Highly Accelerated UV Stress Testing for Transparent Flexible Frontsheets
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The overarching objective of the Phase 1 NuScale SMR First-of-a-Kind (FOAK) Nuclear Demonstration Readiness Project was to enhance competitiveness of the U.S. nuclear industry by enabling timely deployment of the NuScale small modular reactor (SMR). The scope of this Phase 1 project continued to advance the licensing and design maturity, particularly in those areas related to supporting customer readiness, supply chain integration, cost competitiveness, and cost confidence. This investment provided by the Government has accelerated development of these designs and technologies so that the existing domestic fleet of nuclear power plants remains viable and the most mature in the nuclear industry. The intent is to have the new, advanced U.S. designs be deployed as early 2026, and be globally competitive. As a part of the First-of-a-Kind Nuclear Demonstration Readiness Project, NuScale has been developing an advanced reactor design, leading the path for other development projects or complex technology advancements for existing plants that have significant technical and licensing risk. The NuScale Power team (NuScale) is advancing licensing, engineering, supply chain development, testing, and other required activities to enhance the innovation and competitiveness of the U.S. nuclear industry by enabling timely deployment of the NuScale SMR. Specifically, NuScale is performing the following activities in Phase 1. Fully supporting the NRC review of the NuScale DCA to ensure approval of a final safety evaluation report by the end of 2020. Improving plant cost confidence and cost competitiveness through design and supply chain advancement, incorporation of constructability best practices, and margin recovery to increase plant power output. Accelerating design maturity, technology development, and operational program readiness to support a customer commitment for plant deployment. The Department of Energy (DOE) Office of Scientific and Technical Information (OSTI), a unit of the Office of Science, fulfills agency-wide responsibilities to collect, preserve, and disseminate both unclassified and classified scientific and technical information (STI) emanating from DOE-funded research and development (R&D) activities at DOE national laboratories and facilities and at universities and other institutions nationwide. This scientific and technical report provides summaries of the analyses and research that was performed by NuScale under this project that achieves the objective of disseminating information to the nuclear industry to ensure the innovations realized are shared for the benefit of the industry at large.
Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub
Transient electronics are a rapidly emerging field due to their potential applications in the environment and human health. Recently, a few studies have incorporated acid-labile imine bonds into polymer semiconductors to impart transience; however, understanding of the structure–degradation property relationships of these polymers is limited. In this work, we systematically design and characterize a series of fully degradable diketopyrrolopyrrole-based polymers with engineered sidechains to investigate the impact of several molecular design parameters on the degradation lifetimes of these polymers. By monitoring degradation kinetics via ultraviolet–visible spectroscopy, we reveal that polymer degradation in solution is aggregation-dependent based on the branching point and M n , with accelerated degradation rates facilitated by decreasing aggregation. Additionally, increasing the hydrophilicity of the polymers promotes water diffusion and therefore acid hydrolysis of the imine bonds along the polymer backbone. The aggregation properties and degradation lifetimes of these polymers rely heavily on solvent, with polymers in chlorobenzene taking six times as long to degrade as in chloroform. We develop a new method for quantifying the degradation of polymers in the thin film and observe that similar factors and considerations (e.g., interchain order, crystallite size, and hydrophilicity) used for designing high-performance semiconductors impact the degradation of imine-based polymer semiconductors. We found that terpolymerization serves as an attractive approach for achieving degradable semiconductors with both good charge transport and tuned degradation properties. This study provides crucial principles for the molecular design of degradable semiconducting polymers, and we anticipate that these findings will expedite progress toward transient electronics with controlled lifetimes.
Alkali antimonides are well established as high efficiency, low intrinsic emittance photocathodes for accelerators and photon detectors. However, conventionally grown alkali antimonide films are polycrystalline with surface disorder and roughness that can limit achievable beam brightness. Ordering the crystalline structure of alkali antimonides has the potential to deliver higher brightness electron beams by reducing surface disorder and enabling the engineering of material properties at the level of atomic layers. In this report, we demonstrate the growth of ordered Cs3Sb films on single crystal substrates 3C-SiC and graphene-coated 4H-SiC using pulsed laser deposition and conventional thermal evaporation growth techniques. The crystalline structures of the Cs3Sb films were examined using reflection high energy electron diffraction and x-ray diffraction diagnostics, while film thickness and roughness estimates were made using x-ray reflectivity. With these tools, we observed ordered domains in less than 10 nm thick films with quantum efficiencies greater than 1% at 530 nm. Moreover, we identify structural features such as Laue oscillations indicative of highly ordered films. We found that Cs3Sb films grew with flat, fiber-textured surfaces on 3C-SiC and with multiple ordered domains and sub-nanometer surface roughness on graphene-coated 4H-SiC under our growth conditions. We identify the crystallographic orientations of Cs3Sb grown on graphene-coated 4H-SiC substrates and discuss the significance of examining the crystal structure of these films for growing epitaxial heterostructures in future experiments.
The development of compact, SRF-based accelerators for applications beyond research is experiencing notable advancements due to the use of cryocoolers for conduction-cooling instead of traditional liquid cryogens. Following the successful demonstration of a single-cell cavity operated through conduction cooling with three two-stage cryocoolers, Jefferson Lab (JLab) has made strides in the operation of a multi-cell resonator. This milestone paves the way for high-energy applications of compact, conduction-cooled SRF machines. The demonstration, carried out in collaboration with General Atomics, will take place in a dedicated horizontal test cryostat (HTC) at their San Diego facility. This presentation will highlight the technological developments, the latest results, and valuable lessons learned.
Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.
Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modes of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.
