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Increasing fish taxonomic and functional richness affects ecosystem properties of small headwater prairie streams
Stream fish can regulate their environment through direct and indirect pathways, and the relative influence of communities with different taxonomic and functional richness on ecosystem properties likely depends on habitat structure. Given this complexity, it is not surprising that observational studies of how stream fish communities influence ecosystems have shown mixed results. In this study, we evaluated the effect of an observed gradient of taxonomic (zero, one, two or three species) and functional (zero, one or two groups) richness of fishes on several key ecosystem properties in experimental stream mesocosms. Our study simulated small (less than two metres wide) headwater prairie streams with a succession of three pool-riffle structures (upstream, middle and downstream) per mesocosm. Additionally, ecosystem responses included chlorophyll a from floating algal mats and benthic algae, benthic organic matter, macroinvertebrates (all as mass per unit area), algal filament length and stream metabolism (photosynthesis and respiration rate). Ecosystem responses were analysed individually using general linear mixed models. Significant treatment (taxonomic and functional richness) by habitat (pools and riffles) interactions were found for all but one ecosystem response variable. After accounting for location (upstream, middle and downstream) effects, the presence of one or two grazers resulted in shorter mean algal filament lengths in pools compared to no-fish controls. These observations suggest grazers can maintain short algal filaments in pools, which may inhibit long filaments from reaching the surface. Accordingly, floating algal mats decreased in mid- and downstream locations in grazer treatment relative to no-fish controls. At the scale of the entire reach, gross primary productivity and respiration were greater in treatments with two grazer species compared to mixed grazer/insectivore or control treatments. Lastly, the distribution of stream resources across habitat types and locations within a reach can therefore be influenced by the taxonomic and functional composition of fishes in small prairie streams. Thus, disturbances that alter diversity of these systems might have unexpected ecosystem-level consequences.
OC6 Phase Ia Definition Document: Validation of Nonlinear Hydrodynamic Loading on the DeepCwind Semisubmersible
The objective of OC6 Phase I is to investigate the persistent under-prediction (about 20% on average) of the structural loads in the OC5-DeepCwind floating semisubmersible wind system, as observed within the previous OC5 Phase II project. The OC5 results indicate that much of this under-prediction originates from the low-frequency response of the system at the surge/pitch natural frequencies, resulting from nonlinear hydrodynamic loading and/or overprediction of hydrodynamic damping. This work package therefore focuses on better understanding this low-frequency response behavior, the applicability of computational-fluid-dynamics (CFD) models and engineering-level hydrodynamic models for predicting the nonlinear hydrodynamic loading, and the level of uncertainty in the measured response characteristics. To address these objectives, two experimental campaigns were developed and performed in 2017 and 2018 by a sub-group of the OC5 project: Floating test campaign: A simplified OC5-DeepCwind semisubmersible model (no turbine, rigid tower) was moored to three taut-spring lines and subjected to motion decay and wave tests (regular and irregular waves), during which motion amplitudes of the model were measured (see Figure 1a). The purpose of this test campaign was to examine the repeatability of the low-frequency response of the system to wave excitation and determine the level of uncertainty in the response behavior.Constrained test campaign: The same model, but without the tower present, was fixed to a carriage and subjected to towing, forced oscillation and wave tests (regular and irregular waves), during which forces exerted upon the model were measured (see Figure 1b). The purpose of this test campaign was to examine the hydrodynamic loading contributions by separating the wave excitation and radiation forces. Both model test campaigns were performed in the concept basin of the Maritime Research Institute Netherlands (MARIN) in Wageningen, the Netherlands and the first test was within the framework of the MARINET2 project. Both experimental campaigns were examined within Phase Ia of the OC6 project. The purpose of this report is to supply the needed information to participants of the OC6 project to build and simulate the experimental campaigns.
