel melting margins are predicted for the unprotected transient overpower accident.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Optimizing radiotherapy protocols using computer automata to model tumour cell death as a function of oxygen diffusion processes

The concept of hypofractionation is gaining momentum in radiation oncology centres, enabled by recent advances in radiotherapy apparatus. The gain of efficacy of this innovative treatment must be defined. We present a computer model based on translational murine data for in silico testing and optimization of various radiotherapy protocols with respect to tumour resistance and the microenvironment heterogeneity. This model combines automata approaches with image processing algorithms to simulate the cellular response of tumours exposed to ionizing radiation, modelling the alteration of oxygen permeabilization in blood vessels against repeated doses, and introducing mitotic catastrophe (as opposed to arbitrary delayed cell-death) as a means of modelling radiation-induced cell death. Published data describing cell death in vitro as well as tumour oxygenation in vivo are used to inform parameters. Our model is validated by comparing simulations to in vivo data obtained from the radiation treatment of mice transplanted with human prostate tumours. We then predict the efficacy of untested hypofractionation protocols, hypothesizing that tumour control can be optimized by adjusting daily radiation dosage as a function of the degree of hypoxia in the tumour environment. Further biological refinement of this tool will permit the rapid development of more sophisticated strategies for radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Evaluation of Electromechanical Systems Dynamically Emulating a Candidate Hydrokinetic Turbine

The use of controllable motor-generator sets to emulate the dynamics of a hydrokinetic turbine is evaluated as an alternative to field testing a prototype. The emulator control dynamic equations are presented, methods for scaling turbine parameters are examined, and experimental results are presented from three electromechanical emulation machines (EEMs) programmed to emulate the same vertical-axis fixed-pitch turbine. Although hardware platforms and control implementations varied, results show that each EEM is successful in emulating the turbine model, thus demonstrating the general feasibility of the approach. However, performance of motor control under torque command, current command or speed command differed. In one of the EEMs evaluated, the power take off controller tracks the maximum power-point of the turbine in response to turbulence. Utilizing realistic inflow conditions and control laws, the emulator dynamic speed response is shown to agree well at low frequencies with numerical simulation but to deviate at high frequencies.

13 HYDRO ENERGY↗

DOE OSTI2017

CT scanning and flow measurements of shale fractures after multiple shearing events

A shearing apparatus was used in conjunction with a Hassler-style core holder to incrementally shear fractured shale cores while maintaining various confining pressures. Computed tomography scans were performed after each shearing event, and were used to obtain information on evolving fracture geometry. Fracture transmissivity was measured after each shearing event to understand the hydrodynamic response to the evolving fracture structure. The digital fracture volumes were used to perform laminar single phase flow simulations (local cubic law with a tapered plate correction model) to qualitatively examine small scale flow path variations within the altered fractures. Fractures were found to generally increase in aperture after several shear slip events, with corresponding transmissivity increases. Lower confining pressure resulted in a fracture more prone to episodic mechanical failure and sudden changes in transmissivity. Conversely, higher confining pressures resulted in a system where, after an initial setting of the fracture surfaces, changes to the fracture geometry and transmissivity occurred gradually. Flow paths within the fractures are largely controlled by the location and evolution of zero aperture locations. Lastly, a reduction in the number of primary flow pathways through the fracture, and an increase in their width, was observed during all shearing tests.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2019

Adaptive relay autotuning under static and non-static disturbances with application to friction stir welding

When a traditional relay feedback test is biased, asymmetry is induced, erroneous system identification occurs, and the test may stop oscillating. Adaptive methods which restore symmetry to the relay test are developed, and additional methods are presented to detect and recover from a non-oscillation condition. This is demonstrated both in simulation and in a process with significant transients and disturbances - friction stir welding (FSW). In testing, it is shown that the adaptive method is able to restore symmetry even in the presence of severe and changing disturbances, including after the weld plunge where temperature gradients change rapidly. Finally, these improvements enable system identification of systems such as FSW in a more robust and accurate manner.

