Early experiences with node-level power capping on the Cray XC40 platform
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Pedretti, Kevin↗
SEARCH · Engineering Papers
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Multi-faceted framework for extrapolating early age flexural strength to facilitate rapid lifting/handling of high-volume fly ash precast members
Maintaining adequate early-age structural performance for precast concrete components has grown in importance as more sustainable mix designs become more widespread. Achieving high-early flexural strength is particularly crucial to facilitate rapid removal of hardened concrete components from formwork, often within 24 h after fresh concrete placement. Limited research has assessed the effectiveness of traditional design methods in correlating flexural strength with compressive strength for next-generation mix designs, or demonstrated extrapolation of such material performance to larger-scale structural tests. This paper presents a multi-faceted framework to reassess early-age flexural strength for concretes made with relatively high proportions of fly ash from both fresh and harvested sources. Here, the framework provides several pathways, from which the user can select based upon available resources and the specific application, to improve accuracy of early-age cracking moment calculations. Furthermore, the scope includes evaluation of strength performance under curing conditions emulative of those in a precast facility, recommending modulus of rupture equations which are more performance-driven than current design provisions, and experimental tests on prefabricated concrete beams to validate the proposed methodologies. Correlations of early-age strength with both concrete age and maturity measurements compare the effectiveness of utilizing in-situ data to further enhance the prediction methods. Ultimately, the proposed framework helped reduce errors when calculating cracking moment capacity at early ages by tailoring calculations to reflect mix-dependent behavior. Furthermore, most estimates of cracking moment were within 25 % of their corresponding experimental test results, thus promoting confidence for using these strategies with high-volume fly ash precast structures.
42 ENGINEERING↗
Structural Characterization of Bimetallic Nanocrystal Electrocatalysts
Late transition metal nanocrystals find applications in heterogeneous catalysis such as plasmon-enhanced catalysis and as electrode materials for fuel cells, a zero-emission and sustainable energy technology. Their commercial viability for automotive transportation has steadily increased in recent years, almost exclusively due to the discovery of more efficient bimetallic nanocatalysts for the oxygen reduction reaction (ORR) at the cathode. Despite improvements to catalyst design, achieving high activity while maintaining durability is essential to further enhance their performance for this and other important applications in catalysis. Electronic effects arising from the generation of metal-metal interfaces, from plasmonic metals, and from lattice distortions, can vastly improve sorption properties at catalytic surfaces, while increasing durability.[1] Multimetallic lattice-strained nanoparticles are thus an interesting opportunity for fundamental research.[2,3] A colloidal synthesis approach is demonstrated to produce AuPd alloy and Pd@Au core-shell nanoicosahedra as catalysts for electro-oxidations. The nanoparticles are characterized using aberration-corrected scanning transmission electron microscopy (ac-STEM) and large solid angle energy dispersive X-ray spectroscopy (EDS) on an FEI Talos 4-detector STEM/EDS system. Figure 1 shows bright-field (BF) and high-angle annular dark-field (HAADF) ac-STEM images of the alloy and core-shell nanoicosahedra together with EDS line-scans and elemental maps. These structures are unique in that the presence of twin boundaries, alloying, and core-shell morphology could create highly strained surfaces and interfaces. The shell thickness of the core-shell structures observed in HAADF-STEM images is tuned by adjusting the ratio between metal precursors (Figure 2a-f) to produce shells ranging from a few to several monolayers. Specific activity was measured in ethanol electro-oxidation to examine the effect of shell thickness on catalytic activity. A volcano relationship was observed for the core-shell nanoicosahedra having different Pd-shell thicknesses as Pd content is increased (Figure 2g). Durability tests are ongoing for the AuPd system; however, promising ORR materials and morphologies have also been synthesized for a more cost-effective Cu-based system of Cu-CuM (M = Pd, Rh, Pt) core-alloy-shell nanocrystals. The synthesis, characterization, and catalytic behavior of different high-index faceted morphologies of Cu-based materials towards ORR and methanol oxidation catalysis will be discussed, where we show how they exceed the performance of commercial Pd- and Pt- based catalysts. The development of new materials and their characterization is critical to understanding the effects of structure and composition on catalysis. Future efforts are directed at resolving these structures and more industrially relevant fuel cell catalysts in 3D through electron tomography.[4] References: [1] X. Huang, et al., Science 348 (2015) p. 1230. [2] P. Strasser, et al., Nat. Chem. 2 (2010) p. 454. [3] C. Chen, et al., Science 343 (2014) p. 1339. [4] Microscopy performed as part of a user project through ORNL s Center for Nanophase Materials Sciences, which is a U.S. DOE Office of Science User Facility, and instrumentation provided by the U.S. DOE Office of Nuclear Energy, Fuel Cycle R&D Program, and the Nuclear Science User Facilities.
