Thermodynamics of Phase Selection in MnO 2 Framework Structures through Alkali Intercalation and Hydration
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charge transport↗
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Kolmogorov flow in two dimensional strongly coupled dusty plasma
Undriven, incompressible Kolmogorov flow in two dimensional doubly periodic strongly coupled dusty plasma is modelled using generalised hydrodynamics, both in linear and nonlinear regime. A complete stability diagram is obtained for low Reynolds numbers R and for a range of viscoelastic relaxation time τ{sub m} [0 < τ{sub m} < 10]. For the system size considered, using a linear stability analysis, similar to Navier Stokes fluid (τ{sub m} = 0), it is found that for Reynolds number beyond a critical R, say R{sub c}, the Kolmogorov flow becomes unstable. Importantly, it is found that R{sub c} is strongly reduced for increasing values of τ{sub m}. A critical τ{sub m}{sup c} is found above which Kolmogorov flow is unconditionally unstable and becomes independent of Reynolds number. For R < R{sub c}, the neutral stability regime found in Navier Stokes fluid (τ{sub m} = 0) is now found to be a damped regime in viscoelastic fluids, thus changing the fundamental nature of transition of Kolmogorov flow as function of Reynolds number R. A new parallelized nonlinear pseudo spectral code has been developed and is benchmarked against eigen values for Kolmogorov flow obtained from linear analysis. Nonlinear states obtained from the pseudo spectral code exhibit cyclicity and pattern formation in vorticity and viscoelastic oscillations in energy.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
XploreNAS : Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars
Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute efficiencies. However, the intrinsic non-idealities associated with the analog nature of computing in crossbars limits the performance of the deployed DNNs. Furthermore, DNNs are shown to be vulnerable to adversarial attacks leading to severe security threats in their large-scale deployment. Thus, finding adversarially robust DNN architectures for non-ideal crossbars is critical to the safe and secure deployment of DNNs on the edge. This work proposes a two-phase algorithm-hardware co-optimization approach called XploreNAS that searches for hardware efficient and adversarially robust neural architectures for non-ideal crossbar platforms. We use the one-shot Neural Architecture Search approach to train a large Supernet with crossbar-awareness and sample adversarially robust Subnets therefrom, maintaining competitive hardware efficiency. Our experiments on crossbars with benchmark datasets (SVHN, CIFAR10, CIFAR100) show up to ~8–16% improvement in the adversarial robustness of the searched Subnets against a baseline ResNet-18 model subjected to crossbar-aware adversarial training. We benchmark our robust Subnets for Energy-Delay-Area-Products (EDAPs) using the Neurosim tool and find that with additional hardware efficiency–driven optimizations, the Subnets attain ~1.5–1.6× lower EDAPs than ResNet-18 baseline.
97 MATHEMATICS AND COMPUTING↗
Advances in Sensitivity Analysis with MCNP [Slides]
Monte Carlo codes such as MCNP are used extensively in preventing inadvertent nuclear criticalities in processes involving fissionable material. Their ability to do this relies on the accuracy of the simulation tools, the underlying nuclear data libraries, and their validation of relevant experiments. Recent development of an inline method for importance or adjoint weighting with continuous-energy Monte Carlo in MCNP has allowed for the calculation of sensitivity coefficients without the need of meshes or multigroup collapse, unlike previously available tools. These capabilities allow for quantitative comparisons of processes to benchmark experiments and uncertainty quantification for the calculation of biases and safety margins. The current state-of-the-art is discussed, as well as future research avenues.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
2020 Leadership Agenda for Existing Commercial and Multifamily Buildings
Leadership by state and local governments is critical to unlock national energy efficiency opportunities and deliver the benefits of efficiency to all Americans. But related to building energy efficiency, what will it mean to be a public sector leader over the next several years? What are the energy efficiency solutions that cities, counties, and states are implementing today that will make their communities more affordable, livable, healthy, and economically competitive? The SEE Action Network 2020 Leadership Agenda for Existing Commercial and Multifamily Buildings establishes a benchmark for state and local government leadership on improving the energy efficiency of buildings and seeks two-way collaboration among state, local, and federal officials. It defines a suite of innovative, yet practical policies and programs for policymakers to consider implementing by 2020, focusing on six important areas.
