SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
eProject Builder (ePB) is a secure, web-based system that enables agencies and ESCOs to securely: (1) preserve, track, and access energy project information for the life of the contract (2) quickly generate data and reports for their portfolio of projects (3) develop project scenarios using standardized amortization calculations (4) benchmark new projects against historical project data
EScp is a high speed transfer tool with a similar command line syntax to scp. Unlike SCP it is designed to transfer files at high speed, thus far we have been able to show 100gbit/s transfers, although I expect that the throughput should scale in proportion to the network interface, i.e. I expect 400gbit/s performance on our 400gbit/s test bed. EScp achieves good performance through an innovative design (multithreaded, zero copy transfers), along with pluggable filters and I/O engines. As an example, you can switch from POSIX i/O to UIO by checking a different engine. It also natively supports encryption, and cheksums for file verification and transport security. AAA is through standard SSH (same as SCP). By taking advantage of filters, EScp supports transferring unstructured data and/or I/O to non-posix data sources. Examples include streaming data (i.e. from equipment), transferring data to the cloud, and/or supporting non-posix file systems (like HPSS).
The Mu2e experiment at Fermilab will enable the search for the neutrinoless muon to electron conversion in the field of an Al nucleus, a charged lepton flavor violating process. If observed, there would be a clear indication of physics beyond the Standard Model. Mu2e aims to reach a single event sensitivity of $3 /times 10^{-17}$, improving from the previous limit by 4 orders of magnitude. This improvement relies on the development of trigger selection systems, designed to discard data from background-induced events by placing kinematic, topological cuts on a particle’s reconstructed track. One of the largest sources of background Mu2e faces is proton-antiproton annihilation. These annihilations produce a 2 GeV shower of particles, among which there could be an electron that mimics the conversion electron signal, with an expected number of 0.010 ± 0.010. The large uncertainty on this number is dominated by the systematic uncertainty associated with the theoretical production model. To better characterize this background, we have developed an antiproton trigger selection by taking advantage of the track multiplicity and topology of these events. We discuss the steps taken in this development and the first performance study of this trigger, evaluating the signal efficiency and background rate. This trigger is essential to enable a data-driven analysis targeting the reduction of the systematic uncertainty of the antiproton-induced background in the Mu2e experiment.
Additional ventilation capacity has been designed for the 3430 Building filtered exhaust stack system. The updated system will increase the number of fans from two to three and include new ductwork with a larger diameter to integrate the new fan into the existing stack. Stack operations will involve running various fan combinations at any given time. The air monitoring system of the existing two-fan stack previously was found to comply with the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard. Full-scale, three-dimensional computational fluid dynamics (CFD) modeling was used to evaluate the modified three-fan system for compliance with the ANSI/HPS N13.1-2011 standard, which essentially is equivalent to the ANSI/HPS N13.1-1999 standard. The four mixing criteria evaluated are 1) flow angle, 2) velocity, 3) gas tracer, and 4) particle tracer. Benchmarking of the CFD modeling methodology showed good agreement with previous testing used to qualify the stack, and modeling of the existing two-fan system showed good agreement with test data collected from the 3430 Building stack. Modeling was performed to develop a suitable three-fan design. Initial modeling of the three-fan design and basic ductwork showed that flow angles and velocity uniformity were acceptable; however, the gas tracer and particle tracer mixing results were not acceptable. To meet ANSI/HPS N13.1-2011 criteria, an air blender was added to the stack design. This revision models the individual maximum fan reduced flow capacity from 38,000 cfm to 31,200 cfm; no changes to the duct design are made.
Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.
Abstract The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.