Cullen, David A↗
On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems. In: EASC '16 Proceedings of the Exascale Applications and Software Conference 2016, Article No. 5
The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies such as [5] indicated a recommendable metric for strong scalability from a theoretical viewpoint, which we test here extensively on three parallel machines with different performance characteristics and interconnect networks, namely Mira (IBM Blue Gene/Q), Beskow (Cray XC40) and Titan (Cray XK7). The test cases considered for the simulations correspond to a turbulent flow in a straight pipe at four different friction Reynolds numbers Reτ = 180, 360, 550 and 1000. Considering the linear model for parallel communication we quantify the machine characteristics in order to better assess the scaling behaviors of the code. Subsequently sampling and profiling tools are used to measure the computation and communication times over a large range of compute cores. We also study the effect of the two coarse grid solvers XXT and AMG on the computational time. Super-linear scaling due to a reduction in cache misses is observed on each computer. The strong scaling limit is attained for roughly 5000-10,000 degrees of freedom per core on Mira, 30,000 - 50,0000 on Beskow, with only a small impact of the problem size for both machines, and ranges between 10,000 and 220,000 depending on the problem size on Titan. This work aims at being a reference for Nek5000 users and also serves as a basis for potential issues to address as the community heads towards exascale supercomputers.
Offermans, Nicolas↗
Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR
Abstract The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.
Nguyen, Phu↗
Orange County Photovoltaic Project & Educational COmponent
The purpose of this report is to discuss the projects implemented, utilizing Department of Energy grant funds, to support the use and understanding of renewable energy in Orange County, Florida and the Greater Orlando Area. Orange County is located in the State of Florida and is most popularly referred to as Orlando. The greater Orlando area’s current population is 1,225,267 and in 2015 was the first destination to surpass 60 million visitors. Orange County utilized grant funds to add to the growing demand for access to charging stations by installing one level 2 dual NovaCharge CT4021 electric vehicle charging station at the Orange County/University of Florida Cooperative Extension Center. The charging station is considered a “smart” charger connected to a central network operated by a third party. Data collected includes the number of charging sessions, session start and end times, the electricity usage, greenhouse gases saved and other pertinent data used for reporting purposes. Orange County continues to support the use of electric vehicles in Metro Orlando and this project continues to bring awareness to our public regarding using alternative vehicles. Additionally, we offer all visitors to the Orange County/University of Florida Cooperative Extension Center free charges for their electric vehicles 24 hours a day. Since the operation of the charging station there have been 52 unique driver users, a total of 532.2258 kg of greenhouse gas savings and 159.03 gallons of gasoline savings. The installation of the additional electric vehicle charging station is part of a county-wide goal of promoting implementation of renewable energy technologies as well as supporting the use of electric vehicles including the Drive Electric Orlando & Florida programs. http://driveelectricorlando.com/ & ; http://www.driveelectricflorida.org/ . Grant funds were also used for Outreach and Educational efforts. Educational efforts about renewable energy were accomplished through the continued support as well as a proposed expansion and potential relocation of the Climate Change Education Center. The growth of the Climate Change Education Center focused on 2 educational subsectors. The first educational sector focused on an apprenticeship with university students. The second sector Orange County partnered with a hospitality college that held seminars to educate students in sustainability best practices that would influence the industry as a whole as students take jobs around the U.S. and other parts of the world. Orange County completed five of the originally proposed six educational seminars. The first seminar focused on community based social marketing techniques for driving sustainable behavior changes. The second seminar held was a green team workshop. The third seminar focused on urban sustainability planning. The fourth and fifth seminars held were Florida Energy Code workshops for building inspectors. A sixth transit oriented development seminar in partnership with Rollins College was explored, but was not conducted because the proposed on campus venue was not accessible for an extended period of time due to renovations. Additionally, an ENERGY STAR training program was conducted with students from the University of Central Florida; three of the five buildings assessed received ENERGY STAR ratings; one student completed the training and received their certification as an ENERGY STAR Specialist. Background: Location: Orange County is located in the central region of the State of Florida and is most popularly known for including the City of Orlando. The greater Orlando area’s current population is 1,225,267 and is home to large corporations such as Walt Disney World, Universal Studios Orlando, Ritz