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At least 343 records · Page 19

Evaluating How Climate Adaptation Measures Affect the Interconnected Water‐Energy Resource Systems of the Western United States

Abstract The Western US faces increasing water stress from the impacts of climate change, making it difficult to meet water demands for the region's cities, agriculture, and hydropower generators. Existing literature suggests that climate adaptation measures such as water conservation, cropland retirement, wastewater recycling, and managed aquifer recharge can alleviate some of these challenges. Few analyses, however, compare the relative efficacy and system‐wide effects of these adaptations under different climate projections across the entire Western United States. Here we use a Western US‐wide water systems model to evaluate, by sector and sub‐region, how the widespread implementation of these adaptive measures impacts water demands, water deliveries, and electricity use related to the water system for three different climate projections. We find that wastewater recycling has greater potential to lower unmet indoor water demands than urban indoor water conservation measures. However, when implemented at scale, indoor water conservation reduces electricity use by an average of 683 Terawatt hours while wastewater recycling increases energy use by an average of 721 Terawatt hours, cumulatively from 2020 to 2070. Cropland retirement and aquifer recharge adaptations increase the ability to meet agricultural water demand, increase groundwater storage, and reduce summertime electricity use. While most of these findings are consistent across different climate projections, the benefits of aquifer recharge are sensitive to spatial variation of precipitation. Given the limitations and tradeoffs of each individually, the results suggest that a portfolio of adaptation measures will be needed for a climate‐resilient water and energy future in the Western US. Plain Language Summary The Western US faces increased water stress from the impacts of climate change, making it difficult to meet demands for cities, agriculture, and hydropower facilities. Adaptation measures like water conservation, retiring agricultural lands, recycling wastewater, and storing water underground can address these challenges. However, there is little modeling to understand the impact of these adaptations if they were implemented across the entire Western US while also considering climate change. We use a Western US‐wide water systems model to evaluate how implementing adaptation measures impacts groundwater levels, the ability to meet water demands, and electricity use related to water in three different possible climate futures. We find that recycling wastewater to drinking water standards does a better job of meeting urban water demands than water conservation measures. However, indoor water conservation reduces energy use for water while recycling wastewater increases energy use for water. Retiring agricultural lands and storing water underground both increase the ability to meet agricultural water demands, increase underground water storage, and reduce summertime energy use. No adaptation measures provides benefits across every metric we track. Therefore, multiple adaptation measures will likely be needed to achieve a climate‐resilient future for energy and water in the Western US. Key Points Water‐related climate change adaptations have different benefits and tradeoffs for interconnected water‐energy systems in the Western U.S. Wastewater recycling most increases urban water coverage, but has a significant tradeoff in the form of greater electricity use Cropland retirement and aquifer recharge benefit water and electricity systems most in the summer when those systems are most stressed

Singhal, A↗

Electricity use in big area additive manufacturing of fiber-reinforced polymer composites

In recent years, additive manufacturing (AM), especially large-format additive manufacturing (LFAM), has gained momentum in the manufacturing industry. While LFAM offers benefits over conventional manufacturing processes, such as minimizing material waste and providing vast geometric freedom, assessing its sustainability remains challenging due to limited data, particularly on energy consumption. Most existing data pertain to small-scale or desktop AM and are not directly applicable to LFAM. In this study, we conducted real-time measurements of electricity usage for a type of LFAM known as big area additive manufacturing (BAAM), which typically uses fiber-reinforced polymer pellets as feedstock. We collected electricity usage data from fifteen printing jobs over two months in an industrial production setting. These data fill the existing gap and can be reused to enhance the community’s understanding of LFAM electricity usage, support further research, and promote sustainable development in advanced manufacturing technologies.

ecology↗

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗

Seamless integration of commercial Clouds with ATLAS Distributed Computing

The CERN ATLAS Experiment successfully uses a worldwide dis-tributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing ser-vices have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.

97 MATHEMATICS AND COMPUTING↗

WLCG Token Usage and Discovery

Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token based authentication and authorisation throughout its entire middleware stack. Following the publication of the WLCG Common JSON Web Token (JWT) Schema v1.0 [1] in 2019, middleware developers have been able to enhance their services to consume and validate the JWT-based [2] OAuth2.0 [3] tokens and process the authorization information they convey. Complex scenarios, involving multiple delegation steps and command line flows, are a key challenge to be addressed in order for the system to be fully operational. This paper expands on the anticipated token based workflows, with a particular focus on local storage of tokens and their discovery by services. The authors include a walk-through of this token flow in the RUCIO managed data-transfer scenario, including delegation to FTS and authorised access to storage elements. Next steps are presented, including the current target of submitting production jobs authorised by Tokens within 2021.

