Computation and Control in Complex Experiments
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Miller, W.↗
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Detecting Outliers in Network Transfers with Feature Extraction
Reliable file transfers are essential for successful science computations and experiments that need large complex data files to be moved across long distances. Networks use protocols such as TCP and UDP to support file transfers, and their performance degrades under packet losses and duplications, thereby adversely affecting science applications. Hence, scientists and engineers often monitor network statistics to find and repair network problems that cause such degradations.Outliers or anomalies can be detected using statistical and machine learning methods that highlight abnormal behaviors. However, without knowing what is normal, it is very difficult and subjective to determine the anomalies. In this paper, we investigate statistical and machine learning approaches to extract unknown or unsupervised features from TCP and perfSONAR data sets, with the aim to identify anomalies such as packet loss, duplication and retransmission sequencing that affect file transfer performance. Simple statistical and feature extraction methods (e.g. PCA) have shown that detecting anomalies is simpler if labeled data sets of normal behavior are available. While perfSONAR logs record losses,they do not detect duplications and reordering. We show that PCA applied to TCP statistics is able to determine abnormal behavior in the above cases. In particular, our results show that simple feature extraction techniques provide building blocks for more advanced network monitoring tools that can quickly flag anomalies using simple measurements, thereby helping to build confidence in file transfer reliability.
Rao, Nageswara S.↗
Theory and experiments for silicon based quantum computing.
Abstract not provided.
Carroll, Malcolm S.↗
Atomic Layer Deposition (ALD) to Extend Catalyst Lifetime for Biobased Adipic Acid Production
Robust heterogeneous catalysts are essential for enabling biomass conversion; however, harsh reaction environments introduce durability challenges for many conventional catalyst materials [1]. The hydrogenation of biobased muconic acid to adipic acid is one such emerging chemistry that faces PGM catalyst stability challenges [2]. Muconic acid is a heavily-investigated biobased platform chemical that can be converted into an array of large-market commodity chemicals [2]. PGM catalysts are exceptionally effective for muconic acid hydrogenation to adipic acid, with Pd the most active to date. [2] However, Pd leaches in an acidic environment and this chemistry has a high propensity for fouling. Atomic layer deposition (ALD) is one such material design strategy that has emerged to stabilize supported metal catalysts [3]. ALD coatings are theorized to stabilize supported metal active sites by i) covering high-energy facets most susceptible to degradation, ii) disrupting the physical mobility of active sites, and iii) reinforcing the structure of the underlying catalyst support [3]. However, ALD coatings for catalyst durability with carboxylic acids remains an underdeveloped area of research and literature reports have yet to consider the techno-economic tradeoffs between the ALD manufacturing cost and catalyst lifetime productivity. This study examines low-cycle Al2O3 ALD coatings to stabilize Pd/TiO2 against deactivation during muconic acid hydrogenation. The unique harshness of muconic acid for Pd leaching was evaluated by both experiment and computation. Based on batch reactor screening results, uncoated and ALD coated catalysts were evaluated in a continuous flow reactor for their productivity, stability, and post-reaction regenerability at 700 degrees C. Characterization was performed to assess the impact of ALD coatings on catalyst morphology, as well as following regeneration. Finally, techno-economic analysis models evaluated the value proposition for ALD-coated catalysts within an nth-generation adipic acid biorefinery.
09 BIOMASS FUELS↗
AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations
Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.
Rao, Nageswara↗
Automated quantum error mitigation based on probabilistic error reduction
Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.
McDonough, Benjamin↗
Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)
Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Nuclear Science & Technology March 2023 Highlights
Highlights from the Nuclear Science and Technology directorate for the month of March 2023.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Phenomena Identification and Ranking Table (PIRT) for Heat Pipes
This Phenomena Identification and Ranking Table (PIRT) report provides an evaluation of key phenomena affecting the performance and operational regimes of heat pipes, particularly in the context of heat pipe microreactors (HPMRs). Heat pipes are advanced passive thermal management devices that utilize phase change and capillary action to achieve efficient heat transfer. However, due to the complexity of the phenomena coupled in the heat pipe, including phase change, turbulent transition, and compressibility effects, among others, there is high uncertainty in identifying and ranking the important phenomena affecting the operation of heat pipes and the current knowledge for their modeling and simulation and experimental measurements and instrumentation. This PIRT exercise, conducted as a collaborative effort involving the Department of Energy (DOE) Microreactor Program (MRP), the Nuclear Regulatory Commission (NRC), and university partners systematically identifies, reviews, and prioritizes critical phenomena affecting the operation of heat pipes based on their importance and knowledge levels. The report analyzes phenomena with high importance and low knowledge, such as wick de-wetting, critical heat flux, contact angles, and pressure dynamics, discussing challenges and future research directions for improving their modeling and simulation and experimental measurements. Additionally, the report addresses phenomena with low knowledge that could impact heat pipe operation during non-normal or transient operation, including frozen startup, laminar to turbulent transition, geysering, wick priming, underfilling conditions, surface roughness of the wick, NCGs trapped in the wick, and the timescales of startup and shutdown. This comprehensive evaluation serves as a valuable resource for guiding future research and development efforts, supporting the successful integration of heat pipes into critical applications such as nuclear reactors, and contributing to the advancement of heat pipe technologies in safety-critical industries.