99 GENERAL AND MISCELLANEOUS↗
Investigating Similarity Differences for Light-Water-Moderated and Polyethylene-Moderated Systems [Slides]
Validation efforts revealed noticeable differences in c k for light-water and polyethylene moderated systems when compared to experimental systems. Although 1 H-H 2 O and 1 H-poly use some of the same cross section and identical covariance data, the treatment of TSLs in SCALE lead to no contribution to c k between polyethylene application and benchmark experiment. When H 2 O contribution is removed the difference in c k appears to come from 239 Pu chi. Sensitivity profiles and data-induced uncertainty confirm that the polyethylene application more closely resembles the benchmark experiment. Interrogation techniques were used to observe how TSLs and nuclide IDs affect c k . Specific differences between similar systems can be accounted for and examined to help understand differences in similarity (c k ) between systems
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Amino Acid Encoding for Deep Learning Applications
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
Deep-learning↗
Amino Acid Encoding for Deep Learning Applications
Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
Hesham ElAbd↗
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. Despite its importance, the development of a predictive theory of dielectric breakdown has remained a challenge, owing to the complex multiscale nature of this process. We focus on the intrinsic dielectric breakdown field of insulators—the theoretical limit of breakdown determined purely by the chemistry of the material, i.e., the elements the material is composed of, the atomic-level structure, and the bonding. Starting from a benchmark dataset (generated from laborious first principles computations) of the intrinsic dielectric breakdown field of a variety of model insulators, simple predictive phenomenological models of dielectric breakdown are distilled using advanced statistical or machine learning schemes, revealing key correlations and analytical relationships between the breakdown field and easily accessible material properties. Lastly, the models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.
36 MATERIALS SCIENCE↗
Core Noise - Increasing Importance
This presentation is a technical summary of and outlook for NASA-internal and NASA-sponsored external research on core (combustor and turbine) noise funded by the Fundamental Aeronautics Program Subsonic Fixed Wing (SFW) Project. Sections of the presentation cover: the SFW system-level noise metrics for the 2015, 2020, and 2025 timeframes; turbofan design trends and their aeroacoustic implications; the emerging importance of core noise and its relevance to the SFW Reduced-Perceived-Noise Technical Challenge; and the current research activities in the core-noise area, with additional details given about the development of a high-fidelity combustor-noise prediction capability as well as activities supporting the development of improved reduced-order, physics-based models for combustor-noise prediction. The need for benchmark data for validation of high-fidelity and modeling work and the value of a potential future diagnostic facility for testing of core-noise-reduction concepts are indicated. The NASA Fundamental Aeronautics Program has the principal objective of overcoming today's national challenges in air transportation. The SFW Reduced-Perceived-Noise Technical Challenge aims to develop concepts and technologies to dramatically reduce the perceived aircraft noise outside of airport boundaries. This reduction of aircraft noise is critical to enabling the anticipated large increase in future air traffic. Noise generated in the jet engine core, by sources such as the compressor, combustor, and turbine, can be a significant contribution to the overall noise signature at low-power conditions, typical of approach flight. At high engine power during takeoff, jet and fan noise have traditionally dominated over core noise. However, current design trends and expected technological advances in engine-cycle design as well as noise-reduction methods are likely to reduce non-core noise even at engine-power points higher than approach. In addition, future low-emission combustor designs could increase the combustion-noise component. The trend towards high-power-density cores also means that the noise generated in the low-pressure turbine will likely increase. Consequently, the combined result from these emerging changes will be to elevate the overall importance of turbomachinery core noise, which will need to be addressed in order to meet future noise goals.
