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Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set. However, the actual device-level control of a quantum computer is performed via analog pulses. Here, we introduce a compiler that exploits direct control at this microarchitectural level to achieve significant improvements for quantum programs. Unlike quantum optimal control, our approach is bootstrapped from existing gate calibrations and the resulting pulses are simple. Our techniques are applicable to any quantum computer and realizable on current devices. We validate our techniques with millions of experimental shots on IBM quantum computers, controlled via the OpenPulse control interface. For representative benchmarks, our pulse control techniques achieve both 1.6x lower error rates and 2x faster execution time, relative to standard gate-based compilation. These improvements are critical in the near-term era of quantum computing, which is bottlenecked by error rates and qubit lifetimes.
Frequency stabilization of laser light is crucial in both scientific and industrial applications. Technological developments now allow analog laser stabilization systems to be replaced with digital electronics such as field-programmable gate arrays, which have recently been utilized to develop such locking systems. We have developed a frequency stabilization system based on a field-programmable gate array, with emphasis on hardware simplicity, which offers a user-friendly alternative to commercial and previous home-built solutions. Frequency modulation, lock-in detection, and a proportional-integral-derivative controller are programmed on the field-programmable gate array and only minimal additional components are required to frequency stabilize a laser. The locking system is administered from a host-computer which provides comprehensive, long-distance control through a versatile interface. Various measurements were performed to characterize the system. The linewidth of the locked laser was measured to be 0.7 ± 0.1 MHz with a settling time of 10 ms. The system can thus fully match laser systems currently in use for atom trapping and cooling applications.
Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs.
The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the initial software release as ADDER v1.0.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.
The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.0.1. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.
This project was broadly motivated by the need for new hardware that can process information such as images and sounds right at the point of where the information is sensed (e.g. edge computing). The project was further motivated by recent discoveries by group demonstrating that while certain organic polymer blends can be used to fabricate elements of such hardware, the need to mix ionic and electronic conducting phases imposed limits on performance, dimensional scalability and the degree of fundamental understanding of how such devices operated. As an alternative to blended polymers containing distinct ionic and electronic conducting phases, in this LDRD project we have discovered that a family of mixed valence coordination compounds called Prussian blue analogue (PBAs), with an open framework structure and ability to conduct both ionic and electronic charge, can be used for inkjet-printed flexible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. Retention of programmed states is improved by nearly two orders of magnitude compared to the extensively studied organic polymers, thus enabling in-memory compute and avoiding energy costly off-chip access during training. We demonstrate dopamine detection using PBA synapses and biocompatibility with living neurons, evoking prospective application for brain - computer interfacing. By application of electron transfer theory to in-situ spectroscopic probing of intervalence charge transfer, we elucidate a switching mechanism whereby the degree of mixed valency between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) .
The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.
The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.
Coincidence Doppler Broadening (CDB) Positron Annihilation Spectroscopy (PAS) is a material analysis technique that can be used to non-destructively measure characteristics of structural defects in samples. Analyzing and comparing large datasets obtained using this technique, however, can be complicated and time intensive. The Coincidence Doppler Broadening Analysis Program (CDB-AP) is a graphical user interface that facilitates rapid analysis of many data files while using transparent processes. It is already used in three laboratories at Idaho National Laboratory and can be used in laboratories worldwide.
ABSTRACT The shift toward sustainable energy has increased the demand for efficient energy storage systems to complement renewable sources like solar and wind. While lithium‐ion batteries dominate the market, challenges such as safety concerns and limited energy density drive the search for new solutions. Liquid metals (LMs) have emerged as promising materials for advanced batteries due to their unique properties, including low melting points, high electrical conductivity, tunable surface tension, and strong alloying tendency. Enabled by the unique properties of LMs, four key scientific functions of LMs in batteries are highlighted: active materials, self‐healing, interface stabilization, and conductivity enhancement. These applications can improve battery performance, safety, and lifespan. This review also discusses current challenges and future opportunities for using LMs in next‐generation energy storage systems. image
The integration of Griffin into Workbench, by way of the PyGriffin code package, was initiated by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY-2022. PyGriffin was developed as a Python wrapper to Griffin to streamline the complex workflow involving mesh, cross section, and Griffin input generation, code execution, and results postprocessing, by improving user experience with high fidelity neutronics analysis through the Workbench GUI interface. PyGriffin can be used as a standalone application or through the PyARC code suite. FY-2023 saw the continued development of PyGriffin to expand its capabilities including advanced post-processing of simulation results and initial development of the Monte Carlo (MC) cross section generation workflow with the Shift MC code. PyGriffin was also approved for open-source software status in FY-2023, initiating plans to move the code repository into an open location. In addition to PyGriffin development, there were many improvements and developments made in PyARC leading to several software releases (latest version is v2.3.0), but the focus of this report is PyGriffin.
