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DOE OSTI2015

MO-FG-207-03: Maximizing the Utility of Integrated PET/MRI in Clinical Applications

The use of integrated PET/MRI systems in clinical applications can best benefit from understanding their technological advances and limitations. The currently available clinical PET/MRI systems have their own characteristics. Thorough analyses of existing technical data and evaluation of necessary performance metrics for quality assurances could be conducted to optimize application-specific PET/MRI protocols. This Symposium will focus on technical advances and limitations of clinical PET/MRI systems, and how this exciting imaging modality can be utilized in applications that can benefit from both PET and MRI. Learning Objectives: To understand the technological advances of clinical PET/MRI systems To correctly identify clinical applications that can benefit from PET/MRI To understand ongoing work to further improve the current PET/MRI technology Floris Jansen is a GE Healthcare employee.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2005

Integrated System-Level Optimization for Concurrent Engineering With Parametric Subsystem Modeling

The introduction of concurrent design practices to the aerospace industry has greatly increased the productivity of engineers and teams during design sessions as demonstrated by JPL's Team X. Simultaneously, advances in computing power have given rise to a host of potent numerical optimization methods capable of solving complex multidisciplinary optimization problems containing hundreds of variables, constraints, and governing equations. Unfortunately, such methods are tedious to set up and require significant amounts of time and processor power to execute, thus making them unsuitable for rapid concurrent engineering use. This paper proposes a framework for Integration of System-Level Optimization with Concurrent Engineering (ISLOCE). It uses parametric neural-network approximations of the subsystem models. These approximations are then linked to a system-level optimizer that is capable of reaching a solution quickly due to the reduced complexity of the approximations. The integration structure is described in detail and applied to the multiobjective design of a simplified Space Shuttle external fuel tank model. Further, a comparison is made between the new framework and traditional concurrent engineering (without system optimization) through an experimental trial with two groups of engineers. Each method is evaluated in terms of optimizer accuracy, time to solution, and ease of use. The results suggest that system-level optimization, running as a background process during integrated concurrent engineering sessions, is potentially advantageous as long as it is judiciously implemented.

Schuman, Todd↗

DOE OSTI2024

AI-Driven Detector Design for the EIC (Final Technical Report)

We developed an optimization workflow based on DNN-based fast-simulation and reconstruction algorithms. We used these methods to advance the design of calorimeter systems for the Electron-Ion Collider (EIC). This DNN-driven optimization provides a blueprint for integrating gradient-based methods into detector-design workflows. All software pipelines and methods have been released publicly and incorporated into the EIC collaboration’s physics studies, broadening their impact. Three journal articles detailing the methods developed here serve as a reference for the design and optimal use of next generation high-granularity calorimeter systems in nuclear and particle physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Recent advances in enzyme engineering for improved deconstruction of poly(ethylene terephthalate) (PET) plastics

In the last ~20 years, a multitude of natural enzymes have been discovered that can catalyze the breakdown of the common plastic poly(ethylene terephthalate) (PET). While enzymatic PET recycling is an attractive alternative end-of-life route for this waste plastic, the enzymes are not yet optimized for efficient and economical industrial use. Here, we discuss recent advances in engineering these PET-degrading enzymes, which include PET, bis(2-hydroxyethyl) terephthalate (BHET), and 2-hydroxyethyl terephthalic acid (MHET) hydrolases, toward industrially-relevant engineering goals. We place emphasis on trends from past efforts in rational and semi-rational design and emerging areas in directed evolution/high throughput screening and computational design for engineering these enzymes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Software design space exploration for exascale combustion co-design