Carlton, Darden Restaurants, and the nation’s second largest convention center. Opportunities Identified: Encouraging Sustainability in Major Sectors: The Central Florida economy is largely dependent on the hospitality industry and in 2015 it was the first destination to surpass 60 million visitors. With over 60 million visitors each year, 120,000 hotel rooms, 4,000 restaurants, and 75 theme parks and attractions, the local hospitality industry provided a wealth of opportunity to reduce greenhouse gas emissions through building energy efficiency and renewable energy. Accordingly, Central Florida is an ideal training ground for teaching best practices for sustainability focused on the hospitality industry. Meeting the Growing Demand for Electric Vehicle Infrastructure: According to the 2014 U.S Clean Tech Leadership Index Report, nationwide electric vehicle ownership in the top 50 largest metro areas increased sharply in 2013; the Orlando Metro Area was ranked #5 in the nation in in terms of electric vehicle charging stations per one million people. Additionally, the Orange County 2014-2015 Annual Sustainable Orange County Report indicated there are 300 electric vehicle charging stations in Central Florida (roughly 300 charging stations within ~70 mile radius available to our residents of Orange County). By increasing and improving access to charging stations, Orange County supports the growing demand for renewable energy technologies. Project Achievements/Accomplishments: Installed an Electric Charging Station: Orange County utilized grant funds to install one level 2 dual NovaCharge CT4021 electric vehicle charging station at the Orange County/University of Florida Cooperative Extension Center. The charging station is considered a “smart” charger connected to a central network operated by a third party that monitors the number of charging sessions, session start and end times, the electricity usage, greenhouse gases saved and other pertinent data. During the six months of operation of the charging station there have been 52 unique driver users of the charging station; a total of 532.2258 kg of greenhouse gas savings and 159.03 gallons of gasoline savings. Operation and Potential Relocation of the Orange County Climate Change Education Center: The Climate Change Education Center, originally located within the Orange County Convention Center, served as a central place to inform and educate commercial and residential sectors of environmental, health, and economic benefits of renewable energy, energy efficiency, and greenhouse gas emission reductions. Grant funds were used to pay for the rent required to maintain a lease at the Convention Center, conduct five educational workshops, and for planned upgrades to the Center displays highlighting a number of energy efficiency and renewable energy projects implemented at the Orange County Convention Center and Downtown Orange County. An initial design concept for an upgraded Climate Change Education Center and educational displays was proposed. However, after the Convention Center determined the space occupied by the Climate Change Education Center; a feasibility study for relocation to the proposed area was conducted in October 2013. In December 2013, it was determined that the desired location of the for the Climate Change Education Center at the Orange County Extension Education Center provided too many construction challenges for relocation with too many building retrofits and access problems, including lack of access for loading and unloading exhibits as well as roof drainage relocations and sewer service connection issues. Another suitable location for relocating the Climate Change Education Center could not be located so the project was terminated. The Climate Change Education Center is no longer in existence in Orange County and has been eliminated. Grant funds were used to pay for marketing services for a state wide marketing program including solar & other renewable energy technologies for rebranding and developing marketing materials including web site design and updating. A website was maintained and updated the www.PowerUpGreenEnergy.com website (no longer in service). Sustainability Training for the Hospitality Industry and Major Sectors: To introduce energy efficiency and renewable energy concepts to the hospitality industry, Orange County partnered with a hospitality college that held seminars to educate students in sustainability best practices that would influence the industry as a whole as students take jobs around the U.S. and other parts of the world. Orange County completed five of the originally proposed six education seminars. Seminar topics included: driving sustainable behavior change through community based social marketing; green team programs; urban sustainability planning; and Florida Energy Code workshops for building inspectors. In partnership with Rollins College, a sixth seminar focused on transit-oriented development was explored, but was not conducted due to extended renovations at the proposed on-campus venue. Green Job Training Development: To promote green industry development and on-the-job training in occupations that encourage energy efficiency and resource reduction, Orange County piloted an ENERGY STAR training and apprenticeship program with students from the University of Central Florida; three students applied for the program; one student completed the training and received their certification as an ENERGY STAR specialist; three of the five buildings assessed received ENERGY STAR Ratings.
