Analysis of measured photovoltaic module performance for Florida, Oregon, and Colorado locations
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Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance. We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of Eulerian/stationary datasets external to the tracking data for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.
We present performance results for the simulation of proteins suspensions in crowding conditions obtained with MUPHY, a computational platform for multi-scale simulations of real-life biofluidic problems. Previous versions of MUPHY have been used in the past for the simulation of blood flow through the human coronary arteries and DNA translocation across nanopores. The simulation exhibits excellent scalability up to 18, 000 K20X Nvidia GPUs and achieves almost 20 Petaflops of aggregate sustained performance with a peak performance of 27.5 Petaflops for the most intensive computing component. Those figures demonstrate once again the flexibility of MUPHY in simulating biofluidic phenomena, exploiting at their best the features of the architecture in use. Preliminary results were obtained in the present case on a completely different platform, the IBM Blue Gene/Q. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and parallelization techniques required to achieve these results are presented.
High performance LINPACK (HPL) remains the primary benchmark for evaluating supercomputing performance. It includes many parts with substantial internal complexity, and its performance is affected by a large number of parameters that interact in ways that are difficult to predict on large-scale heterogeneous supercomputer systems. We present a comprehensive performance analysis of HPL on Frontier, the world’s first exascale supercomputer, which achieved HPL performance of 1.35 exaflops. Through empirical parameter tuning, detailed modeling, and comparative evaluation, we uncover critical performance insights, share lessons learned, and outline best practices for effective parameter tuning on exascale systems. We introduce and evaluate two novel PDFACT strategies: a dedicated-thread (DT) variant and a GPU-based variant (GPUPDFACT) implementation using HIP cooperative groups, demonstrating that GPU-based factorization outperforms conventional CPU-based PDFACT on Frontier’s architecture. Our findings establish key performance factors for HPL on exascale systems and offer valuable guidance for future high-performance computing and benchmarking efforts.
The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, combining a multidisciplinary approach in the computational analysis of additive manufactured structures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency, without sacrificing performance, while demonstrating versatility to fulfill different mission profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy aerodynamic data, which is particularly important for complex aircraft concepts such as blended wing bodies. To address challenges relating to anisotropic materials, the Tsai–Wu failure criterion is applied to the structural analysis, using CFD solutions as load inputs. Aerodynamic performance results show the low-speed variant attains an endurance of 1 h, 48 min, whereas its high-speed counterpart is 29 min at a 66.7% higher cruise speed. Each variant serves different aspects of small UAS deployment, with low speed envisioned for high-endurance surveying, and high speed for long-range or time-critical missions such as delivery. The experimental and simulation results suggest room for design iteration, in wing area and geometry adjustments. Structural simulations demonstrated the need for airframe improvements to the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to be useful across multiple industries, advocating for further research and design improvements.
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. In this work, we firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. We build the performance model for hash-table and heap-based algorithms, which supports the recipe. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix. Finally, we integrate our implementations into a large-scale protein clustering code named HipMCL, accelerating its SpGEMM kernel by up to 10X and achieving an overall performance boost for the whole HipMCL application by 2.6X.
During the January 1, 2013 – December 31, 2015 contract period, the UW Fusion Technology Institute personnel have actively participated in the ARIES-ACT and FESS-FNSF projects, led the nuclear and thermostructural tasks, attended several project meetings, and participated in all conference calls. The main areas of effort and technical achievements include updating and documenting the nuclear analysis for ARIES-ACT1, performing nuclear analysis for ARIES-ACT2, performing thermostructural analysis for ARIES divertor, performing disruption analysis for ARIES vacuum vessel, and developing blanket testing strategy and Materials Test Module for FNSF.
The goal of the Exascale Data Management Workflows project is to create tools to support techniques for in-situ data analysis and data management. Online data management co-running with simulations accelerates the scientific processes being carried out, provides rapid and timely scientific insights, and can help avoid unnecessary and scientifically invalid simulation computations, particularly when combined with or able to utilize data from experimental instrument and/or from past simulation runs. The University of Oregon team is contributing to the performance instrumentation and analysis aspect of this project. Our focus has been on efficient performance monitoring of data management at scale. The general approach of the MONA project is depicted in Figure 1 below. The figure shows performance monitoring applied to an I/O workflow associated with a scientific simulation, with online measurements captured from this workflow informing methods for workflow reconfiguration and dynamic adjustment. The goal is to maintain suitable levels of Quality of Service for workflow execution, by understanding the under lying causes of workflow performance and behavior. One outcome is workflow performance models able to characterize realistic workflows. Another outcome is performance ’mini-apps’ implementing such workflow behavior. Out of scope for this project are advanced methods for online workflow control.