The design of hardware for next-generation exascale computing systems will require a deep understanding of how software optimizations impact hardware design trade-offs. In order to characterize how co-tuning hardware and software parameters affects the performance of combustion simulation codes, we created ExaSAT, a compiler-driven static analysis and performance modeling framework. Our framework can evaluate hundreds of hardware/software configurations in seconds, providing an essential speed advantage over simulators and dynamic analysis techniques during the co-design process. Our analytic performance model shows that advanced code transformations, such as cache blocking and loop fusion, can have a significant impact on choices for cache and memory architecture. Our modeling helped us identify tuned configurations that achieve a 90% reduction in memory traffic, which could significantly improve performance and reduce energy consumption. These techniques will also be useful for the development of advanced programming models and runtimes, which must reason about these optimizations to deliver better performance and energy efficiency.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Technology planning for long range utilization of Space Station Freedom

A system is described with specific examples for identifying and selecting advanced technologies for use with the Space Station Freedom (SSF) that positively impact capabilities and costs. Emerging advanced technologies should be compared with SSF technological needs and selected according to an optimal combination of readiness, development risks/costs, performance requirements, and crew safety. The selected technologies should be planned to the extent that the technology continues advancing to the point of readiness in a manner consistent with the objectives and timeframe of the SSF program. An SSF Advanced Studies component is described that worked to advance critical emergent technologies including advanced heat rejection, closed air-loops, high-efficiency space-power systems, and advanced power storage. The efforts can lead to the identification of crucial parameters such as performance degradation, lifetime, growth, and area that are relevant to the SSF overall objectives.

Ahlf, P.↗

DOE OSTI2019

Optimized illumination for high-throughput ptychography

As a scanning version of coherent diffraction imaging (CDI), X-ray ptychography has become a popular and very successful method for high-resolution quantitative imaging of extended specimens. The requirements of mostly coherent illumination and the scanning mechanism limit the throughput of ptychographic imaging. In this paper, we will introduce the methods we use at the Advanced Photon Source (APS) to achieve high-throughput ptychography by optimizing the parameters of the illumination beam. One work we have done is increasing the illumination flux by using a double-multilayer monochromator (DMM) optics with about 0.8% bandwidth. Compared with our double-crystal monochromator (DCM) optics with 0.01% bandwidth, this DMM optics provides around 20 times more flux. A multi-wavelength reconstruction method has been implemented to deal with the consequential degraded temporal coherence from such an illumination to ensure high-quality reconstruction. In the other work, we adopt a novel use of flat-top focusing optics to generate a flat-top beam with the diameter of about 1.5 µm on the focal plane. The better uniformity of the probe and the large beam size allow one to significantly increase the step size in ptychography scans and thereby the imaging efficiency.

Broad bandwidth↗

DOE OSTI2014

Development of advanced manufacturing technologies for low cost hydrogen storage vessels

The U.S. Department of Energy (DOE) defined a need for low-cost gaseous hydrogen storage vessels at 700 bar to support cost goals aimed at 500,000 units per year. Existing filament winding processes produce a pressure vessel that is structurally inefficient, requiring more carbon fiber for manufacturing reasons, than would otherwise be necessary. Carbon fiber is the greatest cost driver in building a hydrogen pressure vessel. The objective of this project is to develop new methods for manufacturing Type IV pressure vessels for hydrogen storage with the purpose of lowering the overall product cost through an innovative hybrid process of optimizing composite usage by combining traditional filament winding (FW) and advanced fiber placement (AFP) techniques. A numbers of vessels were manufactured in this project. The latest vessel design passed all the critical tests on the hybrid design per European Commission (EC) 79-2009 standard except the extreme temperature cycle test. The tests passed include burst test, cycle test, accelerated stress rupture test and drop test. It was discovered the location where AFP and FW overlap for load transfer could be weakened during hydraulic cycling at 85°C. To design a vessel that passed these tests, the in-house modeling software was updated to add capability to start and stop fiber layers to simulate the AFP process. The original in-house software was developed for filament winding only. Alternative fiber was also investigated in this project, but the added mass impacted the vessel cost negatively due to the lower performance from the alternative fiber. Overall the project was a success to show the hybrid design is a viable solution to reduce fiber usage, thus driving down the cost of fuel storage vessels. Based on DOE’s baseline vessel size of 147.3L and 91kg, the 129L vessel (scaled to DOE baseline) in this project shows a 32% composite savings and 20% cost savings when comparing Vessel 15 hybrid design and the Quantum baseline all filament wound vessel. Due to project timing, there was no additional time available to fine tune the design to improve the load transfer between AFP and FW. Further design modifications will likely help pass the extreme temperature cycle test, the remaining test that is critical to the hybrid design.