21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
Selected Uses of TSUNAMI in Critical Experiment Design and Analysis
Validation in criticality safety is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 and enable investigation of the parameters at which such a critical condition is achieved. For the critical experiments used in a validation to capture the biases of the materials and neutron energy spectra of interest, those materials must be included in the experiment such that they influence k eff or another observable parameter with statistical significance. This paper discusses the use of sensitivity uncertainty (S/U) methods to develop critical experiments for various purposes. S/U techniques are useful for understanding the underlying components of nuclear data which affect the k eff or another parameter of a given configuration. S/U calculations are most commonly used to compare existing experiments to applications of interest; however, S/U techniques can also be used to identify, optimize, or assess features of proposed experiments so that they can better test specific portions of nuclear data or match an application of interest. The S/U techniques discussed here are from the TSUNAMI code system. The two primary codes discussed in this work are TSUNAMI-3D, which implements the KENO criticality code to calculate the sensitivity of k eff to nuclear data, and TSAR, which calculates the sensitivity of a reactivity difference between two configurations based on their TSUNAMI-3D generated sensitivity profiles. The methods used in these tools are discussed in more detail in the SCALE manual. This paper is one of a series on the development and use of TSUNAMI tools. The other papers address development of TSUNAMI methods and a review of TSUNAMI applications.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Seamless integration of commercial Clouds with ATLAS Distributed Computing
The CERN ATLAS Experiment successfully uses a worldwide dis-tributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing ser-vices have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.
97 MATHEMATICS AND COMPUTING↗
The FIFE Project: Supporting Non-LHC Experiments at Fermilab [PowerPoint]
FabrIc for Frontier Experiments (FIFE) standardizes computing framework for non-LHC FNAL experiments and offers a robust, shared, and modular set of tools for experiments. The FIFE project provides a valuable service for FNAL non-LHC experiments and makes life much easier for those on multiple experiments. Plans include increasing containerization and transition to other. Following existing approaches saves time and effort and enables easy international cooperation. We are eagerly seeking common approaches as widely as possible in future projects (Rucio, HPC, IRIS_HEP, etc.).
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Harness the Power of AI and CI/CD to Fuel Scientific Discovery
The "Harness the Power of AI and CI/CD to Fuel Scientific Discovery" project aims to enhance and automate critical scientific computing systems used in large-scale experiments like CMS at LHC and DUNE at Fermilab. By leveraging GlideinWMS and HEPCloud, this initiative focuses on developing containerized CI/CD pipelines, integrating AI for code quality improvement, and automating security verifications. Participants will gain hands-on experience with distributed computing systems and implement secure communications, contributing to real-world scientific progress and the open-source community.
Nurcellari, Tea↗
Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.
97 MATHEMATICS AND COMPUTING↗
Data Science and Computation for Rapid and Dynamic Compression Experiment Workflows at Experimental Facilities, September 8-11, 2020. Workshop Report
The application of high pressure to materials has enabled discoveries in scientific fields such as planetary science, materials science, and materials synthesis. Recent advances in X-ray user light sources and other facilities, co-location and integration of user facilities with high-pressure drivers, availability of high-performance computing (HPC) platforms, and the development of new data science techniques have created opportunities for, and challenges in, advancing data analytics for rapid and dynamic compression experiments. To address these challenges, harness the emerging technology now available, and expedite scientific discovery, Los Alamos National Laboratory (LANL) hosted a virtual workshop entitled “Data Science and Computation for Rapid and Dynamic Compression Workflows at Experimental Facilities” from September 8 to 11, 2020. The workshop included 95 registered scientists and analytics experts from 15 universities, 9 United States (US) national laboratories, 5 US and European X-ray light sources, neutron sources such as the Los Alamos Neutron Science Center (LANSCE), other big science facilities such as the National Ignition Facility (NIF), and an industry representative. The workshop included 31 invited talks and 4 lightning talks by students and postdocs.
36 MATERIALS SCIENCE↗
Moving closer to experimental level materials property prediction using AI
Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.
36 MATERIALS SCIENCE↗
High Performance Computing Facility Operational Assessment 2016 - Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable systems; creating innovative solutions for high-performance computing (HPC) needs; and managing risks, safety, and security associated with operating one of the most powerful computers in the world. The results can be seen in the cutting-edge science delivered by users and the praise from the research community. Calendar year (CY) 2016 was filled with outstanding operational results and accomplishments: a very high rating from users on overall satisfaction for the third year in a row; the greatest number of core hours delivered to research projects; the largest number of research publications for the life of Titan; and success in delivering on the allocation of 60%, 30%, and 10% of core hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively. These accomplishments, coupled with the extremely high utilization rate, represent the fulfillment of the promise of Titan: maximum use by maximum-size simulations.
97 MATHEMATICS AND COMPUTING↗
US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director's Discretionary (DD) programs, respectively (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).
97 MATHEMATICS AND COMPUTING↗