42 ENGINEERING↗

DOE OSTI2021

Prediction of softening kinetics and recrystallization texture in non-isothermally annealed bulged tubes using CPFEM and CA models

A hierarchically coupled cellular automata (CA) model, crystal plasticity finite element method (CPFEM), and thermal finite element (FE) model is developed to predict the softening kinetics of the bulged steel tube during non-isothermal annealing. Through the developed model, the kinetics of softening mechanisms including static recovery (SRV) and static recrystallization (SRX), as well as the recrystallization texture are predicted. Later, the Johnson-Mehl-Avrami-Kohnogorov (JMAK) model based on the predicted SRX data is developed to interpret the recrystallization behavior of the material. To perform this study, diverse experimental tests including tube hydroforming (THF), annealing, uniaxial tensile test, hardness test, as well as microstructure observations through optical microscopy and Electron Backscatter Diffraction (EBSD) tests on steel tube are performed. The obtained experimental data are utilized to calibrate and verify the implemented CPFEM model for simulation of THF process, thermal FE model for prediction of the local temperature over annealing time, and CA algorithm for modeling of the softening kinetics and texture evolution throughout the annealing process. The study shows that the predicted deformation characteristics, softening kinetics, recrystallization texture and temperature profile during non-isothermal annealing are in good agreement with experimental data. During the annealing process, a total of four stages for the kinetics of softening mechanisms is observed: No softening; SRV only; SRV dominant; and SRX dominant. During the progress of SRX, the behavior of recrystallization is abruptly changed, confirming that two different mechanisms are controlling the kinetics of transformation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

Here, improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

A study on the statistical significance of mutual information between morphology of a galaxy and its large-scale environment

ABSTRACT A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in Sloan Digital Sky Survey (SDSS) upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We propose three different methods to test the statistical significance of these non-zero mutual information and apply them to SDSS and Millennium run simulation. We randomize the morphological information of SDSS galaxies without affecting their spatial distribution and compare the mutual information in the original and randomized data sets. We also divide the galaxy distribution into smaller subcubes and randomly shuffle them many times keeping the morphological information of galaxies intact. We compare the mutual information in the original SDSS data and its shuffled realizations for different shuffling lengths. Using a t-test, we find that a small but statistically significant (at $99.9{{\ \rm per\ cent}}$ confidence level) mutual information between morphology and environment exists upto the entire length-scale probed. We also conduct another experiment using mock data sets from a semi-analytic galaxy catalogue where we assign morphology to galaxies in a controlled manner based on the density at their locations. The experiment clearly demonstrates that mutual information can effectively capture the physical correlations between morphology and environment. Our analysis suggests that physical association between morphology and environment may extend to much larger length-scales than currently believed, and the information theoretic framework presented here can serve as a sensitive and useful probe of the assembly bias and large-scale environmental dependence of galaxy properties.