Bockelman, Brian↗

Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing

The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during inter-fill periods, in the LHC Run 2. Since then, it has become a nearly transparent extension of the CMS capacity at CERN, being located on-site at the LHC interaction point 5 (P5), where the CMS detector is installed. This resource has been configured to support the execution of critical CMS tasks, such as prompt detector data reconstruction. It can therefore be used in combination with the dedicated Tier 0 capacity at CERN, in order to process and absorb peaks in the stream of data coming from the CMS detector. The initial configuration for this resource, based on statically configured VMs, provided the required level of functionality. However, regular operations of this cluster revealed certain limitations compared to the resource provisioning and use model employed in the case of WLCG sites. A new configuration, based on a vacuum-like model, has been implemented for this resource in order to solve the detected shortcomings. This paper reports about this redeployment work on the permanent cloud for an enhanced support to CMS offline computing, comparing the former and new models’ respective functionalities, along with the commissioning effort for the new setup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Implementing interdisciplinary sustainability education with the food-energy-water (FEW) nexus

Growth in the green jobs sector has increased demand for college graduates who are prepared to enter the workforce with interdisciplinary sustainability skills. Simultaneously, scholarly calls for interdisciplinary collaboration in the service of addressing the societal challenges of enhancing resilience and sustainability have also increased in recent years. However, developing, executing, and assessing interdisciplinary content and skills at the post-secondary level has been challenging. The objective of this paper is to offer the Food-Energy-Water (FEW) Nexus as a powerful way to achieve sustainability competencies and matriculate graduates who will be equipped to facilitate the transformation of the global society by meeting the targets set by the United Nations Sustainable Development Goals. The paper presents 10 curricular design examples that span multiple levels, including modules, courses, and programs. These modules enable clear evaluation and assessment of key sustainability competencies, helping to prepare graduates with well-defined skillsets who are equipped to address current and future workforce needs.

54 ENVIRONMENTAL SCIENCES↗

A Customizable Metric to Provide a Comprehensive Picture of the Mobility Potential of a Location

Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

P SI4 1.4: Open-source software for high-throughput quantum chemistry

PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stochastic evaluation of four-component relativistic second-order many-body perturbation energies: A potentially quadratic-scaling correlation method

A second-order many-body perturbation correction to the relativistic Dirac-Hartree-Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real space of electron coordinates is carried out by the Monte Carlo (MC) method with the Metropolis sampling, whereas the MC integration in the imaginary-time domain is performed by the inverse-CDF (cumulative distribution function) method. The computational cost to reach a given relative statistical error for spatially compact but heavy molecules is observed to be no worse than cubic and possibly quadratic with the number of electrons or basis functions. This is a vast improvement over the quintic scaling of the conventional, deterministic second-order many-body perturbation method. The algorithm is also easily and efficiently parallelized with demonstrated 92% strong scalability going from 64 to 4096 processors for a fixed job size.

74 ATOMIC AND MOLECULAR PHYSICS↗

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

User Impressions and Gait Analysis of Exoskeleton Device Usage in Generalized Tank Farm Activities

Tank farm workers involved in nuclear cleanup activities perform physically demanding tasks, typically while wearing heavy personal protective equipment (PPE). Exoskeleton devices have the potential to bring considerable benefit to this industry but have not been thoroughly studied in the context of nuclear cleanup. In this paper, we examine the performance of exoskeletons during a series of tasks emulating jobs performed on tank farms while participants wore PPE commonly deployed by tank farm workers. The goal of this study was to evaluate the effects of commercially available lower-body exoskeletons on a user’s gait kinematics and user perceptions. Three participants each tested three lower-body exoskeletons in a 70-min protocol consisting of level treadmill walking, incline treadmill walking, weighted treadmill walking, a weight lifting session, and a hand tool dexterity task. Results were compared to a no exoskeleton baseline condition and evaluated as individual case studies. The three participants showed a wide spectrum of user preferences and adaptations toward the devices. Individual case studies revealed that some users quickly adapted to select devices for certain tasks while others remained hesitant to use the devices. Temporal effects on gait change and perception were also observed for select participants in device usage over the course of the device session. Here, device benefit varied between tasks, but no conclusive aggregate trends were observed across devices for all tasks. Evidence suggests that device benefits observed for specific tasks may have been overshadowed by the wide array of tasks used in the protocol.