The recent advances in the automation of metadata normalization and the invention of a unified schema — Brick — alleviate the metadata normalization challenge for deploying portable applications across buildings. Yet, the lack of compatibility between existing metadata normalization methods precludes the possibility of comparing and combining them. While generic machine learning (ML) frameworks, such as MLJAR and OpenML, provide versatile interfaces for standard ML problems, they cannot easily accommodate the metadata normalization tasks for buildings due to the heterogeneity in the inference scope, type of data required as input, evaluation metric, and the building-specific human-in-the-loop learning procedure. We propose Plaster, an open and modular framework that incorporates existing advances in building metadata normalization. It provides unified programming interfaces for various types of learning methods for metadata normalization and defines standardized data models for building metadata and timeseries data. Thus, it enables the integration of different methods via a workflow, benchmarking of different methods via unified interfaces, and rapid prototyping of new algorithms. With Plaster, we 1) show three examples of the workflow integration, delivering better performance than individual algorithms, 2) benchmark/analyze five algorithms over five common buildings, and 3) exemplify the process of developing a new algorithm involving time series features. We believe Plaster will facilitate the development of new algorithms and expedite the adoption of standard metadata schema such as Brick, in order to enable seamless smart building applications in the future.
LIQUID (Lipid Informed Quantitation and Identification) is a software program that has been developed to enable users to conduct both informed and high-throughput global liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based lipidomics analysis. This newly designed desktop application can quickly identify and quantify lipids from LC-MS/MS datasets while providing a friendly graphical user interface for users to fully explore the data. Informed data analysis simply involves the user specifying an electrospray ionization mode, lipid common name (i.e. PE(16:0/18:2)), and associated charge carrier. A stemplot of the isotopic profile and a line plot of the extracted ion chromatogram are also provided to show the MS-level evidence of the identified lipid. In addition to plots, other information such as intensity, mass measurement error, and elution time are also provided. Typically, a global analysis for 15,000 lipid targets.
When planning for control room upgrades, nuclear power plants have to deal with a multitude of engineering and operational impacts. This will inevitably include several human factors considerations, including physical ergonomics of workstations, viewing angles, lighting, seating, new communication requirements, and new concepts of operation. In helping nuclear power utilities to deal with these challenges, the Idaho National Laboratory (INL) has developed effective methods to manage the various phases of the upgrade life cycle. These methods focus on integrating human factors engineering processes with the plant’s systems engineering process, a large part of which is the development of end-state concepts for control room modernization. Such an end-state concept is a description of a set of required conditions that define the achievement of the plant’s objectives for the upgrade. Typically, the end-state concept describes the transition of a conventional control room, over time, to a facility that employs advanced digital automation technologies in a way that significantly improves system reliability, reduces human and control room-related hazards, reduces system and component obsolescence, and significantly improves operator performance. To make the various upgrade phases as concrete and as visible as possible, an end-state concept would include a set of visual representations of the control room before and after various upgrade phases to provide the context and a framework within which to consider the various options in the upgrade. This includes the various control systems, human-system interfaces to be replaced, and possible changes to operator workstations. This paper describes how this framework helps to ensure an integrated and cohesive outcome that is consistent with human factors engineering principles and also provide substantial improvement in operator performance. The paper further describes the application of this integrated approach in the strategic modernization program at a nuclear power plant where legacy systems are upgraded to advanced digital technologies through a systematic process that links human factors principles to the systems engineering process. This approach will help to create an integrated control room architecture beyond what is possible for individual subsystem upgrades alone. In addition, several human factors design and evaluation methods were used to develop the end-state concept, including interactive sessions with operators in INL’s Human System Simulation Laboratory, three-dimensional modeling to visualize control board changes.
Control room modernization is one of the most challenging and complex upgrade projects that a nuclear power plant can undertake. It can even have a bigger impact on operations than, for example, turbine replacement. The challenges of migrating an analog control system to a distributed control system are already well known and a number of nuclear utilities have embarked upon various levels of effort to upgrade some of the systems in the control room. When planning for control room upgrades, plants have to deal with a multitude of engineering and operational impacts. This will inevitably include several human factors considerations, including physical ergonomics of workstations, viewing angles, lighting, seating, new communication requirements, and new concepts of operation. In helping nuclear power utilities to deal with these challenges, the Idaho National Laboratory (INL) has developed human factors design and evaluation methods to be used in the development of an end-state control room concept and to manage the various phases of the upgrade life cycle. This included interactive sessions with operators in INL's Human System Simulation Laboratory, and three-dimensional modeling to visualize control board changes. To make the various upgrade phases as concrete and as visible as possible, the end-state concept includes a set of visual representations of the control room before and after various upgrade phases to provide the context and a framework within which to consider the various options in the upgrade. This includes the various control systems, human-system interfaces to be replaced, and possible changes to operator workstations. This paper describes how this framework helps to ensure an integrated and cohesive outcome that is consistent with human factors engineering principles and also provide substantial improvement in operator performance. The paper further describes the application of this integrated approach in the strategic modernization program at a nuclear power plant where legacy systems are upgraded to advanced digital technologies through a systematic process that links human factors principles to the systems engineering process. This approach results in an integrated control room architecture beyond what is possible for individual subsystem upgrades alone. (authors)
Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.
Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.