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The goal is to develop an intuitive, dynamic, and consistent interface for the Metis Planning System by combining user requirements and human engineering concepts. The system is largely based upon existing systems so some tools already have working models that we can follow. However, the web-based interface is completely new.
This presentation discusses the design and development of web interfaces that house datasets.
SENSMG is a tool for computing first-order sensitivities of neutron reaction rates, reaction-rate ratios, leakage, k eff , and α using the PARTISN multigroup discrete-ordinates code. SENSMG computes sensitivities to all of the transport cross sections and data (total, fission, nu, chi, and all scattering moments), two edit cross sections (absorption and capture), and the density for every isotope and energy group. It also computes sensitivities to the mass density for every material and derivatives with respect to all interface locations. The tool can be used for one-dimensional spherical (r) and two-dimensional cylindrical (r-z) geometries. The tool can be used for fixed-source and eigenvalue problems. The tool implements Generalized Perturbation Theory (GPT) as discussed by Williams and Stacey. Section II of this report describes the theory behind adjoint-based sensitivities, gives the equations that SENSMG solves, and defines the sensitivities that are output. Section III describes the user interface, including the input file and command line options. Section IV describes the output. Section V gives some notes about the coding that may be of interest. Section VI discusses verification, which is ongoing. Section VII lists needs and ideas for future work. Appendix A lists all of the input files whose results are presented in Sec. VI.
SENSMG is a tool for computing first-order sensitivities of neutron reaction rates, reaction-rate ratios, leakage, keff, α, and subcritical multiplication using the PARTISN multigroup discrete-ordinates code.1 SENSMG computes sensitivities to all of the transport cross sections and data (total, fission,a nu, chi, and all scattering moments), four edit cross sections (absorption, capture, elastic scattering, and inelastic scattering), and the density for every isotope and energy group. It also computes sensitivities to the mass density for every material and derivatives with respect to all interface locations and outer boundaries. The tool can be used for one-dimensional spherical and slab (r) and two-dimensional cylindrical (r-z) geometries.
Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.
A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.
A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.
The computer code relates to human nature studies and more specifically to a novel graphical user interface (GUI) that facilitates high-efficacy collection of perceptual similarity opinions of a user in an effective and intuitive manner. The GUI presents a base image for rating its similarity to seven peripheral images that are displayed simultaneously following a circular layout. The user is asked to report the base image's pairwise similarity to each peripheral image on a fixed scale while preserving the relative ranking among all peripheral images. The collected data are then used to predict the user's subjective opinions regarding the perceptual similarity of images.
Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learningmethods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with uniform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10 Gbps connections with 0–366 ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss ratesbut become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.
The Waste Information Management System (WIMS) -2012 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 40 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and inter-modal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. WIMS continues to successfully accomplish the goals and objectives set forth by DOE for this project. It has replaced the historic process of each DOE site gathering, organizing, and reporting their waste forecast information utilizing different databases and display technologies. In addition, WIMS meets DOE's objective to have the complex-wide waste forecast and transportation information available to all stakeholders and the public in one easy-to-navigate system. The enhancements to WIMS made since its initial deployment include the addition of new DOE sites and facilities, an updated waste and transportation information, and the ability to easily display and print customized waste forecast, the disposition maps, GIS maps and transportation information. The system also allows users to customize and generate reports over the web. These reports can be exported to various formats, such as Adobe{sup R} PDF, Microsoft Excel{sup R}, and Microsoft Word{sup R} and downloaded to the user's computer. Future enhancements will include database/application migration to the next level. A new data import interface will be developed to integrate 2012-13 forecast waste streams. In addition, the application is updated on a continuous basis based on DOE feedback. (authors)
It is demanded to find key issues and critical paths for verifying and monitoring a spent nuclear fuel cask or storage with a purpose of non-proliferation of nuclear weapon. However, unfortunately there are no reliable methods to verify the containment in a spent nuclear fuel cask or storage. Therefore, research and development (R and D) has been continuously required to develop new and alternative technologies for searching and verifying undeclared nuclear materials, activities, and facilities. Therefore, the present authors have developed a new safeguards technique of passively scanning a spent fuel cask or storage using pressurized He-4 scintillation neutron detectors. A new approach is to quickly verify content in a spent nuclear fuel cask or presence of all special nuclear materials in unknown packages by comparing experimental and computational results. For that, the present authors have developed a new algorithm and user-interface coupled ORIGEN-ARP and MCNP codes, and the prototype safeguards apparatus employing the array of He-4 fast neutron detectors has been designed, built, and tested. The feasibility test of this new apparatus has been performed with Cf-252 neutron sources and the laboratory scale model cask, minimized by a factor of ten compared to the actual dimension of a KN-21 cask. This technique has several advantages over current methods based on neutron and gamma detection; (1) incident neutron energy information is preserved, (2) disturbance of gamma-ray is excellently rejected (discriminated), and (3) much higher neutron counts and operational cost and space-effectiveness are expected since no moderator is required for fast neutrons. The final goal of this study will (1) conduct a prototype instrument and employ it to monitor a nuclear spent fuel cask or storage at a commercial nuclear power reactor facilities, (2) use spectral-specific measurement data to show proof of concept for measurable data successfully converted to quantifiable signatures, such as diversion/removal of fuel, and maintenance of fuel and cask integrity of fuel, and (3) develop a cask imaging capability. (authors)
Technological applications of liquid crystals have generally relied on control of molecular orientation at a surface or an interface. Such control has been achieved through topography, chemistry and the adsorption of monolayers or surfactants. The role of the substrate or interface has been to impart order over visible length scales and to confine the liquid crystal in a device. In this work, we report results from a computational study of a liquid-crystal-based system in which the opposite is true: the liquid crystal is used to impart order on the interfacial arrangement of a surfactant. Recent experiments on macroscopic interfaces have hinted that an interfacial coupling between bulk liquid crystal and surfactant can lead to a two-dimensional phase separation of the surfactant at the interface, but have not had the resolution to measure the structure of the resulting phases. To enhance that coupling, we consider the limit of nanodroplets, the interfaces of which are decorated with surfactant molecules that promote local perpendicular orientation of mesogens within the droplet. In the absence of surfactant, mesogens at the interface are all parallel to that interface. As the droplet is cooled, the mesogens undergo a transition from a disordered (isotropic) to an ordered (nematic or smectic) liquid-crystal phase. As this happens, mesogens within the droplet cause a transition of the surfactant at the interface, which forms new ordered nanophases with morphologies dependent on surfactant concentration. Such nanophases are reminiscent of those encountered in block copolymers, and include circular, striped and worm-like patterns.