Tank farm↗

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Supporting multiple hardware architectures at CMS: the integration and validation of POWER9

Computing resources in the Worldwide LHC Computing Grid (WLCG) have been based entirely on the x86 architecture for more than two decades. In the near future, however, heterogeneous non-x86 resources, such as ARM, POWER and Risc-V, will become a substantial fraction of the resources that will be provided to the LHC experiments, due to their presence in existing and planned world-class HPC installations. The CMS experiment, one of the four large detectors at the LHC, has started to prepare for this situation, with the CMS software stack (CMSSW) already compiled for multiple architectures. In order to allow for a production use, the tools for workload management and job distribution need to be extended to be able to exploit heterogeneous architectures. Profiting from the opportunity to exploit the first sizable IBM Power9 allocation available on Marconi100 HPC system at CINECA, CMS developed all the needed modifications to the CMS workload management system. After a successful proof of concept, a full physics validation has been performed in order to bring the system in production. The experiences are of very high value, when it comes to commissioning of the similar (even larger) Summit HPC system at Oak Ridge, where CMS is also expecting a resource allocation. Moreover the compute power of those systems is being provided also via GPUs and this represents an extremely valuable opportunity to exploit the offloading capability already implemented in CMSSW. The status of the current integration including the exploitation of the GPUs, the results of the validation as well as the future plans will be shown and discussed.

Boccali, Tommaso [INFN, Pisa]↗

Optimal calibration of gates in trapped-ion quantum computers

Abstract To harness the power of quantum computing, it is essential that a quantum computer provide maximal possible fidelity for a quantum circuit. To this end, much work has been done in the context of qubit routing or embedding, i.e., mapping circuit qubits to physical qubits based on gate performance metrics to optimize the fidelity of execution. Here, we take an alternative approach that leverages a unique capability of a trapped-ion quantum computer, i.e., the all-to-all qubit connectivity. We develop a method to determine a fixed number (budget) of quantum gates that, when calibrated, will maximize the fidelity of a batch of input quantum programs. This dynamic allocation of calibration resources on randomly accessible gates, determined using our heuristics, increases, for a wide range of calibration budget, the average fidelity from 70% or lower to 90% or higher for a typical batch of jobs on an 11-qubit device, in which the fidelity of calibrated and uncalibrated gates are taken to be 99% and 90%, respectively. Our heuristics are scalable, more than 2.5 orders of magnitude faster than a randomized method for synthetic benchmark circuits generated based on real-world use cases.

Maksymov, Andrii (ORCID:0000000282442851)↗

The impact of halo concentration on the Sunyaev Zel’dovich effect signal from massive galaxy clusters

ABSTRACT The Sunyaev Zel’dovich (SZ) effect is sensitive to the pressure of ionized gas inside galaxy clusters, which is in turn controlled largely by the gravitational potential of the cluster. Changing the concentration parameter describing the cluster mass distribution impacts the gravitational potential and thus the cluster SZ signal, with implications for cosmological and other analyses of SZ-selected clusters. We investigate the concentration–SZ relation in theory and simulations. We find that the impact of concentration on the inner SZ profile (R ≲ 0.75R200c) can be captured with standard polytropic gas models. However, we find that such models do a poor job of reproducing the outer SZ profiles (R ≳ 0.75R200c) and the relation between the integrated SZ signal, Y, and concentration. This disagreement results from a sharp truncation of the gas pressure profile near the splashback radius, likely caused by virial shocks. We develop a simple description of the truncation that leads to a good match with the simulated SZ profiles out to several R200c for clusters of varying mass and concentration, and that also accurately predicts the concentration–Y relationship. Finally, we determine how inference of the linear bias parameter and splashback radius for SZ-selected clusters can be biased by ignoring the concentration dependence of the SZ signal, finding that bias to the former is essentially negligible, while bias to the latter can be as much as 2 per cent.

Astronomy & Astrophysics↗

Pseudonymization at Scale: OLCF’s Summit Usage Data Case Study

The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems, which offer reliable and efficient management of large-scale computational and data resources. Understanding these analyses’ needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to extract workload characteristics from system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user’s point of view with ordinary Unix commands.In this paper, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about the users of OLCF which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset, which reduced the linkability of the dataset to the users’ identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, which is approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.

Maheshwari, Ketan↗