This paper examines national and tribal collaborative opportunities to get ahead of the critical infrastructure insecurity problem. Recommendations are viewed through the lens of the Sandia Labs Tribal Cyber-Energy initiative and national security projects. Recommendations include 1) Collaboratively address national priority and shared challenges to gain faster and better solutions to national priority problems on a smaller yet comprehensive American Indian and Alaskan Native sovereign single-point of authority scale 2) Utilize newer standards-based technologies to provide scalable, capable, and manageable solutions for greatly expanded and connected national critical infrastructures 3) Employ Cyber-Physical-Resilient design preliminary analysis to define concept- to-disposition design requirements for preemptive critical infrastructure risk mitigation and baked-in security; 4) Develop data-centric protection to provide increased information asset protection as data shifts from data-owner operated on-premises infrastructure to virtual service provider data-steward owned and operated off-premises infrastructure; and 5) Balance shared solutions with the National Institute of Science and Technology (NIST) Cybersecurity and Risk Management frameworks, and the System Security Engineering Guidelines. As yet unallocated federal funding would support research, development, the timely application of National-Tribal critical infrastructure protection, and critical infrastructure Cyber disruption response and recovery with extraordinary mutual benefits for the foreseeable future. The Critical Infrastructure Insecurity Problem: Rapid modernization and expansive connectivity are due to advances in Information and Communications Technologies that have sweeping cyber impact across all critical infrastructure sectors. Supervisory Control and Data Acquisition and Industrial Control Systems are particularly impacted as systems long separated from the Internet are now being connected and computerized. Virtualization and mobility create a Data Everywhere-User Anywhere paradigm that has evaporated the enterprise network perimeter. There are multi-front technological challenges at play, where long depended on technologies simply don't scale to current needs resulting in a digital dichotomy of competing old and new standards. New standards-based technologies scale but are not as well-known or as widely deployed, which leaves decision makers, stakeholders, and the workforce in a quandary, caught mid-stream between the technological past and the virtual future. Rapid and expansive cyber threat accompanies disruptive change in connectivity and computational dependencies. A lack of action will exacerbate the problem if new technologies roll out without baked-in security design. The Risk: If National-Tribal CIP collaboration to design in security is not done, then an ongoing state of insufficient bolt-on security and elevated threat exposure will remain for years to come.
The position accuracy of Navigation with Indian Constellation (NavIC) system is affected by several sources of errors. Among them, the Ionospheric Time Delay (ITD) error is the most predominant one which depends upon the total electron content (TEC) present in the ionosphere. The ITD variations are more intense over low latitude regions due to the equatorial anomaly effects. Hence, modelling of ITD error is necessary. The International Reference Ionosphere (IRI)-2016 model is one of the standard global ionospheric models to estimate the Vertical TEC (VTEC). This paper discusses about the VTEC deviations due to the IRI-2016 model over low latitude Hyderabad station, Indian region using NavIC, Global Positioning System and Global Navigation Satellite System signals at corresponding Ionospheric Pierce Point latitude and longitudes for all months and various Kp indices during the low solar activity year 2017. In this work, cross correlation coefficient, the metric norm (L2N), Symmetric Kullbacke Leibler Distance metrics are used to evaluate the performance of IRI-2016 model TEC with NavIC, GPS and GLONASS data. From the results, it is found that TEC predicted by the IRI-2016 model produced smaller estimation errors with NavIC data over Indian region. The obtained results will be helpful for future updates of IRI model.
Differential cross sections for top quark pair ($\textrm{t}\overline{\textrm{t}}$) production are measured in proton-proton collisions at a center-of-mass energy of 13 TeV using a sample of events containing two oppositely charged leptons. The data were recorded with the CMS detector at the CERN Large Hadron Collider and correspond to an integrated luminosity of 138 fb −1 . The differential cross sections are measured as functions of kinematic observables of the $\textrm{t}\overline{\textrm{t}}$ system, the top quark and antiquark and their decay products, as well as of the number of additional jets in the event. The results are presented as functions of up to three variables and are corrected to the parton and particle levels. When compared to standard model predictions based on quantum chromodynamics at different levels of accuracy, it is found that the calculations do not always describe the observed data. The deviations are found to be largest for the multi-differential cross sections.
A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.