Hultgren, Lennart S.↗
Interaction of loading pattern and nuclear data uncertainties in reactor core calculations
Along with best-estimate calculations for design and safety analysis, understanding uncertainties is important to determine appropriate design margins. In this framework, nuclear data uncertainties and their propagation to full core calculations are a critical issue. To deal with this task, different error propagation techniques, deterministic and stochastic are currently developed to evaluate the uncertainties in the output quantities. Among these is the sampling based uncertainty and sensitivity software XSUSA which is able to quantify the influence of nuclear data covariance on reactor core calculations. In the present work, this software is used to investigate systematically the uncertainties in the power distributions of two PWR core loadings specified in the OECD UAM-Benchmark suite. With help of a statistical sensitivity analysis, the main contributors to the uncertainty are determined. Using this information a method is studied with which loading patterns of reactor cores can be optimized with regard to minimizing power distribution uncertainties. It is shown that this technique is able to halve the calculation uncertainties of a MOX/UOX core configuration. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Enhancing Critical Infrastructure and Key Resources (CIKR) Level-0 Physical Process Security Using Field Device Distinct Native Attribute Features
Here, the need for improved Critical Infrastructure and Key Resource (CIKR) security is unquestioned and there has been minimal emphasis on Level-0 (PHY Process) improvements. Wired Signal Distinct Native Attribute (WS-DNA) Fingerprinting is investigated here as a non-intrusive PHY-based security augmentation to support an envisioned layered security strategy. Results are based on experimental response collections from Highway Addressable Remote Transducer (HART) Differential Pressure Transmitter (DPT) devices from three manufacturers (Yokogawa, Honeywell, Endress+Hauer) installed in an automated process control system. Device discrimination is assessed using Time Domain (TD) and Slope-Based FSK (SB-FSK) fingerprints input to Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) and Random Forest (RndF) classifiers. For 12 different classes (two devices per manufacturer at two distinct set points), both classifiers performed reliably and achieved an arbitrary performance benchmark of average cross-class percent correct of %C > 90%. The least challenging cross-manufacturer results included near-perfect %C ≈ 100%, while the more challenging like-model (serial number) discrimination results included 90%< %C < 100%, with TD Fingerprinting marginally outperforming SB-FSK Fingerprinting; SB-FSK benefits from having less stringent response alignment and registration requirements. The RndF classifier was most beneficial and enabled reliable selection of dimensionally reduced fingerprint subsets that minimize data storage and computational requirements. The RndF selected feature sets contained 15% of the full-dimensional feature sets and only suffered a worst case %CΔ = 3% to 4% performance degradation.
60 APPLIED LIFE SCIENCES↗
Defect Engineering of Chalcogen-Tailored Oxygen Electrocatalysts for Rechargeable Quasi-Solid-State Zinc-Air Batteries
A critical bottleneck limiting the performance of rechargeable zinc-air batteries lies in the inefficient bifunctional electrocatalysts for the oxygen reduction and evolution reactions at the air electrodes. Hybridizing transition-metal oxides with functional graphene materials has shown great advantages due to their catalytic synergism. However, both the mediocre catalytic activity of metal oxides and the restricted 2D mass/charge transfer of graphene render these hybrid catalysts inefficient. Here, an effective strategy combining anion substitution, defect engineering, and the dopant effect to address the above two critical issues is shown. This strategy is demonstrated on a hybrid catalyst consisting of sulfur-deficient cobalt oxysulfide single crystals and nitrogen-doped graphene nanomeshes (CoO 0.87 S 0.13 /GN). The defect chemistries of both oxygen-vacancy-rich, nonstoichiometric cobalt oxysulfides and edge-nitrogen-rich graphene nanomeshes lead to a remarkable improvement in electrocatalytic performance, where CoO 0.87 S 0.13 /GN exhibits strongly comparable catalytic activity to and much better stability than the best-known benchmark noble-metal catalysts. In application to quasi-solid-state zinc-air batteries, CoO 0.87 S 0.13 /GN as a freestanding catalyst assembly benefits from both structural integrity and enhanced charge transfer to achieve efficient and very stable cycling operation over 300 cycles with a low discharge-charge voltage gap of 0.77 V at 20 mA cm(-2) under ambient conditions.
bifunctional electrocatalysts↗
Second-order wave excitation forces in WEC-Sim/MOST: Implementation, experimental validation, and code-to-code comparison
Accurate prediction of second-order hydrodynamic loads is essential for floating bodies, including floating offshore wind turbines, wave energy converters, and hybrid wind–wave platforms. These nonlinear effects, arising from both sum- and difference-frequency forcing, are critical for capturing key response characteristics but remain challenging to model efficiently. In this work, we extend the open-source Wave Energy Converter Simulator / MATLAB for Offshore Simulation Tool by implementing second-order wave excitation forces, supporting both the full Quadratic Transfer Function formulation and the Newman approximation. The full Quadratic Transfer Function method is used for all code-to-code comparisons and experimental validation, while the Newman approximation is provided as a computationally lighter alternative. To benchmark the new capability, we perform a code-to-code comparison with OpenFAST and OrcaFlex. We then validate the enhanced model using wave-tank measurements of a 1:96 scale DeepCwind semi-submersible, showing that second-order effects are required to reproduce platform motions. The implementation employs a computationally efficient pre-computation strategy for second-order wave excitation forces, reducing simulation cost while maintaining engineering accuracy. Overall, this work advances the tool as an open-source and versatile tool for modelling floating offshore renewable-energy systems requiring second-order hydrodynamic fidelity.
