Challenges and Optimization of Mu2e Proton Target Design with Radiative Cooling
• Introduction • Mu2e target – Status – Optimization • Thermal management • Structural analysis – Stress, Fatigue, Rupture, Creep • Summary
Liu, Zunping [Fermilab]↗
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A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis
To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.
42 ENGINEERING↗
Resonant Extraction and Extinction Measurement for the Mu2e Experiment
The Mu2e experiment, currently under construction at Fermilab, will search for coherent neutrinoless muon to electron conversion, extending the sensitivity of searches for charged lepton flavour violation by four orders of magnitude in 3-5 years of data-taking. This improved sensitivity is made possible by using a pulsed beam structure that is optimized for reducing prompt backgrounds when muons are stopped on an aluminium target. Producing a high-rate pulsed beam is achieved using resonant extraction of a circulating proton beam, an “AC dipole” with a time-varying field to deflect out-of-time protons, and a system to measure the extinction of out-of-time beam particles incident on the muon production target. These proceedings summarize the systems that have been designed to achieve the required level of extinction and to continuously place limits on the presence of out-of-time beam hitting the production target with a sensitivity of <1e-10.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs
A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.
97 MATHEMATICS AND COMPUTING↗
A Challenge for Discrimination of Color-Singlet versus Color-Octet Quarkonium Production
The precise mechanism for production of quarkonium at hadron colliders is still an open question. Within non-relativistic quantum chromodynamics, quarkonium production cross sections can be factorized into short-distance, perturbative contributions and universal, non-perturbative, long-distance matrix elements, and then summed over quantum numbers of the heavy quark pair. In principle, at short-distances, the heavy quark pair can be either in a color-singlet or color-octet state, and it is desirable to establish the relative contributions to compare with data and to make predictions in different experimental environments. From the explicit form of the lowest-order perturbative matrix elements for color-singlet and color-octet production, we show that the structure of the optimal observable for discrimination on phase space, the likelihood ratio, has strong dependence on the angular momentum state of the heavy quark pair. This presents an obstruction for construction of a single, robust discrimination observable that can be applied to production of an arbitrary quarkonium state.
Larkoski, Andrew↗
Development of a Vibration Technique Based on Geometric Optimization for Fatigue Life Evaluation of Sandwich Composite Structures
A major obstacle to obtaining cost-effective experimental data on the fatigue life of sandwich panels is the prohibitive amount of time and cost required to carry out millions of cycles. On the other hand, vibration techniques applied to sandwich geometries fail to match the stress patterns that are obtained from standard flexural fatigue tests. To overcome such limitations, a vibration-based fatigue technique is proposed, which entails the use of sandwich specimens whose geometries are optimized to reproduce the stress distribution observed during three point bend loading while vibrating at the first resonant frequency. The proposed vibration technique was experimentally validated. The results, compared with the average number of cycles to failure at different stress ratios obtained via the Three-Point Bending test, showed high levels of accuracy. The proposed method is robust and time effective and indicates the possibility of attaining fatigue lifetime prediction of a wide class of composite elements, such as sandwich panels.
36 MATERIALS SCIENCE↗
Non-collinear spin structure for DFT calculations of γ- and ε-Pu [Poster]
Optimized non-collinear spin structures for γ- and ε-Pu improve the reliability of DFT calculations.
36 MATERIALS SCIENCE↗
Automated Identification of Molecular Crystals’ Packing Motifs
Packing motifs—patterns in how molecules orient relative to one another in a crystal structure—are an important concept in many subdisciplines of materials science because of correlations observed between specific packing motifs and properties of interest. That said, packing motif data sets have remained small and noisy due to intensive manual labeling processes and insufficient labeling schemes. The most prominent labeling algorithms calculate relative interplanar angles of nearest neighbor molecules to determine the packing motif of a molecular crystal, but this simple approach can fail when neighbors are naively sampled isotropically around the crystal structure. To remedy this issue, here we propose an optimization algorithm, which rotates the molecular crystal structure to find representative molecules that inform the packing motif. We package this algorithm into an automated framework—Autopack—which both optimally rotates the crystal structure and labels the packing motif based on the appropriate neighboring molecules. In this work, we detail the Autopack framework and its performance, which shows improvements compared to previous state-of-the-art labeling methods, providing the first quantitative point of comparison for packing motif labeling algorithms. Furthermore, using Autopack (available at https://ipo.llnl.gov/technologies/software/autopack), we perform the first large-scale study of potential relationships between chemicals’ compositions and packing motifs, which shows that these relationships are more complex than previously hypothesized from studies that used only tens of polycyclic aromatic hydrocarbon molecules. Autopack’s capabilities help pose next steps for crystal engineering research focusing not only on a molecule’s adoption of a specific packing motif but also on new structure–property relationships.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Advanced Modeling and Process-Materials Co-Optimization Strategies for Swing Adsorption Based Gas Separations
This project devised a computational framework for simultaneously co-optimizing pressure swing adsorption process designs along with the sorbent materials (specifically, metal-organic frameworks) to be employed in the associated packed bed columns. The materials optimization aspect involved search over a design space that can describe the material’s molecular structure, while the process optimization aspect considered various process degrees of freedom for steps arising in various cycle configurations. This framework was demonstrated on the separation of nitrogen and carbon dioxide, which arises ubiquitously in a multitude of post-combustion carbon capture and “blue” hydrogen production applications. Our results led to metal-organic framework molecular descriptor choices that are predicted to outperform standard structures used in practice, providing guidance for future metal-organic framework synthesis efforts.