08 HYDROGEN↗

DOE OSTI2018

Infrastructure Spatial Sensing at Intersections

This presentation was presented at the VTO AMR in 2018 covering the Infrastructure Spatial Sensing at Intersection project, which investigates the benefits and state of industry for utilizing LiDAR and advanced video signal processing to track all objects in the field of view at intersections to enhance safety and optimize traffic flow. Whereas the safety, mobility, and energy benefits of automated and connected vehicles (CAVs) will only be realized at intersections, once a significant portion of the vehicle fleet is converted to CAV technology, advanced spatial sensors deployed at intersections provide the observability to optimize traffic flow in the near term, hastening its benefits.

25 ENERGY STORAGE↗

DOE OSTI2017

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

13 HYDRO ENERGY↗

DOE OSTI2025

Multi-Objective Design Optimization of 100 kW Non-Rare-Earth or Reduced-Rare-Earth Machines

The goal of this project is to reduce the size, weight, cost, and losses associated with rotating electric machinery and its associated power electronics for electric and hybrid vehicle applications. In particular, this effort strives to facilitate electric machinery that will meet the requirements set forth in the USDRIVE Electrical and Electronics Technical Team Roadmap of October 2017. This roadmap calls for an electric machine with a peak power of 100 kW, a continuous rated power of 55 kW, a peak speed of less than 20,000 rpm, a volume of no more than 2 liters, a mass of less than 20 kg, and a useful life of 15 years or 300,000 miles of vehicle service. This will be achieved through a combination of (i) new materials, (ii) new electric machine topologies, (iii) advances in power electronics, and (iv) superior design through the use of formal and rigorous multi-objective optimization-based design founded on advanced analysis techniques.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Achieving designed texture and flows in bulk active nematics using optimal control theory

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Furthermore, our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

Complex systems theory↗

DOE OSTI2015

MO-D-213-06: Quantitative Image Quality Metrics Are for Physicists, Not Radiologists: How to Communicate to Your Radiologists Using Their Language

Purpose: A framework for explaining differences in image quality to non-technical audiences in medial imaging is needed. Currently, this task is something that is learned “on the job.” The lack of a formal methodology for communicating optimal acquisition parameters into the clinic effectively mitigates many technological advances. As a community, medical physicists need to be held responsible for not only advancing image science, but also for ensuring its proper use in the clinic. This work outlines a framework that bridges the gap between the results from quantitative image quality metrics like detectability, MTF, and NPS and their effect on specific anatomical structures present in diagnostic imaging tasks. Methods: Specific structures of clinical importance were identified for a body, an extremity, a chest, and a temporal bone protocol. Using these structures, quantitative metrics were used to identify the parameter space that should yield optimal image quality constrained within the confines of clinical logistics and dose considerations. The reading room workflow for presenting the proposed changes for imaging each of these structures is presented. The workflow consists of displaying images for physician review consisting of different combinations of acquisition parameters guided by quantitative metrics. Examples of using detectability index, MTF, NPS, noise and noise non-uniformity are provided. During review, the physician was forced to judge the image quality solely on those features they need for diagnosis, not on the overall “look” of the image. Results: We found that in many cases, use of this framework settled mis-agreements between physicians. Once forced to judge images on the ability to detect specific structures inter reader agreement was obtained. Conclusion: This framework will provide consulting, research/industrial, or in-house physicists with clinically relevant imaging tasks to guide reading room image review. This framework avoids use of the overall “look” or “feel” to dictate acquisition parameter selection. Equipment grants GE Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Development of CSG-based radiation shielding module for ARCHER: preliminary results for photons - Paper 126