A detailed description of the Langmuir probe system on Mega Ampere Spherical Tokamak Upgrade is presented. The system features 850 tile-embedded probes and 40 bespoke electronic modules that each have the capability to drive and acquire data from up to 16 probes in a time-multiplexed manner. The system provides spatiotemporal-resolved measurements (1 cm and ~1 ms, respectively) in the divertor region of ion saturation current, electron temperature, and floating potential. The standard interpretation of current-voltage (IV) characteristics is to apply a four-parameter fit, based on unmagnetized probe theory, which includes a linear model for the ion saturation region. In conclusion, to mitigate the effect of the magnetic field, analysis is restricted to the region of the IV characteristic, which is sensitive to only the tail of the electron energy distribution function.
FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication, thereby mitigating the von Neumann bottleneck. Existing work, however, has been generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs, but most control tasks are left to the CPUs. This leads to frequent waiting, inferior performance, and scaling challenges. In this work, we propose a new distributive data-centric computing framework, named FCsN, for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture, FCsN allows the complete detaching of kernel execution, control logic, system scheduling, and network communication to the SmarNICs. This boosts performance by: (i) avoiding the control dependency with CPUs and (ii) supporting streaming kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network applications including graph neural networks; as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with the support of FCsN can achieve, on average, 144 speedups over the MPI-based standard CPU baselines.
To enable the rapid, widespread commercial availability of secondary distribution grid voltage and phase angle data, the DOE OE Technology Commercialization Fund (TCF) project: 1) created a new American National Standard for next-generation grid power quality sensing and communications, 2) created a new prototype broadband-based standard-compliant grid power sensing system for use by utilities and others, and 3) improved by several orders of magnitude the spatiotemporal scale of the in-progress DOE CESER project: Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics.
Routine childhood immunizations are proven to be one of the most effective public health interventions at controlling numerous deadly diseases. Therefore, the CDC recommends routine immunizations for children and adolescent populations against vaccine-preventable diseases e.g., tetanus, pertussis, diphtheria, etc. This current review sought to examine barriers to pediatric vaccine uptake behaviors during the COVID-19 pandemic. We also explored the implications for parental vaccine hesitancy/delay during an ongoing health crisis and proposed recommendations for increasing vaccine confidence and compliance. Our review determined that the receipt for vaccinations steadily improved in the last decade for both the United States and Tennessee. However, this incremental progress has been forestalled by the COVID-19 pandemic and other barriers i.e. parental vaccine hesitancy, social determinants of health (SDoH) inequalities, etc. which further exacerbate vaccination disparities. Moreover, non-compliance to routine vaccinations could cause an outbreak of diseases, thereby, worsening the ongoing health crisis and already strained health care system. Healthcare providers are uniquely positioned to offer effective recommendations with presumptive languaging to increase vaccination rates, as well as, address parental vaccine hesitancy. Best practices that incorporate healthcare providers’ quality improvement coaching, vaccination reminder recall systems, adherence to standardized safety protocols (physical distancing, hand hygiene practices, etc.), as well as, offer telehealth and outdoor/drive-through/curbside vaccination services, etc. are warranted. Additionally, a concerted effort should be made to utilize public health surveillance systems to collect, analyze, and interpret data, thereby, ensuring the dissemination of timely, accurate health information for effective health policy decision-making e.g., vaccine distribution, etc.
This data package contains point-scale Earth System Model simulations run with DOE's E3SM Model. The package includes four sets of simulations, one set of simulations run in the default configuration, a second set of simulations run with the dynamic root module enabled, the third set with dynamic leaf to fine root allocation, and the fourth with the combination of dynamic roots and dynamic allocation. These simulations were run in point mode a Ameriflux sites, US-MMS, US-Moz, US-Syv, and US-NR1. These datasets can be used to assess the impact that dynamic roots have on Land Surface fluxes such as carbon and water. The simulations were run with atmospheric forcing from the Global Soil Wetness Project. We provide specifically here Gross Primary Production, Transpiration, relative root fraction per soil layer as well as Air Temperature, Precipitation and all the parameterizations used for the simulations. Our purpose for generating and analyzing these simulations was to assess the role that root dynamics and foraging for water has in the recovery timescale of ecosystems following climate perturbation. All data provided are in the "netcdf" format using standard CF-1 (Climate and Forecast) Convention. The data are all machine readable using various software platforms including Matlab, NCO, Panoply and R.