Sarkar, Suman↗

DOE OSTI2023

Biomechanical Analysis of Multilevel Posterior Cervical Spinal Fusion Constructs

Study Design: Controlled Laboratory Study Objective: To compare multilevel posterior cervical fusion (PCF) constructs stopping at C7, T1, and T2 under cyclic load to determine the range of motion (ROM) between the lowest instrumented level and lowest instrumented-adjacent level (LIV-1). Summary of Background Data: PCF is a mainstay of treatment for various cervical spine conditions. The transition between the flexible cervical spine and rigid thoracic spine can lead to construct failure at the cervicothoracic junction. There is little evidence to determine the most appropriate level at which to stop a multilevel PCF. Methods: Fifteen human cadaveric cervicothoracic spines were randomly assigned to 1 of 3 treatment groups: PCF stopping at C7, T1, or T2. Specimens were tested in their native state, following a simulated PCF, and after cyclic loading. Specimens were loaded in flexion-extension), lateral bending, and axial rotation. Three-dimensional kinematics were recorded to evaluate ROM. Results: The C7 group had greater flexion-extension motion than the T1 and T2 groups following instrumentation (10.17±0.83 degree vs. 2.77±1.66 degree and 1.06±0.55 degree, P <0.001), and after cyclic loading (10.42±2.30 degree vs. 2.47±0.64 degree and 1.99±1.23 degree, P <0.001). There was no significant difference between the T1 and T2 groups. The C7 group had greater lateral bending ROM than both thoracic groups after instrumentation (8.81±3.44 degree vs. 3.51±2.52 degree, P =0.013 and 1.99±1.99 degree, P =0.003) and after cyclic loading. The C7 group had greater axial rotation motion than the thoracic groups (4.46±2.27 degree vs. 1.26±0.69 degree, P =0.010; and 0.73±0.74 degree, P =0.003) following cyclic loading. Conclusion: Motion at the cervicothoracic junction is significantly greater when a multilevel PCF stops at C7 rather than T1 or T2. This is likely attributable to the transition from a flexible cervical spine to a rigid thoracic spine. Although this does not account for in vivo fusion, surgeons should consider extending multilevel PCF constructs to T1 when feasible. Level of Evidence: Not applicable.

Neurosciences & Neurology↗

DOE OSTI2019

Summary of the Mechanical Performances of the 1.5 m Long Models of the Nb $_{3}$ Sn Low- $\beta$ Quadrupole MQXF

The Nb 3 Sn quadrupole MQXF is being developed as a part of the large hadron collide (LHC) High Luminosity upgrade. The magnet design was tested on 1.5-m-long short models, sharing the same cross section with the full-length magnets. Various azimuthal and longitudinal preloads were applied, studying the impact on the magnet training and on its mechanical performances. The experiments demonstrated the possibility to control the magnet prestress. However, various factors, coil size among the others, may affect the stress variation between and within each winding. This variation could prevent the magnets from reaching the magnet performances, as for example as a result of the critical current reduction of the Nb 3 Sn strands. This paper analyzes the mechanical performances of the short models, studying in particular the stress variation on different coils. The measured coil size was used as input in the numerical simulations, and the results were then compared with the strain gauge measurements. Lastly, the short model experience was used to evaluate the feasibility of a loading operation that does not rely on the strain measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Test Methodologies for Hydrogen Sensor Performance Assessment: Chamber vs. Flow Through Test Apparatus: Preprint

Certification of hydrogen sensors to standards often prescribes using large-volume test chambers [1, 2]. However, feedback from stakeholders such as sensor manufacturers and end-users indicate that chamber test methods are often viewed as too slow and expensive for routine assessment. Flow through test methods potentially are an efficient, cost-effective alternative for sensor performance assessment. A large number of sensors can be simultaneously tested, in series or in parallel, with an appropriate flow through test fixture. The recent development of sensors with response times of less than 1s mandates improvements in equipment and methodology to properly capture the performance of this new generation of fast sensors; flow methods are a viable approach for accurate response and recovery time determinations, but there are potential drawbacks. According to ISO 26142 [1], flow through test methods may not properly simulate ambient applications. In chamber test methods, gas transport to the sensor can be dominated by diffusion which is viewed by some users as mimicking deployment in rooms and other confined spaces. Alternatively, in flow through methods, forced flow transports the gas to the sensing element. The advective flow dynamics may induce changes in the sensor behaviour relative to the quasi-quiescent condition that may prevail in chamber test methods. One goal of the current activity in the JRC and NREL sensor laboratories [3, 4] is to develop a validated flow through apparatus and methods for hydrogen sensor performance testing. In addition to minimizing the impact on sensor behaviour induced by differences in flow dynamics, challenges associated with flow through methods include the ability to control environmental parameters (humidity, pressure and temperature) during the test and changes in the test gas composition induced by chemical reactions with upstream sensors. Guidelines on flow through test apparatus design and protocols for the evaluation of hydrogen sensor performance are being developed. Various commercial sensor platforms (e.g., thermal conductivity, catalytic and metal semiconductor) were used to demonstrate the advantages and issues with the flow through methodology.

14 SOLAR ENERGY↗