Offshore Wind Energy: Technology Below the Water [Slides]
The final webinar in NREL's Offshore Wind Technology Basics series focuses on offshore wind technology below the water, including foundations and cables. Specific aspects of offshore wind that are covered by the webinar include fixed bottom turbines, floating turbines, mooring lines for floating turbines, cabling options and installation, and decommissioning. The webinar also addresses the technology options and development considerations for diverse seafloor and oceanographic characteristics.
Numerical Prediction of Experimentally Observed Behavior of a Scale Model of an Offshore Wind Turbine Supported by a Tension-Leg Platform: Preprint
Realizing the critical importance the role physical experimental tests play in understanding the dynamics of floating offshore wind turbines, the DeepCwind consortium conducted a one-fiftieth-scale model test program where several floating wind platforms were subjected to a variety of wind and wave loading condition at the Maritime Research Institute Netherlands wave basin. This paper describes the observed behavior of a tension-leg platform, one of three platforms tested, and the systematic effort to predict the measured response with the FAST simulation tool using a model primarily based on consensus geometric and mass properties of the test specimen.
Oregon Offshore Wind Site Feasibility and Cost Study
The focus of this presentation is to: Reflect current floating wind technology trends and industry market data in cost models; Provide cost analysis in support of Oregon state energy planners considering floating offshore wind's potential contribution to future state energy supplies.
A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud
Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.
Laser-Induced Self-Assembled Nanostructures on Electron-Transparent Substrates
Currently, one of the challenges in high-resolution transmission electron microscopy (TEM) studies of nanomaterials is to make contamination-free materials in a simple and time-efficient way. We demonstrate a method that combines nanosecond-pulsed laser dewetting of thin films with a film float-off technique to realize nanostructures (NSs) on electron-transparent substrates in a robust and rapid manner. NSs of metal (Ag) and bimetals (AgCo, AuCo) ranging from 20 to 150 nm are synthesized on thin carbon film deposited on mica substrates. The NS/carbon system is subsequently transferred onto TEM grids by a float-off process resulting from debonding of the carbon from mica due to their contrasting hydrophobic nature. Our process enables the fabrication of different NSs on flexible and electron-transparent substrates.
Expert perspectives on the wind plant of the future
Abstract Wind power technology has changed rapidly in recent years. Technology innovation, evolving power markets, and competing land and ocean uses continue to influence the design and operation of wind turbines and plants. Anticipating these trends and their impact on future facilities can inform commercial strategies and research priorities. Drawing from a recent survey of 140 of the world's foremost wind experts, we identify expectations of future wind plant design in 2035, both for onshore and offshore wind. Experts anticipate continued growth in turbine size, to 5.5 (onshore) and 17 MW (offshore), with plants located in increasingly less favorable wind and siting regimes. They expect plant sizes of 1,100 MW for fixed‐bottom and 600 MW for floating offshore wind. Experts forecast enhanced grid‐system value from wind through significant to widespread use of larger rotors, hybrid projects with batteries and hydrogen production, and more. To explain experts' perspectives on future plant design and operation, we identify five mechanisms: economies of unit, plant, and resource scale; grid‐system value economies; and production efficiencies. We characterize learning effects as a moderating influence on the strength of these mechanisms. In combination, experts predict that these design choices support levelized cost of energy reductions of 27% (onshore) and 17%–35% (floating and fixed‐bottom offshore) by 2035 compared to today, while enhancing wind energy's grid service offerings. Our findings provide a much‐needed benchmark for representing future wind technologies in power sector models and address a critical research gap by explaining the economics behind wind energy design choices.
Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability
The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.
