Results for “Acceleration methods” — Page 59

DOE OSTI2018

Accelerating pathway evolution by increasing the gene dosage of chromosomal segments

Experimental evolution is a critical tool in many disciplines, including metabolic engineering and synthetic biology. However, current methods rely on the chance occurrence of a key step that can dramatically accelerate evolution in natural systems, namely increased gene dosage. Our studies sought to induce the targeted amplification of chromosomal segments to facilitate rapid evolution. Since increased gene dosage confers novel phenotypes and genetic redundancy, we developed a method, Evolution by Amplification and Synthetic Biology (EASy), to create tandem arrays of chromosomal regions. In Acinetobacter baylyi , EASy was demonstrated on an important bioenergy problem, the catabolism of lignin-derived aromatic compounds. The initial focus on guaiacol (2-methoxyphenol), a common lignin degradation product, led to the discovery of Amycolatopsis genes ( gcoAB ) encoding a cytochrome P450 enzyme that converts guaiacol to catechol. However, chromosomal integration of gcoAB in Pseudomonas putida or A. baylyi did not enable guaiacol to be used as the sole carbon source despite catechol being a growth substrate. We report in ~1,000 generations, EASy yielded alleles that in single chromosomal copy confer growth on guaiacol. Different variants emerged, including fusions between GcoA and CatA (catechol 1,2-dioxygenase). This study illustrates the power of harnessing chromosomal gene amplification to accelerate the evolution of desirable traits.

09 BIOMASS FUELS↗

DOE OSTI2015

WE-EF-BRD-01: Past, Present and Future: MRI-Guided Radiotherapy From 2005 to 2025

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

WE-EF-BRD-02: Battling Maxwell’s Equations: Physics Challenges and Solutions for Hybrid MRI Systems

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-EF-BRD-03: I Want It Now!: Advances in MRI Acquisition, Reconstruction and the Use of Priors to Enable Fast Anatomic and Physiologic Imaging to Inform Guidance and Adaptation Decisions

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-EF-BRD-04: MR in the OR: The Growth and Applications of MRI for Interventional Radiology and Surgery

MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Magnetic Measurement With Single Stretched Wire Method on SuperKEKB Final Focus Quadrupoles

Superconducting-final-focus-quadrupole magnet system (QCS) were installed on an interaction region (IR) of SuperKEKB on Feb. 2017. The QCS consists of eight quadrupole magnets and four compensation solenoids; these magnets are contained in the two cryostats and are installed into Belle II detector which generates a solenoid field of 1.5 T. We determined the quadrupole centers with respect to accelerator beam lines with a single stretched wire (SSW) method. Here the results of the magnetic measurement with SSW are presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Learning subgrid-scale models with neural ordinary differential equations

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary differential equations (NODEs). Solving systems with fine temporal and spatial grid scales is an ongoing computational challenge, and closure models are generally difficult to tune. Machine learning approaches have increased the accuracy and efficiency of computational fluid dynamics solvers. In this approach neural networks are used to learn the coarse- to fine-grid map, which can be viewed as subgrid-scale parameterization. We propose a strategy that uses the NODE and partial knowledge to learn the source dynamics at a continuous level. Our method inherits the advantages of NODEs and can be used to parameterize subgrid scales, approximate coupling operators, and improve the efficiency of low-order solvers. Numerical results with the two-scale Lorenz 96 ODE, the convection-diffusion PDE, and the viscous Burgers' PDE are used to illustrate this approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Measuring Q₀ in LCLS-II Cryomodules Using Helium Liquid Level

The nitrogen-doped cavities used in the Linac Coherent Light Source II (LCLS-II) cryomodules have shown an unprecedented high Q₀ in vertical and cryomodule testing compared with cavities prepared with standard methods. While demonstration of high Q₀ in the test stand has been achieved, maintaining that performance in the linac is critical to the success of LCLS-II and future accelerator projects. The LCLS-II cryomodules required a novel method of measuring Q₀, due to hardware incompatibilities with existing procedures. Initially developed at Jefferson Lab during cryomodule acceptance testing before being used in the tunnel at SLAC, we use helium liquid level data to estimate the heat generated by cavities. We first establish the relationship between the rate of helium evaporation from known heat loads using electric heaters, and then use that relationship to determine heat from an RF load. Here we present the full procedure along with the development process, lessons learned, and reproducibility while demonstrating for the first time that world record Q₀ can be maintained within the real accelerator environment.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Accelerated ions from pulsed-power-driven fast plasma flow in perpendicular magnetic field