20 FOSSIL-FUELED POWER PLANTS↗
Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging
Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.
Zheng, Ding [Department of Chemistry and the Mater↗
Active learning of ternary alloy structures and energies
Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.
Chemistry↗
Curing process and pore structure of metakaolin-based geopolymers: Liquid-state 1H NMR investigation
Geopolymers are emerging construction materials with lower carbon dioxide emissions compared to the conventional cementitious materials. The knowledge of the curing process and the related pore structures are important for optimizing the properties of these materials for different applications. The curing process and final pore structure are sensitive to the amount of used water, however the specifics are unclear. The curing process and pore structures of metakaolin-based geopolymers with a narrow water-to-solid (w/s) ratio (0.59–0.66) were monitored by nuclear magnetic resonance (NMR) relaxometry and cryoporometry. The 14-day curing process was investigated by monitoring the change of T{sub 2} and T{sub 1} relaxation times and water signal intensity. After the curing, the pore structures were characterized by 2D T{sub 1}-T{sub 2} correlation and T{sub 2}-T{sub 2} exchange measurements of absorbed water. The pore size distributions (PSDs) were measured with NMR cryoporometry and compared to nitrogen physisorption and mercury intrusion porosimetry (MIP) results. We found that the relaxation times decreased as the pore structure of the geopolymers matured during the curing while the dissolution and the condensation periods of the curing were distinguished by the changes in signal amplitude reflecting the proton density. After the curing, three distinct pore sizes and connectivity between pores were identified from T{sub 1}-T{sub 2} and T{sub 2}-T{sub 2} spectra. Their PSDs were measured, and they were found to correspond to two different pore sizes originating from the arrangement of clusters and defective pores. In the narrow w/s ratio (0.59–0.66), the curing times were the same for all samples when cured at 24 °C while the pore sizes were observed to increase as a function of the w/s ratio.
36 MATERIALS SCIENCE↗
Crystal Structure Prediction for Benzene Using Basin-Hopping Global Optimization
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Multiscale topology optimization of compressible and nearly incompressible anisotropic hyperelastic structures using physics-augmented neural networks
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Jadoon, Asghar [University of Texas at Austin]↗
Structural Differences between Morning and Evening Peak in Optimized Water Heaters
Peak reduction is an important concern that can help reduce the growing stress on distribution grids and allow to defer investments in new capacity. Water heaters represent a convenient way of reducing peak, depending on controllability of devices. But while controlling water heaters does allow to shift peak, it also results in rebound effects, which require additional understanding before water heater fleets can be used on a large scale. We attempt to investigate the nature of peak behaviors of water heaters and demonstrate that water heaters are not homogenous in their behavior. Depending on the overall intensity of the use of water, part of the population has higher rebound effect, while part of the population has little or no rebound effect. Even though we do not have sufficient data to statistically evaluate our findings, we use a sample of 42 water heaters in a connected neighborhood to provide an early attempt at discovering and reporting this diversity.
Tsybina, Eve↗
Structural dissection of ergosterol metabolism reveals a pathway optimized for membrane phase separation
Sterols are among the most abundant lipids in eukaryotic cells yet are synthesized through notoriously long metabolic pathways. It has been proposed that the molecular evolution of such pathways must have required each step to increase the capacity of its product to condense and order phospholipids. Here, we carry out a systematic analysis of the ergosterol pathway that leverages the yeast vacuole’s capacity to phase separate into ordered membrane domains. In the post-synthetic steps specific to ergosterol biosynthesis, we find that successive modifications act to oscillate ordering capacity, settling on a level that supports phase separation while retaining fluidity of the resulting domains. Simulations carried out with each intermediate showed how conformers in the sterol’s alkyl tail are capable of modulating long-range ordering of phospholipids, which could underlie changes in phase behavior. Our results indicate that the complexity of sterol metabolism could have resulted from the need to balance lipid interactions required for membrane organization.
Science & Technology - Other Topics↗
Optimization of Nb 3 Sn sputtering on SRF structures
Superconducting radiofrequency (SRF) cavities are the key technology to compact, high-efficiency, and cost-effective CW SRF accelerators. Further improvements in the efficiency can be realized by using novel materials such as Nb 3 Sn coated on the inner surface of SRF cavities. The Nb 3 Sn coatings enable cavity operation at 4 K, where the Nb 3 Sn coated cavities are already very efficient and have high intrinsic quality factors. Advances in Nb3Sn coatings on the inner surface of SRF cavities showed the potential for this material to significantly cut the capital and operating costs of SRF accelerators. Several approaches have been and are being investigated for coating SRF cavities with Nb 3 Sn. Magnetron sputtering is a versatile technique in that it has been shown to produce Nb 3 Sn films on flat Nb target in a single phase and a precisely controlled thickness, low defect levels, and a favorable morphology. Deposition of Nb 3 Sn films on SRF cavities have also shown promising RF superconducting properties. However, sputter coating of Nb 3 Sn on SRF cavities requires further development to be used in accelerators.
43 PARTICLE ACCELERATORS↗