ALACRITY: Analytics-Driven Lossless Data Compression for Rapid In-Situ Indexing, Storing, and Querying. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems X. Lecture Notes in Computer Science, vol 8220
High-performance computing architectures face nontrivial data processing challenges, as computational and I/O components further diverge in performance trajectories. For scientific data analysis in particular, methods based on generating heavyweight access acceleration structures, e.g. indexes, are becoming less feasible for ever-increasing dataset sizes. We present ALACRITY, demonstrating the effectiveness of a fused data and index encoding of scientific, floating-point data in generating lightweight data structures amenable to common types of queries used in scientific data analysis. We exploit the representation of floating-point values by extracting significant bytes, using the resulting unique values to bin the remaining data along fixed-precision boundaries. To optimize query processing, we use an inverted index, mapping each generated bin to a list of records contained within, allowing us to optimize query processing with attribute range constraints. Overall, the storage footprint for both index and data is shown to be below numerous configurations of bitmap indexing, while matching or outperforming query performance.
Lithium Divertor Targets and Walls for the ASTER Liquid Stellarator Reactor, Distributed Divertor
Stellarators may have advantages for certain liquid metal options as Plasma Facing Components (PFC) for divertor targets and first walls due to the wide range of possible magnetic configurations, which additionally are free of disruptions and fast field variations. In a previous work (V. Queral et al., IEEE Trans. Plasma Sci. 52, 2024), a concept of stellarator reactor (ASTER-CP) based on swirling Li-molten salts and liquid lithium floating on the molten salt as PFC was presented. The divertor matters were not studied then and, thus, they are being studied and experimentally tested now. The ASTER-CP reactor concept, the initial liquid metal experiments and potential concepts for the ASTER-CP divertor and first wall are reported. Concerning the experiments, several small scale experiments of galinstan in a small rotating cylinder under magnetic field have been produced, including one experiment with high viscosity galinstan-mixture for increased thickness of layer. An experiment of floating lithium on the molten salt LiCl-PbCl2 gave fast volatilization/decomposition of the molten salt. Particularly for divertors, the traditional free-flow, Capillary Porous Systems and ‘divertorlets’ have been studied for application to ASTER-CP. Surface waves (hot spots), lack of enough surface fluid turbulence and excessive fluid speed are the main issues found in fast free-flow. The perhaps original concept of Distributed Divertor and Equi-power Surface is tentatively proposed and studied, taking advantage of stellarator fields and low recycling regime.
Experimentally Determined Phase Diagram for the Barium Sulfide-Copper(I) Sulfide System Above 873 K (600 °C)
The phase diagram of the barium sulfide-copper(I) sulfide system was investigated above 873 K (600 °C) using a custom-built differential thermal analysis (DTA) apparatus. The melting point of barium sulfide was determined utilizing a floating zone furnace. Four new compounds, Ba{sub 2}Cu{sub 14}S{sub 9}, Ba{sub 2}Cu{sub 2}S{sub 3}, Ba{sub 5}Cu{sub 4}S{sub 7}, and Ba{sub 9}Cu{sub 2}S{sub 10}, were identified through quench experiments analyzed with wavelength dispersive X-ray spectroscopy (WDS) and energy dispersive X-ray analysis (EDS). A miscibility gap was observed between 72 and 92 mol pct BaS using both DTA experiments and in situ melts observation in a floating zone furnace. A monotectic was observed at 94.5 mol pct BaS and 1288 K (1015 °C).
Implementing Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle Particle-Mesh
The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with an approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers.
Implementing Molecular Dynamics on Hybrid High Performance Computers - Three-Body Potentials
The use of coprocessors or accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power re- quirements. Hybrid high-performance computers, defined as machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. Although there has been extensive research into methods to efficiently use accelerators to improve the performance of molecular dynamics (MD) employing pairwise potential energy models, little is reported in the literature for models that include many-body effects. 3-body terms are required for many popular potentials such as MEAM, Tersoff, REBO, AIREBO, Stillinger-Weber, Bond-Order Potentials, and others. Because the per-atom simulation times are much higher for models incorporating 3-body terms, there is a clear need for efficient algo- rithms usable on hybrid high performance computers. Here, we report a shared-memory force-decomposition for 3-body potentials that avoids memory conflicts to allow for a deterministic code with substantial performance improvements on hybrid machines. We describe modifications necessary for use in distributed memory MD codes and show results for the simulation of water with Stillinger-Weber on the hybrid Titan supercomputer. We compare performance of the 3-body model to the SPC/E water model when using accelerators. Finally, we demonstrate that our approach can attain a speedup of 5.1 with acceleration on Titan for production simulations to study water droplet freezing on a surface.