We develop an online data-enabled predictive (ODeePC) control method for optimal control of unknown systems, building on the recently proposed DeePC (Coulson et al., 2019). Our proposed ODeePC method leverages a primal-dual algorithm with real-time measurement feedback to iteratively compute the corresponding real-time optimal control policy as system conditions change. The proposed ODeePC conceptual-wise resembles standard adaptive system identification and model predictive control (MPC), but it provides a new alternative for the standard methods. ODeePC is enabled by computationally efficient methods that exploit the special structure of the Hankel matrices in the context of DeePC with Fast Fourier Transform (FFT) and primal-dual algorithm We provide theoretical guarantees regarding the asymptotic behavior of ODeePC, and we demonstrate its performance through numerical examples.
This report documents the eighth edition of the annual Standard Scenarios. It summarizes 70 forward-looking scenarios of the U.S. electricity sector that have been designed to capture a wide range of possible futures. In August 2022, the United States Congress passed the Inflation Reduction Act (IRA), a law aimed at accelerating U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. This year’s scenarios include representations of the main electricity-sector provisions from IRA and the potential impact on electricity demand. The Standard Scenarios are simulated using the Regional Energy Deployment System (ReEDS) model, which projects utility-scale electricity sector evolution for the contiguous United States using a system-wide, least-cost approach subject to policy and operational constraints. A subset of the scenarios are simulated in the PLEXOS production cost model to obtain a broader suite of metrics at the hourly resolution, which are made available through the National Renewable Energy Laboratory’s (NREL’s) annual Cambium data sets. The scenarios can be viewed and downloaded from NREL’s Scenario Viewer. Annual results are available for the full suite of scenarios in the Standard Scenarios projects in the viewer, whereas the Cambium projects contain hourly data for a subset of scenarios. The Standard Scenarios includes a scenario called the Mid-case, which has central or median values for core inputs such as technology costs and fuel prices, moderately paced demand growth averaging 1.3% per year, and electricity sector policies as they existed in September 2022 (including IRA). The remaining 69 scenarios are created by varying inputs such as technology and fuel prices, resource availability, demand growth, whether nascent generation technologies are allowed, and by introducing national decarbonization constraints.
Krylov methods provide a fast and highly parallel numerical tool for the iterative solution of many large-scale sparse linear systems. To a large extent, the performance of practical realizations of these methods is constrained by the communication bandwidth in current computer architectures, motivating the investigation of sophisticated techniques to avoid, reduce, and/or hide the message-passing costs (in distributed platforms) and the memory accesses (in all architectures). This article leverages Ginkgo’s memory accessor in order to integrate a communication-reduction strategy into the (Krylov) GMRES solver that decouples the storage format (i.e., the data representation in memory) of the orthogonal basis from the arithmetic precision that is employed during the operations with that basis. Given that the execution time of the GMRES solver is largely determined by the memory accesses, the cost of the datatype transforms can be mostly hidden, resulting in the acceleration of the iterative step via a decrease in the volume of bits being retrieved from memory. Together with the special properties of the orthonormal basis (whose elements are all bounded by 1), this paves the road toward the aggressive customization of the storage format, which includes some floating-point as well as fixed-point formats with mild impact on the convergence of the iterative process. We develop a high-performance implementation of the “compressed basis GMRES” solver in the Ginkgo sparse linear algebra library using a large set of test problems from the SuiteSparse Matrix Collection. We demonstrate robustness and performance advantages on a modern NVIDIA V100 graphics processing unit (GPU) of up to 50% over the standard GMRES solver that stores all data in IEEE double-precision.