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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications

Advanced measurement and verification (M&V) of energy efficiency savings, often referred to as M&V 2.0 or advanced M&V, is currently an object of much industry attention. Thus far, however, there has been a lack of clarity about what techniques M&V 2.0 includes, how those techniques differ from traditional approaches, what the key considerations are for their use, and what value propositions M&V 2.0 presents to different stakeholders. The objective of this paper is to provide background information and frame key discussion points related to advanced M&V. The paper identifies the benefits, methods, and requirements of advanced M&V and outlines key technical issues for applying these methods. It presents an overview of the distinguishing elements of M&V 2.0 tools and of how the industry is addressing needs for tool testing, consistency, and standardization, and it identifies opportunities for collaboration. In this paper, we consider two key features of M&V 2.0: (1) automated analytics that can provide ongoing, near-real-time savings estimates, and (2) increased data granularity in terms of frequency, volume, or end-use detail. Greater data granularity for large numbers of customers, such as that derived from comprehensive implementation of advanced metering infrastructure (AMI) systems, leads to very large data volumes. This drives interest in automated processing systems. It is worth noting, however, that automated processing can provide value even when applied to less granular data, such as monthly consumption data series. Likewise, more granular data, such as interval or end-use data, delivers value with or without automated processing, provided the processing is manageable. But it is the combination of greater data detail with automated processing that offers the greatest opportunity for value. Using M&V methods that capture load shapes together with automated processing1 can determine savings in near-real time to provide stakeholders with more timely and detailed information. This information can be used to inform ongoing building operations, provide early input on energy efficiency program design, or assess the impact of efficiency by location and time of day. Stakeholders who can make use of such information include regulators, energy efficiency program administrators, program evaluators, contractors and aggregators, building owners, the investment community, and grid planners. Although each stakeholder has its own priorities and challenges related to savings measurement and verification, the potential exists for all to draw from a single set of efficiency valuation data. Such an integrated approach could provide a base consistency across stakeholder uses.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1988

Adapting the NSCAT data system to changing requirements

The data system of the spaceborne eight-beam NASA scatterometer for measuring ocean backscatter is a nonreal-time ground-based science data processing system which inputs backscatter telemetry, processes the data into wind vectors, and archives and distributes the wind vector data and other products. Special attention is given to changes to the baseline design intended to meet new requirements for the data granularity, the data needs of the science team receiving the data, and the telemetry data source.

Benada, J. R.↗
NASA NTRS1999

Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D showed that HPF has potential to be a high level language for parallelization of CFD applications. The use of HPF requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement and data granularity. Since HPF hides data movement from the user even with this knowledge it is easy to overlook pieces of the code causing low performance of the application. In order to simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and partially implemented ADAPT (Automatic Data Distribution and Placement Tool). The ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level or inter application level. ADAPT is designed to annotate existing CFD FORTRAN application performing computations on single or multiple grids. On each grid the application can considered as a sequence of operators each applied to a set of variables defined in a particular grid domain. The operators can be classified as implicit, having data dependences, and explicit, without data dependences. In order to parallelize an explicit operator it is sufficient to create a template for the domain of the operator, align arrays used in the operator with the template, distribute the template, and declare the loops over the distributed dimensions as INDEPENDENT. In order to parallelize an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator then the loop which implements the operator can be declared as INDEPENDENT.

Frumkin, Michael↗
DOE OSTI2016

Supporting Building Portfolio Investment and Policy Decision Making through an Integrated Building Utility Data Platform

The American Recovery and Reinvestment Act stimulus funding of 2009 for smart grid projects resulted in the tripling of smart meters deployment. In 2012, the Green Button initiative provided utility customers with access to their real-time1 energy usage. The availability of finely granular data provides an enormous potential for energy data analytics and energy benchmarking. The sheer volume of time-series utility data from a large number of buildings also poses challenges in data collection, quality control, and database management for rigorous and meaningful analyses. In this paper, we will describe a building portfolio-level data analytics tool for operational optimization, business investment and policy assessment using 15-minute to monthly intervals utility data. The analytics tool is developed on top of the U.S. Department of Energy’s Standard Energy Efficiency Data (SEED) platform, an open source software application that manages energy performance data of large groups of buildings. To support the significantly large volume of granular interval data, we integrated a parallel time-series database to the existing relational database. The time-series database improves on the current utility data input, focusing on real-time data collection, storage, analytics and data quality control. The fully integrated data platform supports APIs for utility apps development by third party software developers. These apps will provide actionable intelligence for building owners and facilities managers. Unlike a commercial system, this platform is an open source platform funded by the U.S. Government, accessible to the public, researchers and other developers, to support initiatives in reducing building energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Granular Temperature Modeling with Large-Scale CFD-DEM Data