To understand the interaction between fast plasma flow and perpendicular magnetic field, we have investigated the behavior of a one-dimensional fast plasma flow in a perpendicular magnetic field by a laboratory-scale experiment using a pulsed-power discharge. The velocity of the plasma flow generated by a tapered cone plasma focus device is about 30 km/s, and the magnetic Reynolds number is estimated to be 8.8. After flow through the perpendicular magnetic field, the accelerated ions are measured by an ion collector. To clarify the behavior of the accelerated ions and the electromagnetic fields, numerical simulations based on an electromagnetic hybrid particle-in-cell method have been carried out. The results show that the behavior of the accelerated ions corresponds qualitatively to the experimental results. Faster ions in the plasma flow are accelerated by the induced electromagnetic fields modulated with the plasma flow.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Flattening filter free beams from TrueBeam and Versa HD units: Evaluation of the parameters for quality assurance

Purpose: Flattening filter free (FFF) beams generated by medical linear accelerators are today clinically used for stereotactical and non-stereotactical radiotherapy treatments. Such beams differ from the standard flattened beams (FF) in the high dose rate and the profile shape peaked on the beam central axis. Definition of new parameters as unflatness and slope for FFF beams has been proposed based on a renormalization factor for FFF profiles. The present study aims to assess the dosimetric differences between FFF beams generated by linear accelerators from different vendors, and to provide renormalization and parameter data of the two kinds of units. Methods: Dosimetric data from two Varian TrueBeam and two Elekta Versa HD linear accelerators, all with 6 and 10 MV nominal accelerating potentials, FF and FFF modes have been collected. Renormalization factors and related fit parameters according to Fogliata et al. [“Definition of parameters for quality assurance of flattening filter free (FFF) photon beams in radiation therapy,” Med. Phys. 39, 6455–6464 (2012)] have been evaluated for FFF beams of both units and energies. Unflatness and slope parameters from profile curves were evaluated. Dosimetric differences in terms of beam penetration and near-the-surface dose were also assessed. Results: FFF profile parameters have been updated; renormalization factors and unflatness from the Varian units are consistent with the published data. Elekta FFF beam qualities, different from the Varian generated beams, tend to express similar behaviour as the FF beam of the corresponding nominal energy. TPR{sub 20,10} for 6 and 10 MV FF and FFF TrueBeam beams are 0.665, 0.629 (6 MV) and 0.738, 0.703 (10 MV). The same figures for Versa HD units are 0.684, 0.678 (6 MV) and 0.734, 0.721 (10 MV). Conclusions: Renormalization factor and unflatness parameters evaluated from Varian and Elekta FFF beams are provided, in particular renormalization factors table and fit parameters.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Distributed memory, GPU accelerated Fock construction for hybrid, Gaussian basis density functional theory

With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. Here in this work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn–Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Quantum Monte Carlo with the Langevin Equation: Coupled Bose-Fermi Systems

We describe the algorithmic details and a performance evaluation of a Langevin approach to a strongly interacting electron-phonon system, and show it has a near linear scaling with lattice size N s . Many of the limitations of previous attempts to employ such methods to condensed matter lattice Hamiltonians are absent. In particular, the iterative linear algebra solution remains well behaved at strong coupling and low temperatures. The use of Fourier Acceleration is crucial for efficiency, and its use makes the method competitive with the widely-used local update methods, which scale as ${N}_{s}^{3}$ for on-site interactions and ${N}_{s}^{4}$ for long range electron-phonon coupling, even on rather small lattice sizes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Experimental Aging and Lifetime Prediction in Grid Applications for Large-Format Commercial Li-Ion Batteries