The recent evolutions of the analysis frameworks and physics data formats of the LHC experiments provide the opportunity of using central analysis facilities with a strong focus on interactivity and short turnaround times, to complement the more common distributed analysis on the Grid. In order to plan for such facilities, it is essential to know in detail the performance of the combination of a given analysis framework, of a specific analysis and of the installed computing and storage resources. This contribution describes performance studies performed at CERN, using the EOS disk-based storage, either directly or through an XCache instance, from both batch resources and highperformance compute nodes which could be used to build an analysis facility. A variety of benchmarks, both synthetic and based on real-world physics analyses and their corresponding input datasets, are utilized. In particular, the RNTuple format from the ROOT project is put to the test and compared to the latest version of the TTree format, and the impact of caches is assessed. In addition, we assessed the difference in performance between the use of storage system specific protocols, like XRootd, and FUSE. The results of this study are intended to be a valuable input in the design of analysis facilities, at CERN and elsewhere.
A novel, low-cost linear Fresnel collector for concentrating solar power applications is being developed by Hyperlight Energy (Hyperlight) and the National Renewable Energy Laboratory (NREL). Hyperlight is currently deploying their linear Fresnel collector technology in a half-acre pilot loop near Brawley, California. The optical performance of a concentrating solar power collector represents the largest loss in the efficiency of the overall system. The ability to accurately model the complex optical interactions of a collector becomes essential in successfully implementing a new collector technology. Here, this study presents a detailed sensitivity analysis of the optical performance of Hyperlight linear Fresnel technology, characterizing the effects of potential optomechanical error sources on collector performance. Optical models are implemented in SolTrace, a Monte-Carlo ray-tracing software developed at NREL. First, SolTrace is used to analyze collector sensitivity to individual optomechanical error perturbations in both the primary reflectors and the receiver assembly. Then, a high-fidelity optomechanical error model is adopted to capture the realistic performance of the installed Hyperlight linear Fresnel collector. Finally, the sensitivity analysis can provide insightful guidance to inform the tightening and relaxation of tolerances during manufacturing and implementation of a new collector technology.
This report presents a concise history in tabular form of events leading up to site identification in 1978, site selection in 1987, subsequent characterization, and ongoing analysis through 2009 of the performance of a repository for spent nuclear fuel and high - level radioactive waste at Yucca Mountain in southern Nevada. The tabulated events generally occurred in five periods: (1) commitment to mined geologic disposal and identification of sites; (2) site selection and analysis, based on regional geologic characterization through literature and analogous data; (3) feasibility analysis demonstrating calculation procedures and importance of system components, based on rough measures of performance using surface exploration, waste process knowledge, and general laboratory experiments; (4) suitability analysis demonstrating viability of disposal system, based on environment - specific laboratory experiments, in - situ experiments, and underground disposal system characterization; and (5) compliance analysis, based on completed site - specific characterization . The current sixth period beyond 2010 represents a new effort to set waste management policy in the United States. Because the relationship is important to understanding the evolution of the Yucca Mountain Project , the tabulation also shows the interaction between the policy realm and technical realm using four broad categories of events : (a) Regulatory requirements and related federal policy in laws and court decisions, (c) Presidential and agency directives, (c) technical milestones of implementing institutions, and (d) critiques of the Yucca Mountain Project and pertinent national and world events related to nuclear energy and radioactive waste. Preface The historical progression of technical milestones for the Yucca Mountain Project was originally developed for 10 journal articles in a special issue of Reliability Engineering System Safety on the performance assessment for the Yucca Mountain license application [1-10]. The listing of mile stones, a distinct feature of those articles, has been collected and tabulated here. A lthough a brief description is presented here (based on the summaries in the 10 journal articles), the emphasis remains on the tabulation because of its usefulness in providing a comprehensive but concise history of the Yucca Mountain Project. The tabulation presented here is more elaborate than originally presented in that many of the interactions that occurred between the technical realm and policy realm can be depicted in separate columns. The usefulness of the milestones table is due in part to L.A. Connolly, for editorial and reference support, and S.K. Best, Raytheon, and L. Mays, Sandia National Laboratories (SNL), for illustration support. Reviewers P.N. Swift, SNL, and K. Gupta, University of Oklahoma, helped improve the discussion. The historical perspective presented is that of the author and is not necessarily held by reviewers, Sandia National Laboratories , and the US Department of Energy. As a historic perspective, the author is reporting on the work of others; however, any interpretative error s of the documentation are those of the author alone. The characterization and modeling of the Yucca Mountain disposal system required numerous participants with expertise in many areas of science and technology, as evident from the extensive reference list. Their diligent efforts are generally acknowledged here and through the many references to their impressive work, but the 10 journal articles acknowledge by name many of the numerous participants that contributed to the Yucca Mountain Project .