The granular temperature is a measure of solids-phase fluctuating kinetic energy in particle-laden gas-solid flows. This report focuses on reduced-order models of granular temperature in triply-periodic cluster induced turbulent flow. The granular temperature data was generated by post-processing large-scale CFD-DEM simulations that were carried out as part of an ALCC awarded study. The algebraic model of Tang et al, originally fit to DNS data, was found to be amenable to the present data. The results of several regression analyses are presented building on the original expression. This work was performed in part through the National Nuclear Security Administration (NNSA) Minority Serving Institutions Internship Program (MSIIP).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

A Tool for Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D has shown that HPF provides an efficient, concise way to express parallelism and to organize data traffic. The use of HPF, as noted in the papers, requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement, and data granularity. To simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and implemented ADAPT (Automatic Data Alignment and Placement Tool). ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT, and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level, or on the application interface level. ADAPT annotates an existing CFD FORTRAN application, performing computations on single or multiple grids. On each grid the application is considered as a sequence of operators, each applied to a set of variables defined in a particular grid domain. ADAPT automatically detects implicit operators (i.e., having data dependences) and explicit operators (without data dependences). For parallelization of an explicit operator ADAPT creates a template for the operator domain, aligns arrays used in the operator with the template, distributes the template, and declares the loops over the distributed dimensions as INDEPENDENT. For parallelization of an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if the compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator, then the loop which implements the operator can be declared as INDEPENDENT. ADAPT starts with an analysis of array index expressions of the loop nests. For each pair of arrays referenced in an assignment statement, it generates an arc in the alignment graph and annotates it with an affinity relation. The template, alignment, and distribution directives for a particular loop nest are then derived from a transitive closure of the affinity relation. A compromise of data distributions in different nests and subroutines is achieved by merging annotated alignment graphs for adjacent nests/stibroutine calls in the nest/call graph of the application in the process called distribution lifting. ADAPT has been implemented as a C++ program running in conjunction with a parallelization tool called CAPTools. ADAPT uses the parse tree, interprocedural analysis and application database generated by CAPTools. It also uses the Directed Graph class, initially implemented in p2d2 (parallel debugger oi distributed programs), and some other classes supporting symbolic computations. ADAPT uses data distribution techniques described. ADAPT was tested with ARC3D and the FT benchmark and has demonstrated a code performance within a factor of 1.5 of handwritten versions.

Frumkin, Michael↗
DOE OSTI2019

Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces

Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-of-Interest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin's functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world.

Masrur, Arif↗
DOE OSTI2023

Adaptive cache management based on programming model information

A processing system selectively allocates space to store a group of one or more cache lines at a cache level of a cache hierarchy having a plurality of cache levels based on memory access patterns of a software application executing at the processing system. The processing system generates bit vectors indicating which cache levels are to allocate space to store groups of one or more cache lines based on the memory access patterns, which are derived from data granularity and movement information. Based on the bit vectors, the processing system provides hints to the cache hierarchy indicating the lowest cache level that can exploit the reuse potential for a particular data.

Na, Weon Taek↗
DOE OSTI2015

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

data mining↗
DOE OSTI2025

Complexity Reduction Methods for Large-Scale Spatially Explicit Biofuels Network Design

The size and complexity of energy system optimization models have increased significantly in recent years, driven by the availability of high-resolution spatial data. We present complexity reduction and solution methods that enable us to efficiently represent high-resolution spatial data in the network design of large-scale energy systems. We aim to reduce the size and enhance the computational efficiency of network design models without sacrificing solution accuracy. Specifically, we first present how to aggregate highly granular data into larger resolutions without averaging out their specific properties through a composite-curve-based approach and then develop a method to linearly represent these curves. Second, we utilize a general clustering method to determine groups of geographically proximate biomass fields and establish a single transportation arc for all of them, reducing the number of transportation-related variables while maintaining an accurate representation of the system. Finally, we introduce a two-step algorithm that decomposes large-scale network design problems into two smaller, more manageable subproblems. We demonstrate the application of our methods using a case study of switchgrass-to-biofuels network design in the eight states of the U.S. Midwest, using realistic and highly explicit spatial data.

09 BIOMASS FUELS↗
DOE OSTI2017

Accessing Wi-Fi Data for Occupancy Sensing

A key issue for saving energy in buildings is to assure that delivery of energy services matches building occupancy as closely as possible, to assure that energy is not wasted providing the services to empty rooms or buildings, and to assure that needed services are provided during all times when desired. Accomplishing this to date has been significantly hampered by a lack of inexpensive mechanisms to obtain and use building-wide and more granular data about occupancy. In recent years, the concept of inferential (or implicit) sensing has been proposed and explored to use data from IT systems that already exists in most buildings, to obtain data that are not perfect, but are nearly free to obtain. Past work by LBNL and others has primarily demonstrated the principle and potential for this, with a primary focus on data from Wi-Fi networks as the best near-term opportunity for inferential sensing from IT networks. This is due to its near-ubiquity, ease of explanation to many audiences, relative uniformness in deployment, low latency of detection, clear ways to mitigate privacy and security, and other benefits (Price et al., 2015). While the potential and value are clear, researchers or building managers who want to obtain the data lack a source of information to understand what data might exist, what devices may have it, and what mechanisms are available to obtain the data. The purpose of this report is to fill that gap. The report begins with a review of institutional issues in collecting such data, including very real concerns about privacy and IT security. It then describes the various system architectures used in Wi-Fi systems in commercial buildings today, and a variety of mechanisms that can be used to obtain information from them, including examples of how to use them, and sample output. Next is a summary of how to use such data for several different purposes, from retrospective analysis to dynamic building operation, and a conclusion. An appendix provides additional detail on specific mechanisms available from major equipment manufacturers. The mechanisms specified in this report can be used by building owners and operators to confirm proper operation and uncover any issues or unexpected conditions as a resource for building owners and researchers interesting in utilizing inferential sensing data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Comparison of temporal resolution selection approaches in energy systems models

Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

What We Learned From Analyzing 18 Million Rows of Commercial Buildings’ HVAC Fault Data

To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.

Crowe, Eliot↗
DOE OSTI2015

Prediction Models for Dynamic Demand Response

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2 R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2 R, which we address in this paper. Our first contribution is the formal definition of D 2 R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2 R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2 R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2 R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2 R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2 R.

Aman, Saima↗
DOE OSTI2023

ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

NWChem: Quantum Chemistry Simulations at Scale

Methods based on quantum mechanics equations have been developed since the 1930's with the purpose of accurately studying the electronic structure of molecules. However, it is only during the last two decades that intense development of new computational algorithms has opened the possibility of performing accurate simulations of challenging molecular processes with high-order many-body methods. A wealth of evidence indicates that the proper inclusion of instantaneous interactions between electrons (or the so-called electron correlation effects) is indispensable for the accurate characterization of chemical reactivity, molecular properties, and interactions of light with matter. The availability of reliable methods for benchmarking of medium-size molecular systems provides also a unique chance to propagate high-level accuracy across spatial scales through the multiscale methodologies. Some of these methods have potential to utilize computational resources in an effi*cient way since they are characterized by high numerical complexity and appropriate level of data granularity, which can be effi*ciently distributed over multi-processor architectures. The broad spectrum of coupled cluster (CC) methods falls into this class of methodologies. Several recent CC implementations clearly demonstrated the scalability of CC formalisms on architectures composed of hundreds thousand computational cores. In this context NWChem provides a collection of Tensor Contraction Engine (TCE) generated parallel implementations of various coupled cluster methods capable of taking advantage of many thousand of cores on leadership class parallel architectures.

chemistry↗
DOE OSTI2023

Prototype Design of Global Common Module for ATLAS Experiment’s Phase-II Upgrade

A new Global Trigger subsystem will be installed in the Level-0 Trigger as part of HL-LHC Upgrade of ATLAS during the upcoming Long-Shutdown 3. It will feature new and improved trigger hardware and algorithms, and an increased maximum output rate of 1 MHz. The Global Trigger will run offline-like trigger algorithms on full-granularity data, gathered from several sub-detectors and trigger-processing subsystems. A single Global Common Module (GCM) hardware is implemented across the Global Trigger system to be used as Multiplexer Processor, Global Event Processor and CTP Interface (gCTPi). This common hardware platform method will minimize the complexity of the firmware and simplify the system design and long-term maintenance. The GCM prototype is an ATCA front form factor board with two Xilinx Virtex UltraScale+ FPGA VU13P and one ZYNQ UltraScale+ FPGA ZU19EG and seventeen 25.78125 Gb/s FireFly duplex optical modules on it. The total power consumption of this board must be less than 350 W, and the temperature of the optical modules should be less than 70 °C in the worst case. The VU13Ps serve as algorithms processor nodes such as MUX, GEP and gCTPi, and the ZU19EG with Peta Linux OS running on it, is used as Command/Control/Readout Unit to configure and monitor the board and communicate with the ATLAS Detector Control System (DCS). The development of an ATCA blade with three large FPGAs and about 200 optical links running at 25Gb/s is a very challenging task, and the successful test results have demonstrated this GCM prototype as an advancement of state-of-the-art electronics module design in HEP experiments. This paper presents the hardware design considerations, functionalities, and performance test results of this GCM prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Energy Efficiency: What Has Research Delivered in the Last 40 Years?

This article presents a critical assessment of 40 years of research that may be brought under the umbrella of energy efficiency, spanning different aggregations and domains—from individual producing and consuming agents to economy-wide effects to the role of innovation to the influence of policy. After 40 years of research, energy efficiency initiatives are generally perceived as highly effective. Innovation has contributed to lowering energy technology costs and increasing energy productivity. Energy efficiency programs in many cases have reduced energy use per unit of economic output and have been associated with net improvements in welfare, emission reductions, or both. Rebound effects at the macro level still warrant careful policy attention, as they may be nontrivial. Complexity of energy efficiency dynamics calls for further methodological and empirical advances, multidisciplinary approaches, and granular data at the service level for research in this field to be of greatest societal benefit.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