14 SOLAR ENERGY↗
ASC CSSE Level 2 Milestone #6362: Resilient Asynchronous Many Task Programming Model
This report is an outcome of the ASC CSSE Level 2 Milestone 6362: Analysis of Re- silient Asynchronous Many-Task (AMT) Programming Model. It comprises a summary and in-depth analysis of resilience schemes adapted to the AMT programming model. Herein, performance trade-offs of a resilient-AMT prograrnming model are assessed through two ap- proaches: (1) an analytical model realized by discrete event simulations and (2) empirical evaluation of benchmark programs representing regular and irregular workloads of explicit partial differential equation solvers. As part of this effort, an AMT execution simulator and a prototype resilient-AMT programming framework have been developed. The former permits us to hypothesize the performance behavior of a resilient-AMT model, and has undergone a verification and validation (V&V) process. The latter allows empirical evaluation of the perfor- mance of resilience schemes under emulated program failures and enabled the aforementioned V&V process. The outcome indicates that (1) resilience techniques implemented within an AMT framework allow efficient and scalable recovery under frequent failures, that (2) the abstraction of task and data instances in the AMT programming model enables readily us- able Application Program Interfaces (APIs) for resilience, and that (3) this abstraction enables predicting the performance of resilient-AMT applications with a simple simulation infrastruc- ture. This outcome will provide guidance for the design of the AMT programming model and runtime systems, user-level resilience support, and application development for ASC's next generation platforms (NGPs).
97 MATHEMATICS AND COMPUTING↗
Genome-Scale 13 C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae .
Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called "-omics" data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Furthermore, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a 13 C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering.The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, 13 C metabolic flux analysis, and two-scale 13 C metabolic flux analysis (2S- 13 C MFA). 2S- 13 C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMM library can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae ( S. cerevisiae ). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user's specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community.
-Omics data↗
Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions
There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.
60 APPLIED LIFE SCIENCES↗
Scalable phononic metamaterials: Tunable bandgap design and multi-scale experimental validation
Phononic metamaterials offer unprecedented control over wave propagation, making them essential for applications such as vibration isolation, waveguiding, and acoustic filtering. However, achieving scalable and precisely tunable bandgap properties across different length scales remains challenging. This study presents a user-friendly design framework for phononic metamaterials, enabling ultra-wide bandgap tunability (B/ω c ratios up to 172 %) across multiple frequency ranges and scales. Using finite element simulations of a Yablonovite-inspired unit cell, we establish a comprehensive parametric design space that illustrates how geometric parameters, such as sphere size and beam diameter, controls bandgap width and frequency. The scalability and robustness of the framework are validated through experimental testing on additively manufactured structures at both macro (10 mm) and micro (80 µm) scales, fabricated using Stereolithography and Two-Photon Polymerization. Transmission loss measurements, conducted with piezoelectric transducers and laser vibrometry, closely match simulations in the kHz and MHz frequency ranges, confirming the reliability and consistency of the bandgap behavior across scales. This work bridges theory and experiments at multiple scales, offering a practical methodology for the rapid design of phononic metamaterials and expanding their potential for diverse applications across a broad range of frequencies.
36 MATERIALS SCIENCE↗
Refining HPCToolkit for application performance analysis at exascale
As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.
Adhianto, Laksono↗
SMART Deliverable 6.1.2a: Application of the ORION tool to the IBDP Carbon Storage Site
Forecasting and managing potential induced seismic activity is one of the challenges facing commercialscale geologic carbon sequestration (GCS), as well as other geologic energy extraction and byproduct disposal technologies. Historically, the process to develop robust, science-based forecasts of induced seismicity has required an integrated effort from experts in seismology, geomechanics, and reservoir engineering to manage data, develop and evaluate models of subsurface processes, and to calibrate and interpret the results from a range of models to understand site behavior relative to prescribed standards and in the context of uncertainty in geologic characterization data, forecasting models, and operational scenario uncertainty. The Operational Forecasting of Induced Seismicity (ORION) toolkit is an open-source, observation-based forecasting toolkit that is being co-developed by two U.S. DOE-funded initiatives: the National Risk Assessment Partnership (NRAP) and the Scienceinformed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative. ORION is designed to provide functionality to support decision making about seismic hazard analysis and risk management for GCS stakeholders ranging from the public to site operators to expert seismologists. The tool, which is written as open-source code in the Python programming language, is composed of a desktop graphical user interface (GUI) and an underlying forecasting engine. The forecasting engine uses available reservoir properties, well and fluid injection scenario details, and observed seismic catalog data as inputs to produce a set of temporal and spatio-temporal seismic forecasts.