17 WIND ENERGY↗
“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.
Li, Zhimin [Univ. of Utah, Salt Lake City, UT (Uni↗
ATRC Neutron Detector Testing Quick Look Report
As part of the Advanced Test Reactor (ATR) National Scientific User Facility (NSUF) program, a joint Idaho State University (ISU) / French Alternative Energies and Atomic Energy Commission (CEA) / Idaho National Laboratory (INL) project was initiated in FY-10 to investigate the feasibility of using neutron sensors to provide online measurements of the neutron flux and fission reaction rate in the ATR Critical Facility (ATRC). A second objective was to provide initial neutron spectrum and flux distribution information for physics modeling and code validation using neutron activation based techniques in ATRC as well as ATR during depressurized operations. Detailed activation spectrometry measurements were made in the flux traps and in selected fuel elements, along with standard fission rate distribution measurements at selected core locations. These measurements provide additional calibration data for the real-time sensors of interest as well as provide benchmark neutronics data that will be useful for the ATR Life Extension Program (LEP) Computational Methods and V&V Upgrade project. As part of this effort, techniques developed by Prof. George Imel will be applied by Idaho State University (ISU) for assessing the performance of various flux detectors to develop detailed procedures for initial and follow-on calibrations of these sensors. In addition to comparing data obtained from each type of detector, calculations will be performed to assess the performance of and reduce uncertainties in flux detection sensors and compare data obtained from these sensors with existing integral methods employed at the ATRC. The neutron detectors required for this project were provided to team participants at no cost. Activation detectors (foils and wires) from an existing, well-characterized INL inventory were employed. Furthermore, as part of an on-going ATR NSUF international cooperation, the CEA sent INL three miniature fission chambers (one for detecting fast flux and two for detecting thermal flux) with associated electronics for assessment. In addition, Prof. Imel, ISU, has access to an inventory of Self-Powered Neutron Detectors (SPNDs) with a range of response times as well as Back-to-Back (BTB) fission chambers from prior research he conducted at the Transient REActor Test Facility (TREAT) facility and Neutron RADiography (NRAD) reactors. Finally, SPNDs from the National Atomic Energy Commission of Argentina (CNEA) were provided in connection with the INL effort to upgrade ATR computational methods and V&V protocols that are underway as part of the ATR LEP. Work during fiscal year 2010 (FY10) focussed on design and construction of Experiment Guide Tubes (EGTs) for positioning the flux detectors in the ATRC N-16 locations as well as obtaining ATRC staff concurrence for the detector evaluations. Initial evaluations with CEA researchers were also started in FY10 but were cut short due to reactor reliability issues. Reactor availability issues caused experimental work to be delayed during FY11/12. In FY13, work resumed; and evaluations were completed. The objective of this "Quick Look" report is to summarize experimental activities performed from April 4, 2013 through May 16, 2013.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap
One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.
99 GENERAL AND MISCELLANEOUS↗
Detector Performances Studies at Muon Collider
The Muon Collider is a possible option for the next generation of high energy collider machines. It would permit to achieve the energy frontier in lepton collisions, without occurring in significant synchrotron radiation losses as in electrons rings.Among the technological challenges in the realization of such a machine, the treatment of the beam-induced background is one of the most critical issues for the detectors.Beams with intensity of the order of $10^{12}$ muons per bunch are necessary to obtain the desired luminosity, therefore the muons decay rate is very high. Beam decay products and subsequent particles from secondary interactions with the machine elements can reach the interaction point, limiting the physical performance of the detector. A study of the beam-induced background is presented together with possible strategies for its reduction. Preliminary results on the reconstruction of a benchmark process, $\mu^+\mu^-\to H\nu\bar{\nu}\to b \bar{b}\nu\bar{\nu}$, including the beam-induced background are illustrated as demonstration of physics measurements feasibility in this harsh environment.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