This report presents a concise history in tabular form of events leading up to site identification in 1978, site selection in 1987, subsequent characterization, and ongoing analysis through 2008 of the performance of a repository for spent nuclear fuel and high-level radioactive waste at Yucca Mountain in southern Nevada. The tabulated events generally occurred in five periods: (1) commitment to mined geologic disposal and identification of sites; (2) site selection and analysis, based on regional geologic characterization through literature and analogous data; (3) feasibility analysis demonstrating calculation procedures and importance of system components, based on rough measures of performance using surface exploration, waste process knowledge, and general laboratory experiments; (4) suitability analysis demonstrating viability of disposal system, based on environment-specific laboratory experiments, in-situ experiments, and underground disposal system characterization; and (5) compliance analysis, based on completed site-specific characterization. Because the relationship is important to understanding the evolution of the Yucca Mountain Project, the tabulation also shows the interaction between four broad categories of political bodies and government agencies/institutions: (a) technical milestones of the implementing institutions, (b) development of the regulatory requirements and related federal policy in laws and court decisions, (c) Presidential and agency directives and decisions, and (d) critiques of the Yucca Mountain Project and pertinent national and world events related to nuclear energy and radioactive waste.
A commercial HCPV/T system, developed by Solergy, is installed at the airport of Rome, in Italy, as part of a prototype smart grid. The system is rated at 15 kW AC electric and 20 kW thermal and is used to provide both electricity for charging electric vehicles and heat for a conventional thermal power plant. This paper presents an analysis of the performance of the system, operating since March 2017, which achieves a combined peak efficiency of 48%. This study incorporates also an investigation on the improvements that can benefit the system, including a new type of receiver with improved heat dissipation.
In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.
Here, from within the umbrella of the Simplified Human Error Experimental Program (SHEEP) framework, this paper analyzes human performance differences between professional and student operators when using a simplified simulator (i.e., Rancor Microworld). This paper represents a crucial step in understanding the fidelity of the simplified simulators and student operators within the SHEEP study. This paper explores a randomized factorial experimental design that features two independent variables: participant type and event class. Six human performance measurements are considered in the experiment. The experiment is conducted using 20 professional reactor operators employed at actual nuclear power plants (NPPs), along with 20 trained students. The experimental data are analyzed via statistical analysis methods. Finally, this paper examines the differences in human performance between actual operators and students when using Rancor Microworld.
The QGESS: Performing a Techno-Economic Analysis for Power Generation Plants provides guidelines on performing a techno-economic analysis (TEA) of power generation processes. The report includes guidance on preparing a technology analysis plan (TAP), a design basis, performing the technical analysis, performing the economic analysis, and information that should be reported. This guideline references numerous reports that provide examples of TEAs and provides a short description of most of the QGESS reports listed on this web page that provide useful information for specific aspects of performing a TEA.
The QGESS: Performing a Techno-Economic Analysis for Power Generation Plants provides guidelines on performing a techno-economic analysis (TEA) of power generation processes. The report includes guidance on preparing a technology analysis plan (TAP), a design basis, performing the technical analysis, performing the economic analysis, and information that should be reported. This guideline references numerous reports that provide examples of TEAs and provides a short description of most of the QGESS reports listed on this web page that provide useful information for specific aspects of performing a TEA.
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.