ARCHER is a parallel Monte Carlo radiation transport code being developed for various hardware platforms including multi-core CPUs, the emerging Nvidia graphics processing units (GPUs) and Intel Xeon Phi coprocessors. Previous studies have demonstrated the accuracy and speed for CT imaging and radiotherapy dosimetry applications. Radiation shielding applications, however, require ARCHER to be more flexible in defining and handling geometries, in addition to the voxel phantom geometries that are commonly used in medical physics calculations. In this paper, we present the development of a general geometry module used for radiation protection and shielding problems in ARCHER. The methods of geometry design and particle transport for the GPUs are introduced. Preliminary results comparing against the MCNP code for cases involving Cs-137 0.662 MeV gammas are reported. The radiation shielding design module of ARCHER has been developed, which supports general geometry setups in the CSG format. The code was implemented using CUDA on Nvidia GPUs, and the performance was benchmarked against the MCNP code for a simple radiation shielding problem. Results showed that ARCHER worked properly and achieved impressive speedup due to highly parallel computing nature of the Nvidia GPUs compared to the MCNP code running on multi-core CPUs. Further optimization of the geometry module will be carried out using advanced geometry structures and variance reduction techniques to improve the precision and performance of ARCHER. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models

This work presents the use of a high-fidelity neural network surrogate model within a Modular Optimization Framework for treatment of crud deposition as a constraint within light-water reactor core loading pattern optimization. The neural network was utilized for the treatment of crud constraints within the context of an advanced genetic algorithm applied to the core design problem. This proof-of-concept study shows that loading pattern optimization aided by a neural network surrogate model can optimize the manner in which crud distributes within a nuclear reactor without impacting operational parameters such as enrichment or cycle length. Several analysis methods were investigated. Analysis found that the surrogate model and genetic algorithm successfully minimized the deviation from a uniform crud distribution against a population of solutions from a reference optimization in which the crud distribution was not optimized. Strong evidence is presented that shows boron deposition in crud can be optimized through the loading pattern. This proof-of-concept study shows that the methods employed provide a powerful tool for mitigating the effects of crud deposition in nuclear reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Transforming Energy Through Computational Excellence: NREL's Computational Science Center

Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. NREL's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling NREL's clean energy mission.

applied mathematics↗

DOE OSTI2013

SciDAC Center for Gyrokinetic Particle Simulation of Turbulent Transport in Burning Plasmas

During the first year of the SciDAC gyrokinetic particle simulation (GPS) project, the GPS team (Zhihong Lin, Liu Chen, Yasutaro Nishimura, and Igor Holod) at the University of California, Irvine (UCI) studied the tokamak electron transport driven by electron temperature gradient (ETG) turbulence, and by trapped electron mode (TEM) turbulence and ion temperature gradient (ITG) turbulence with kinetic electron effects, extended our studies of ITG turbulence spreading to core-edge coupling. We have developed and optimized an elliptic solver using finite element method (FEM), which enables the implementation of advanced kinetic electron models (split-weight scheme and hybrid model) in the SciDAC GPS production code GTC. The GTC code has been ported and optimized on both scalar and vector parallel computer architectures, and is being transformed into objected-oriented style to facilitate collaborative code development. During this period, the UCI team members presented 11 invited talks at major national and international conferences, published 22 papers in peer-reviewed journals and 10 papers in conference proceedings. The UCI hosted the annual SciDAC Workshop on Plasma Turbulence sponsored by the GPS Center, 2005-2007. The workshop was attended by about fifties US and foreign researchers and financially sponsored several gradual students from MIT, Princeton University, Germany, Switzerland, and Finland. A new SciDAC postdoc, Igor Holod, has arrived at UCI to initiate global particle simulation of magnetohydrodynamics turbulence driven by energetic particle modes. The PI, Z. Lin, has been promoted to the Associate Professor with tenure at UCI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Report

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of subcritical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