Efficient block preconditioned eigensolvers for linear response time-dependent density functional theory
We present two efficient iterative algorithms for solving the linear response eigen- value problem arising from the time dependent density functional theory. Although the matrix to be diagonalized is nonsymmetric, it has a special structure that can be exploited to save both memory and floating point operations. In particular, the nonsymmetric eigenvalue problem can be transformed into a product eigenvalue problem that is self-adjoint with respect to a K-inner product. This product eigenvalue problem can be solved efficiently by a modified Davidson algorithm and a modified locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm that make use of the K-inner product. The solution of the product eigenvalue problem yields one component of the eigenvector associated with the original eigenvalue problem. However, the other component of the eigenvector can be easily recovered in a postprocessing procedure. Therefore, the algorithms we present here are more efficient than existing algorithms that try to approximate both components of the eigenvectors simultaneously. The efficiency of the new algorithms is demonstrated by numerical examples.
Research on impacts of population-related factors on carbon emissions in Beijing from 1984 to 2012
Carbon emissions related to population factors have aroused great attention around the world. A multitude of literature mainly focused on single demographic impacts on environmental issues at the national level, and comprehensive studies concerning population-related factors at a city level are rare. This paper employed STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model incorporating PLS (Partial least squares) regression method to examine the influence of population-related factors on carbon emissions in Beijing from 1984 to 2012. Empirically results manifest that urbanization is the paramount driver. Changes in population age structure have significantly positive impacts on carbon emissions, and shrinking young population, continuous expansion of working age population and aging population will keep on increasing environmental pressures. Meanwhile, shrinking household size and expanding floating population boost the discharge of carbon emissions. Besides, per capita consumption is an important contributor of carbon emissions, while industry energy intensity is the main inhibitory factor. Based upon these findings and the specific circumstances of Beijing, policies such as promoting clean and renewable energy, improving population quality and advocating low carbon lifestyles should be enhanced to achieve targeted emissions reductions. - Highlights: • We employed the STIRPAT model to identify population-related factors of carbon emissions in Beijing. • Urbanization is the paramount driver of carbon emissions. • Changes in population age structure exert significantly positive impacts on carbon emissions. • Shrinking household size, expanding floating population and improving consumption level increase carbon emissions. • Industry energy intensity decreases carbon emissions.
Five grand challenges of offshore wind financing in the United States
Offshore wind energy has the potential to play a critical role in fostering a renewable energy transformation in the United States. This owes to its massive technical potential, strategic location near densely populated coastlines, and - relative to onshore wind and solar - high capacity factors and consistent production. The Biden Administration's target to build 30 GW of offshore wind capacity by 2030 (from 0.04 GW today) requires the creation and swift development of a new industry that interlinks the wind and power industries with the maritime sector. Critical to its success is financing. While financial capital is abundant, deploying it for offshore wind faces major challenges. We identify and describe five grand challenges affecting offshore wind finance in the U.S. Failing to address these challenges may put deployment targets at risk. The challenges include (1) Early years financing: navigating the complexities, timing mismatches, and high costs of projects in the development phase; (2) Policy support for project financial solvency: addressing the uncertainty and systematic transfers of tax credits away from offshore wind, characteristic of the U.S. Investment Tax Credit; (3) Workforce development: building a skilled workforce for an emerging market; (4) Transmission and integration barriers: upgrading the power grid to reliably support large scale offshore wind integration; and (5) Floating wind development: financing the development and scale-up of floating offshore wind technologies. The second challenge has already been solved to a large extent by the Inflation Reduction Act.