Due to the growth of electric vehicle and stationary energy storage markets, the production and use of lithium-ion batteries has grown exponentially in recent years. For many of these applications, large-format lithium-ion batteries are being utilized, as large cells have less inactive material relative to their energy capacity and require fewer electrical connections to assemble into packs. And especially for stationary energy storage systems, where energy delivered is the only revenue source, the economics of these battery systems is highly dependent on cell lifetime. However, testing of large-format lithium-ion batteries is time consuming and requires high current channels and large testing chambers, making information on the performance of commercial, large-format lithium-ion batteries hard to come by. Here, accelerated aging test data from four commercial large-format lithium-ion batteries is reported. These batteries span both NMC-Gr and LFP-Gr cell chemistries, pouch and prismatic formats, and a range of cell designs with varying power capabilities. Accelerated aging test results are analyzed to examine both cell performance, in terms of efficiency and thermal response under load, as well as cell lifetime. Cell thermal response is characterized by measuring temperature during cycle aging, which is used to calculated a normalized thermal resistance value that may help estimate both cell cooling needs or to help extrapolate aging test results to different thermal environments. Cell lifetime is evaluated qualitatively, considering simply the average calendar and cycle life across a range of conditions, as well as quantitatively, using statistical modeling and machine-learning methods to identify predictive aging models from the accelerated aging data. These predictive aging models are then used to investigate cell sensitivities to stressors, such as cycling temperature, voltage window, and C-rate, as well as to predict cell lifetime in various stationary storage applications. Results from this work show that cell lifetime and sensitivity to aging conditions varies substantially across commercial cells, necessitating testing for specific cell formats to make quantitative lifetime predictions. That being said, all commercial cells tested here are predicted to reach at least 10-year lifetimes for stationary storage applications. Based on the aging test results and modeling, some cells are expected to be relatively insensitive to temperature and use-case, making them suited for simple use cases with little or no thermal management and simple controls, while the lifetime of other cells could be extended to 20+ years if operated with thermal management and degradation-aware controls.

battery↗

DOE OSTI2022

Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework that builds on the deep deterministic policy gradient from demonstrations to identify an optimal restoration strategy. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. In conclusion, the proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.

Du, Yan↗

DOE OSTI2020

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. In this perspective, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Stochastic averaging and sensitivity analysis for two scale reaction networks

In the presence of multiscale dynamics in a reaction network, direct simulation methods become inefficient as they can only advance the system on the smallest scale. This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution. A two-time-scale formulation is used to establish bounds on the bias induced by the averaging method. Further, this formulation provides a framework to create an accelerated “averaged” version of most single-scale sensitivity estimation methods. In particular, we propose the use of a centered ergodic likelihood ratio method for steady state estimation and show how one can adapt it to accelerated simulations of multiscale systems. Lastly, we develop an adaptive “batch-means” stopping rule for determining when to terminate the micro-equilibration process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

SU-F-T-111: Investigation of the Attila Deterministic Solver as a Supplement to Monte Carlo for Calculating Out-Of-Field Radiotherapy Dose

Purpose: To use the Attila deterministic solver as a supplement to Monte Carlo for calculating out-of-field organ dose in support of epidemiological studies looking at the risks of second cancers. Supplemental dosimetry tools are needed to speed up dose calculations for studies involving large-scale patient cohorts. Methods: Attila is a multi-group discrete ordinates code which can solve the 3D photon-electron coupled linear Boltzmann radiation transport equation on a finite-element mesh. Dose is computed by multiplying the calculated particle flux in each mesh element by a medium-specific energy deposition cross-section. The out-of-field dosimetry capability of Attila is investigated by comparing average organ dose to that which is calculated by Monte Carlo simulation. The test scenario consists of a 6 MV external beam treatment of a female patient with a tumor in the left breast. The patient is simulated by a whole-body adult reference female computational phantom. Monte Carlo simulations were performed using MCNP6 and XVMC. Attila can export a tetrahedral mesh for MCNP6, allowing for a direct comparison between the two codes. The Attila and Monte Carlo methods were also compared in terms of calculation speed and complexity of simulation setup. A key perquisite for this work was the modeling of a Varian Clinac 2100 linear accelerator. Results: The solid mesh of the torso part of the adult female phantom for the Attila calculation was prepared using the CAD software SpaceClaim. Preliminary calculations suggest that Attila is a user-friendly software which shows great promise for our intended application. Computational performance is related to the number of tetrahedral elements included in the Attila calculation. Conclusion: Attila is being explored as a supplement to the conventional Monte Carlo radiation transport approach for performing retrospective patient dosimetry. The goal is for the dosimetry to be sufficiently accurate for use in retrospective epidemiological investigations.

60 APPLIED LIFE SCIENCES↗