58 GEOSCIENCES↗
Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Numerical Simulation of Irradiation Induced Swelling for STAR 4.1 Blanket
Here, this article presents the preliminary results of FEM implementation of irradiation-induced swelling, hardening, and creep effect using ANSYS user programmable features (UPFs) applied to material properties. The first liquid metal breeding blanket model for STAR 4.1 tokamak as a part of the virtual prototyping system is under development at Princeton Plasma Physics Laboratory (PPPL). Intense neutron irradiation produces significant changes in the physical and mechanical properties of Fe-(8%–9%)Cr-based reduced activation ferritic martensitic (RAFM) steels. An ANSYS model of the breeding blanket was built to involve these irradiation-induced material property changes with neutron fluence distribution mapped from the MCNP model. Simulation results show that significant structure deformation forms from nuclear swelling, stress, and plastic strain arising from DPA gradient through wall thickness. The material degradation effect is not negligible. This modeling feature can either assist in analyzing the structural behaviors with the influence of nuclear swelling or provide guidance to design the structure to withstand irradiation.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]
The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.
Sirkis, Leah [National Laboratory of the Rockies (↗
WBS 4.1.2.31 Bioproduct Transition System Dynamics
Bioproducts are chemicals derived from a biomass feedstock; they can be produced on their own or as co-products of a biorefinery that also produces biofuels. Bioproducts have historically been difficult to scale up and bring to market, despite a broad understanding developed within BETO of the various conversion processes that can be used to produce bioproducts. Expanding the bioproducts industry by bringing more bioproducts to market would have a variety of positive impacts. Growth in the bioproducts industry would support the biofuels industry by enabling biorefinery co-products that yield additional revenue streams, allowing biofuel selling prices to be reduced and expanding the biofuels market. A larger bioproducts industry also has the potential to reduce carbon and other emissions associated with the U.S. chemical sector and contribute to increased price stability in some areas of the chemical market by shifting portions of the chemical sector away from reliance on fossil feedstocks. The key barrier to realizing these positive impacts is a lack of knowledge around the factors that prevent or enable bioproducts to reach the commercial market. To date, there has been essentially no research done on how successful bioproducts (commercially produced bioproducts that have captured market share) become successful, why failed bioproducts fail, and what the similarities and differences are between successful and failed bioproducts. The current project addresses this knowledge gap with a decision support tool that can be used by investors, technology developers and government agencies to bring more bioproducts to market, thereby simultaneously growing the bioproducts and biofuels industries. Development of the decision support tool was based on research on the technology development process and success rates in analogous industries, data gathered during previous BETO-funded projects, and a series of interviews with subject-matter experts within BETO, the bioproducts industry, and the investment community. This information formed a knowledge base around how bioproducts are developed from applied research to commercial scale was assembled, which was used to build the Bioproduct Transition Dynamics (BTD) model, a system dynamics (SD) model that tracks bioproduct development projects from applied research through the 'valley of death' of piloting and demoing to commercial scale production. The end-of-project goal is to release the BTD, along with supporting documentation, as a transparent open-source model that will inform decisions around bioproduct development and investments and enable more bioproducts to reach the market. The BTD takes as inputs techno-economic data on specific bioproducts, behavioral parameters for developers and investors, expectations around government support, and exogenous market factors. Model outputs include bioproduct technological progress over time, a cash flow statement for the development project, and market share estimates. Following initial model development, a sensitivity analysis was performed on a subset of model inputs to determine, first, if the BTD could be used to answer analysis questions of interest to BETO, and second, which of the inputs considered were most influential on bioproduct success. Results indicate that the BTD can be used to identify scenarios that enable bioproduct development, and that 'soft' or semi-quantitative factors play a large role in determining if a bioproduct will reach the market. A workshop was also held for subject-matter experts and prospective BTD users in July 2018. The workshop covered BTD model logic, input data and assumptions as well as the sensitivity analysis results and a discussion of potential use cases. Workshop feedback was used to identify additional model development tasks and was incorporated into a draft technical report completed in September 2018. BTD model development, validation and application will continue through 2019.