Software-defined networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, wide-area networks, as well as in ubiquitous communication. In this study, the authors explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, the authors consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. The authors have explored and implemented different SDN-based load-balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with MultiPath transmission control protocol (MPTCP). Furthermore, the proposed results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.
The adoption of small-to-medium sized (1MW~20MW) combined heat and power (CHP) system is lagging, especially in industrial and commercial applications. Our prior research has proved that interfacing CHP with grid-ready converters can increase its economic value and technical benefits; this paper is focused on investigating the critical parameters impacting profitability of converter-interfaced CHP, including CHP sizing scenario, energy price, generator cost, converter cost, voltage support price, converter to engine size ratio and interconnection delay. An automatic toolkit for evaluating CHP return on investment (ROI) is developed to enhance the computation capability and efficiency. Based on varying the value of critical parameters, 975 use cases are analyzed. Results show that the profitability of converter-interfaced CHP over directly-coupled CHP is more sensitive to energy price, delays in interconnection process, converter cost and much less to generator cost or voltage support price.
There is an increased interest in instrument-computing ecosystems (ICEs) that support science workflows empowered by AI-automated experiments and computations in diverse areas. In particular, electrochemistry ICEs are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell, as illustrated in Fig. 1. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. In particular, we developed Python wrappers for Application Programming Interfaces (APIs) of instrument commands and Pyro client-server modules that enable them to be executed from remote computers. The entire workflow is orchestrated by a Jupyter notebook running on a remote computer.
Pore-scale studies of multiphase flow in porous medium systems can be used to understand transport mechanisms and quantitatively determine closure relations that better incorporate microscale physics into macroscale models. Multiphase flow simulators constructed using the lattice Boltzmann method provide a means to conduct such studies, including both the equilibrium and dynamic aspects. Moving, storing, and analyzing the large state space presents a computational challenge when highly-resolved models are applied. We present an approach to simulate multiphase flow processes in which in-situ analysis is applied to track multiphase flow dynamics at high temporal resolution. We compute a comprehensive set of measures of the phase distributions and the system dynamics, which can be used to aid fundamental understanding and inform closure relations for macroscale models. The measures computed include microscale point representations and macroscale averages of fluid saturations, the pressure and velocity of the fluid phases, interfacial areas, interfacial curvatures, interface and common curve velocities, interfacial orientation tensors, phase velocities and the contact angle between the fluid-fluid interface and the solid surface. Test cases are studied to validate the approach and illustrate how measures of system state can be obtained and used to inform macroscopic theory.
In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.
A report was published by the Idaho National Laboratory in September of 2012, entitled Design to Achieve Fault Tolerance and Resilience, which described the benefits of automating operator actions for transients. The report identified situations in which providing additional automation in lieu of operator actions would be advantageous. It recognized that managing certain plant upsets is sometimes limited by the operator’s ability to quickly diagnose the fault and to take the needed actions in the time available. Undoubtedly, technology is underutilized in the nuclear power industry for operator assistance during plant faults and operating transients. In contrast, other industry sectors have amply demonstrated that various forms of operator advisory systems can enhance operator performance while maintaining the role and responsibility of the operator as the independent and ultimate decision-maker. A computerized operator support system (COSS) is proposed for use in nuclear power plants to assist control room operators in addressing time-critical plant upsets. A COSS is a collection of technologies to assist operators in monitoring overall plant performance and making timely, informed decisions on appropriate control actions for the projected plant condition. The COSS does not supplant the role of the operator, but rather provides rapid assessments, computations, and recommendations to reduce workload and augment operator judgment and decision-making during fast-moving, complex events. This project proposes a general model for a control room COSS that addresses a sequence of general tasks required to manage any plant upset: detection, validation, diagnosis, recommendation, monitoring, and recovery. The model serves as a framework for assembling a set of technologies that can be interrelated to assist with each of these tasks. A prototype COSS has been developed in order to demonstrate the concept and provide a test bed for further research. The prototype is based on four underlying elements consisting of a digital alarm system, computer-based procedures, PI&D system representations, and a recommender module for mitigation actions. At this point, the prototype simulates an interface to a sensor validation module and a fault diagnosis module. These two modules will be fully integrated in the next version of the prototype. The initial version of the prototype is now operational at the Idaho National Laboratory using the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Human Systems Simulation Laboratory (HSSL). The HSSL is a full-scope, full-scale glass top simulator capable of simulating existing and future nuclear power plant main control rooms. The COSS is interfaced to the Generic Pressurized Water Reactor (gPWR) simulator with industry-typical control board layouts. The glass top panels display realistic images of the control boards that can be operated by touch gestures. A section of the simulated control board was dedicated to the COSS human-system interface (HSI), which resulted in a seamless integration of the COSS into the normal control room environment.