While energy efficient transportation strategies have the potential to simultaneously slash oil consumption and reduce greenhouse gas (GHG) emissions, a truly sustainable solution will require more than just putting drivers behind the wheels of new fuel-efficient cars. As the only national laboratory dedicated 100% to renewable energy and energy efficiency, the National Renewable Energy Laboratory (NREL) accelerates widespread adoption of high-performance, low-emission, energy-efficient passenger and freight vehicles, as well as alternative fuels and related infrastructure. Researchers collaborate closely with industry, government, and research partners, using a whole-systems approach to design better batteries, drivetrains, and engines, as well as thermal management, energy storage, power electronic, climate control, alternative fuel, combustion, and emission systems. NREL's sustainable transportation research, development, and deployment (RD&D) efforts are not limited to vehicles, roads, and fueling stations. The lab also explores ways to save energy and reduce GHGs by integrating transportation technology advancements with renewable energy generation, power grids and building systems, urban planning and policy, and fleet operations.
Caustic Side Solvent Extraction (CSSX) is currently used at the U.S. Department of Energy (DOE) Savannah River Site (SRS) for removal of cesium from the high-level salt-wastes stored in underground tanks. Currently, the Actinide Removal Process (ARP) and the CSSX process are deployed in the (ARP)/Modular CSSX Unit (MCU), to process salt waste for permanent disposition. The CSSX technology utilizes a multi-component organic solvent and annular centrifugal contactors to extract cesium from alkaline salt waste. The original plant was permitted for a three year design life; however, given the successful operation of the plant, a life extension program was completed to continue operations. The program included detailed engineering analyses of the life-expectancy of passive and active components, resulting in component replacement and/or maintenance and monitoring program improvements. The program also included a review of the operations and resulted in a series of operational improvements. Since the improvements have been made, an accelerated processing rate has been demonstrated. In addition, plans for instituting a next-generation solvent are in place and will enhance the decontamination factors. (author)
The development of compact, SRF-based accelerators for applications beyond research is experiencing notable advancements due to the use of cryocoolers for conduction-cooling instead of traditional liquid cryogens. Following the successful demonstration of a single-cell cavity operated through conduction cooling with three two-stage cryocoolers, Jefferson Lab (JLab) has made strides in the operation of a multi-cell resonator. This milestone paves the way for high-energy applications of compact, conduction-cooled SRF machines. The demonstration, carried out in collaboration with General Atomics, will take place in a dedicated horizontal test cryostat (HTC) at their San Diego facility. This presentation will highlight the technological developments, the latest results, and valuable lessons learned.
Owing to the outstanding characteristics of mass and heat transfer efficiency, two-phase bubbly flows have wide applications in the nuclear engineering fields. However, the physics behind the bubbly flows phenomenon can be very complex due to the various parameters and phenomena involved. Thanks to the increasing computing power and advanced algorithms, computational fluid dynamics (CFD) approach has emerged as an effective tool for exploring the bubbly flows. In typical laminar channel or pipe flow, the flow field develops a parabolic velocity profile due to the wall friction. Assuming the shear rate across the bubble diameter is constant, the bubble will encounter various shear rates depending on its transverse position. Different shear rates may lead to the acceleration of transverse migration speed and influence the bubble rising velocity. In this summary, we evaluate the effect of shear rate on the interfacial forces (lift and drag) acting on a single bubble with the shear rate ranging from 3.8 to 8.3 s{sup -1} and the Eotvos number ranging from 2.58 to 4.45.
Roadway grade is one of the major variables that affects a vehicle’s energy consumption. This study demonstrates the potential benefits of an eco look-ahead control for battery electric vehicles (BEVs). The proposed eco look-ahead controller was developed for the lead vehicle of a platoon of BEVs. The eco look-ahead control predicts the optimum speed and acceleration levels within a preset speed window to minimize energy consumption considering the instantaneous energy used and the regenerated energy. The developed BEV eco look-ahead control system integrates a BEV energy consumption model and a powertrain model to save fuel while maintaining vehicle speed within a user-specified window. In addition, the study demonstrates the effects of roadway grade on BEV energy consumption. The results show that the energy consumption of BEVs are significantly reduced on downhill roads compared with internal combustion engine vehicles owing to regenerative braking. Specifically, testing showed that BEVs could even produce extra energy on downhill roads. The study also tested the eco look-ahead control on an 18-km section of I-81 and found that the system produced savings of 8.51% and 26.88% in the uphill and downhill directions, respectively. This study demonstrated that regenerative energy in BEVs is a critical factor in energy efficiency and the developed eco look-ahead control significantly improved the energy efficiency for BEVs.
Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.
Electrospray (ES) and paper spray (PS) mass spectrometry were used to create confined liquid volumes in which accelerated air and water sensitive, heterogeneous, copper catalysed C–O and C–N coupling reactions occur.
It has been demonstrated that network topology optimization (NTO) may change the topology of power system networks, and consequently, provide additional flexibility to reduce network congestion and violations. Most NTO problems are formulated based on the bus-branch model in which it is challenging to represent a realistic picture of all substation configurations. In this paper, we explore advantages of substation reconfiguration modeling based on node-breaker representations for NTO problem with full nonlinear alternating current power flow. It also proposes a tailored solution algorithm to solve this nonconvex mixed-integer nonlinear programming through the outer approximation method. The proposed solution approach iterates between a mixed-integer linear programming and a nonlinear subproblem. Additional enhancements to further accelerate the iteration process are illustrated. Numerical case studies demonstrate the relative economic and operational impact of optimal network topology with node-breaker representations.
Modern computing platforms whose workloads generate large amounts of network traffic, such as cloud and HPC systems, often suffer from performance bottlenecks associated with the network interface. In order to alleviate the effects of this obstacle, a new generation of accelerators known as ‘SmartNICs’, which are designed to offload low level networking tasks from the processor into the NIC, have emerged.