09 BIOMASS FUELS↗
Effects of Granular Control on Customers’ Perspective and Behavior with Automated Demand Response Systems
Automated demand response (Auto-DR) is expected to close the loop between buildings and the grid by providing machine-to-machine communications to curtail loads without the need for human intervention. Hence, it can offer more reliable and repeatable demand response results to the grid than the manual approach and make demand response participation a hassle-free experience for customers. However, many building operators misunderstand Auto-DR and are afraid of losing control over their building operation. To ease the transition from manual to Auto-DR, we designed and implemented granular control of Auto-DR systems so that building operators could modify or opt out of individual load-shed strategies whenever they wanted. This paper reports the research findings from this effort demonstrated through a field study in large commercial buildings located in New York City. We focused on (1) understanding how providing granular control affects building operators’ perspective on Auto-DR, and (2) evaluating the usefulness of granular control by examining their interaction with the Auto-DR user interface during test events. Through trend log analysis, interviews, and surveys, we found that: (1) the opt-out capability during Auto-DR events can remove the feeling of being forced into load curtailments and increase their willingness to adopt Auto-DR; (2) being able to modify individual load-shed strategies allows flexible Auto-DR participation that meets the building’s changing operational requirements; (3) a clear display of automation strategies helps building operators easily identify how Auto-DR is functioning and can build trust in Auto-DR systems.
Schetrit, Oren↗
Agent-Based Modeling for the Circular Economy: Lessons Learned From Three Case Studies
The circular economy (CE) aims at decoupling human activities from resource use, creating wealth in the process. Recently, many scholars have questioned the link between increased circularity and sustainability, resulting in many methodological approaches being developed for that purpose. This presentation summarizes the insights gained from the application of agent-based modeling (ABM) to study the techno-economic and social conditions promoting circularity and sustainability of three technologies: photovoltaic (PV) modules, hard disk drives (HDDs), and wind blades. Four main categories of agents are defined in the ABM: asset owners, service providers (e.g., refurbishers), recyclers, and manufacturers. Two main CE strategies are represented: lifetime extension (through repair or reuse) and recycling. The developed models start by projecting installed capacities and end-of-life (EOL) quantities. Then the theory of planned behavior - a social psychology model explaining behavior adoption based on attitude, peer influence, and costs - is used to model the asset owners' EOL decision (i.e., landfill, recycle or extend the lifetime of the asset). Then, the quantities of assets flowing to the recycler and service provider agents and quantities of materials flowing to manufacturers are computed. Recyclers' economies of scale are dynamically modeled, and the value generated by the CE strategies for the recyclers, service providers, and manufacturers is computed within the model. When data are available, avoided greenhouse gas emissions resulting from the CE strategies adoption are calculated exogenously from the ABM simulations. Results show that with improved used PV modules warranties, the reuse CE strategy adoption increases from 1% to 23% between 2020 and 2050. Similarly, improved standards could enhance HDDs end-users trust in data-wiping - a prerequisite to reuse - leading to a 3-fold increase in the reuse rate and avoid about 5 million tons of CO2 eq by 2050. Regarding wind blades, 5-15 years lifetime extension could reduce EOL blade quantities by 13%. High costs and logistic issues prevent blades from being recycled in greater quantities. One insight from the case studies is the necessity to have mature secondary markets for reuse to be a viable option. Interestingly, PV reuse is limited by the willingness of PV owners to purchase used modules (on the demand side), while HDDs reuse is constrained by the lack of trust toward data-wiping (limiting the supply of used HDDs). The six limits of the CE concepts presented by Korhonen et al. (2018) are finally used to interpret the results. The HDD case study is an exemplary lock-in, where the first accepted practice (shredding) retains most of the market. The PV results illustrate the technical limitation to reuse, as the growing demand cannot be supplied entirely with used PV modules. The wind case study shows the relevance of clearly defining physical flows - what type of waste should wind blades be considered, how should they be transported and landfilled? - a crucial consideration that also applies to PV modules. Finally, the three case studies highlight the relevance of studying a technology's technical, economic, and market material efficiency potentials altogether and the potential benefit of coupling ABM to life cycle assessment.
agent-based modeling↗